http://www.simulace.info/api.php?action=feedcontributions&user=Manj01&feedformat=atomSimulace.info - User contributions [en]2024-03-29T12:25:35ZUser contributionsMediaWiki 1.31.1http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17565Ticket Solving Process at a Small IT dev Company2019-02-06T07:37:18Z<p>Manj01: /* Code */</p>
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<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
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• Class: 4IT496 (WS 2018/2019)<br />
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• Author: Bc. Jan Mandík<br />
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• Model type: Discrete-event simulation<br />
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• Software used: SimProcess, trial version<br />
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<br />
==Problem definition==<br />
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There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
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==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
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==Model==<br />
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The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
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==Entities==<br />
The only entity is defect, that is not prioritized. <br />
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==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
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==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. <br />
Answers to these questions could all be get from the results file, which is unfortunately impossible to upload. <br />
{| class="wikitable"<br />
<br />
|-<br />
|'''Number or Developers'''<br />
|2<br />
|5<br />
|10<br />
|100<br />
|-<br />
|'''Needed number of testers'''<br />
|1<br />
|1<br />
|1<br />
|1<br />
|-<br />
|'''Total cost of workers'''<br />
|13 152 748 CZK<br />
|32 605 759 CZK<br />
|63 842 645 CZK<br />
|76 281 384 CZK<br />
|-<br />
|'''Percentage of issues solved in given year'''<br />
|17%<br />
|43%<br />
|82%<br />
|98%<br />
|}<br />
Note that both testers and developers are paid per hour. This is reflected in the simulation and it works such in reality. If hiring too many developers, they might not be satisfied with the fact that they do not have much work.<br />
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==Conclusion==<br />
A working and useful simulation has been created. Such simulation could provide good insight on what would happen in case more developers were hired. No specific requirement has been set for finding a corresponding value, but the management would be able to find it in the simulation on its own. <br />
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==Code==<br />
Numerous reports and input xlsx file can be found in this file: [[Media:Simulation results.zip]]. The code can be found in this file: [[Media:Reports_and_code.zip]]<br />
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==References== <br />
http://simprocess.com/about-simprocess/simprocess-documentation/</div>Manj01http://www.simulace.info/index.php?title=File:Reports_and_code.zip&diff=17564File:Reports and code.zip2019-02-06T07:35:43Z<p>Manj01: </p>
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<div></div>Manj01http://www.simulace.info/index.php?title=WS_2018/2019&diff=17393WS 2018/20192019-01-24T17:35:23Z<p>Manj01: /* Papers */</p>
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<div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br />
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==Simulations==<br />
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--[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br />
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-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 16:50, 18 January 2019 (CET) [[Optimal size of HDD for virtual Digitization server]]<br />
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-- Jiří Korčák [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 11:09, 19 January 2019 (CET) [[Vacuum cleaner]]<br />
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-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 14:46, 19 January 2019 (CET) [[Ticket Solving Process at a Small IT dev Company]] <br />
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-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:43, 19 January 2019 (CET) [[evacuation from burning building]]<br />
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-- [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 12:11, 20 January 2019 (CET) [[Medieval Battle Simulation]]<br />
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-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 16:19, 20 January 2019 (CET) [[Argentinska Intersection]]<br />
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-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 16:41, 20 January 2019 (CET) [[You are what you eat]]<br />
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-- [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:19, 20 January 2019 (CET) [[Slime mold]]<br />
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-- [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 01:38, 21 January 2019 (CET) [[Simulation of north korea migration]]<br />
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-- Tomáš Smysl [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 01:19, 24 January 2019 (CET) [[Cafe simulation]]<br />
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==Papers==<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br />
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-- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]<br />
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-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 4:48, 20 January 2019 (CET) [https://en.wikipedia.org/wiki/Draft:MMABP MMABP in English]<br />
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-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 17:19, 20 January 2019 (CET) [[Limits to Growth_ver2]] <br />
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-- Tomáš Smysl (xsmyt00) [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 22:36, 20 January 2019 (CET) [[https://en.wikipedia.org/wiki/ArchiMate ArchiMate wiki]] Note: I had some issues with the Wikipedia image upload - they did not approve my images. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 13:53, 23 January 2019 (CET) EDIT: Solved.<br />
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-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 11:09, 21 January 2019 (CET) [[http://www.simulace.info/index.php/Variance_reduction Variance reduction]]<br />
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-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 21:52, 21 January 2019 (CET) [[Vickrey%27s_auction]] <br />
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-- [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 22:21, 23 January 2019 (CET) [[Serious Gaming - textbook text]]<br />
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-- [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 23:14, 23 January 2019 (CET) [https://en.wikipedia.org/wiki/Database_normalization Database normalization (Wikipedia)] - introduced step by step normalization in examples - my username on Wikipedia is "Honzikec"<br />
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-- [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 18:30, 24 January 2019 (CET) [[Leverage point]]</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17392Vickrey's auction2019-01-24T17:34:30Z<p>Manj01: </p>
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<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
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While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf [cit. 2019-01-21]</ref><br />
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==Vickrey auction==<br />
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Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
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Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory. <ref name="last">M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002. [cit. 2019-01-21]</ref><br />
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Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
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The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf [cit. 2019-01-21]</ref> <br />
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The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
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''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
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(the only difference is the Goethe is not willing to pay less than he demands) <ref name="goethe"> GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955. [cit. 2019-01-21]</ref><br />
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==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
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As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
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When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
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In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<ref name="last">M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002. [cit. 2019-01-21]</ref><br />
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==Uses of Vickrey auction==<br />
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Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
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'''Google AdWords'''<br />
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As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <ref name="google>Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en</ref> <br />
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'''Word of Warcraft Auctions'''<br />
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Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
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In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <ref name="wow">Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.[cit. 2019-01-21]</ref><br />
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'''Network routing'''<br />
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A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <ref name="network>ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf [cit. 2019-01-21]</ref><br />
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'''Closed groups auctions'''<br />
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Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
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==Derived types of auctions==<br />
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From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
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'''Uniform Price auction'''<br />
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Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf [cit. 2019-01-21]</ref><br />
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The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf [cit. 2019-01-21]</ref><br />
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'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <ref name="vcg>Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9. [cit. 2019-01-21]</ref><br />
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<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
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A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered.<br />
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'''Uniform Price auction'''<br />
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There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered, but only 5 dollars per apple – because it is the second best price offered. In another version of Uniform Price auction, they might also pay 7 dollars per apple because it the best price that did not win.<br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Let’s reuse the scenario of Uniform Price auction so the difference is clearly visible: There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered – and the price they are going to pay is the difference between Vickrey-Clark-Groves auction and Uniform Price auction. The price each of them is going to pay is calculated as a difference between the offers of the other two. Jack offered 150 dollars totally, Isaac offered 160 dollars and Sean offered 105 dollars. Thus, Jack is going to pay 160 – 105 = 55 dollars, while Isaac is going to pay 150 – 105 = 45 dollars.<br />
<br />
== References ==</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17391Vickrey's auction2019-01-24T17:33:56Z<p>Manj01: </p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf [cit. 2019-01-21]</ref><br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory. <ref name="last">M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002. [cit. 2019-01-21]</ref><br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf [cit. 2019-01-21]</ref> <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing to pay less than he demands) <ref name="goethe"> GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955. [cit. 2019-01-21]</ref><br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<ref name="last">M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.[cit. 2019-01-21]</ref><br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <ref name="google>Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en</ref> <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <ref name="wow">Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.[cit. 2019-01-21]</ref><br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <ref name="network>ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf [cit. 2019-01-21]</ref><br />
<br />
'''Closed groups auctions'''<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf [cit. 2019-01-21]</ref><br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf [cit. 2019-01-21]</ref><br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <ref name="vcg>Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9. [cit. 2019-01-21]</ref><br />
<br />
<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
<br />
A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered.<br />
<br />
'''Uniform Price auction'''<br />
<br />
There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered, but only 5 dollars per apple – because it is the second best price offered. In another version of Uniform Price auction, they might also pay 7 dollars per apple because it the best price that did not win.<br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Let’s reuse the scenario of Uniform Price auction so the difference is clearly visible: There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered – and the price they are going to pay is the difference between Vickrey-Clark-Groves auction and Uniform Price auction. The price each of them is going to pay is calculated as a difference between the offers of the other two. Jack offered 150 dollars totally, Isaac offered 160 dollars and Sean offered 105 dollars. Thus, Jack is going to pay 160 – 105 = 55 dollars, while Isaac is going to pay 150 – 105 = 45 dollars.<br />
<br />
== References ==</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17390Vickrey's auction2019-01-24T17:32:31Z<p>Manj01: </p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf </ref><br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory. <ref name="last">M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</ref><br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf </ref> <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing to pay less than he demands) <ref name="goethe"> GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955. </ref><br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<ref name="last">M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</ref><br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <ref name="google>Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en</ref> <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <ref name="wow">Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.</ref><br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <ref name="network>ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf</ref><br />
<br />
'''Closed groups auctions'''<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf</ref><br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf</ref><br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <ref name="vcg>Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.</ref><br />
<br />
<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
<br />
A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered.<br />
<br />
'''Uniform Price auction'''<br />
<br />
There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered, but only 5 dollars per apple – because it is the second best price offered. In another version of Uniform Price auction, they might also pay 7 dollars per apple because it the best price that did not win.<br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Let’s reuse the scenario of Uniform Price auction so the difference is clearly visible: There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered – and the price they are going to pay is the difference between Vickrey-Clark-Groves auction and Uniform Price auction. The price each of them is going to pay is calculated as a difference between the offers of the other two. Jack offered 150 dollars totally, Isaac offered 160 dollars and Sean offered 105 dollars. Thus, Jack is going to pay 160 – 105 = 55 dollars, while Isaac is going to pay 150 – 105 = 45 dollars.<br />
<br />
== References ==</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17376Vickrey's auction2019-01-24T16:44:47Z<p>Manj01: not done</p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf </ref><br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory. <ref name="last">M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</ref><br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <ref name="complexity"> LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf </ref> <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing to pay less than he demands) <ref name="goethe"> GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955. </ref><br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<ref name="last"> <br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <ref name="google>Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en</ref> <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <ref name="wow">Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.</ref><br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <ref name="network>ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf</ref><br />
<br />
'''Closed groups auctions'''<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf</ref><br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <ref name="uniform">MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf</ref><br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <ref name="vcg>Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.</ref><br />
<br />
<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
<br />
A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered.<br />
<br />
'''Uniform Price auction'''<br />
<br />
There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered, but only 5 dollars per apple – because it is the second best price offered. In another version of Uniform Price auction, they might also pay 7 dollars per apple because it the best price that did not win.<br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Let’s reuse the scenario of Uniform Price auction so the difference is clearly visible: There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered – and the price they are going to pay is the difference between Vickrey-Clark-Groves auction and Uniform Price auction. The price each of them is going to pay is calculated as a difference between the offers of the other two. Jack offered 150 dollars totally, Isaac offered 160 dollars and Sean offered 105 dollars. Thus, Jack is going to pay 160 – 105 = 55 dollars, while Isaac is going to pay 150 – 105 = 45 dollars.<br />
<br />
== References ==</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17375Vickrey's auction2019-01-24T16:23:53Z<p>Manj01: /* Vickrey auction */</p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory.<br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing to pay less than he demands)<br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <br />
Closed groups auctions<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <br />
<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
<br />
A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered.<br />
<br />
'''Uniform Price auction'''<br />
<br />
There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered, but only 5 dollars per apple – because it is the second best price offered. In another version of Uniform Price auction, they might also pay 7 dollars per apple because it the best price that did not win.<br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Let’s reuse the scenario of Uniform Price auction so the difference is clearly visible: There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered – and the price they are going to pay is the difference between Vickrey-Clark-Groves auction and Uniform Price auction. The price each of them is going to pay is calculated as a difference between the offers of the other two. Jack offered 150 dollars totally, Isaac offered 160 dollars and Sean offered 105 dollars. Thus, Jack is going to pay 160 – 105 = 55 dollars, while Isaac is going to pay 150 – 105 = 45 dollars.<br />
<br />
== References ==<br />
<br />
MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf<br />
<br />
LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf <br />
<br />
ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf<br />
<br />
Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.<br />
<br />
Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.<br />
<br />
GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955.<br />
<br />
Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en<br />
<br />
M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17359Vickrey's auction2019-01-24T09:28:39Z<p>Manj01: /* Examples */</p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory.<br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing less than he demands)<br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <br />
Closed groups auctions<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <br />
<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
<br />
A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered.<br />
<br />
'''Uniform Price auction'''<br />
<br />
There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered, but only 5 dollars per apple – because it is the second best price offered. In another version of Uniform Price auction, they might also pay 7 dollars per apple because it the best price that did not win.<br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Let’s reuse the scenario of Uniform Price auction so the difference is clearly visible: There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered – and the price they are going to pay is the difference between Vickrey-Clark-Groves auction and Uniform Price auction. The price each of them is going to pay is calculated as a difference between the offers of the other two. Jack offered 150 dollars totally, Isaac offered 160 dollars and Sean offered 105 dollars. Thus, Jack is going to pay 160 – 105 = 55 dollars, while Isaac is going to pay 150 – 105 = 45 dollars.<br />
<br />
== References ==<br />
<br />
MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf<br />
<br />
LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf <br />
<br />
ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf<br />
<br />
Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.<br />
<br />
Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.<br />
<br />
GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955.<br />
<br />
Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en<br />
<br />
M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17358Vickrey's auction2019-01-24T09:20:35Z<p>Manj01: /* Examples */</p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory.<br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing less than he demands)<br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <br />
Closed groups auctions<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <br />
<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
<br />
A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered.<br />
<br />
'''Uniform Price auction'''<br />
<br />
There are 50 apples and three bidders for these apples, Jack, Isaac and Sean. Jack offers 5 dollars per apple and wants 30 apples; Isaac wants 20 apples and offers 8 dollars per apple. Sean wants 15 apples for 7 dollars per apple. There offers are sealed so it is not possible to see other bidders offer. To determine who will get the apples, we need to calculate to maximum possible profit in case full price was paid – which is 30 apples for Jack and 20 apples for Isaac. They are however not going to pay the price they offered, but only 5 dollars per apple – because it is the second best price offered. In another version of Uniform Price auction, they might also pay 7 dollars per apple because it the best price that did not win.<br />
<br />
== References ==<br />
<br />
MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf<br />
<br />
LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf <br />
<br />
ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf<br />
<br />
Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.<br />
<br />
Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.<br />
<br />
GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955.<br />
<br />
Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en<br />
<br />
M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17357Vickrey's auction2019-01-24T08:57:18Z<p>Manj01: </p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory.<br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing less than he demands)<br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <br />
Closed groups auctions<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <br />
<br />
==Examples==<br />
<br />
Even though some partial examples have been provided above, here you can find another examples to sum up the topic.<br />
<br />
'''Vickrey Auction'''<br />
<br />
A historical painting is being auctioned. There are four bidders, John, Abraham, Simon and Nathaniel. Each of them offer a price they are willing to pay. They submit the offers in closed envelopes so the other cannot see how much they are willing to pay. The auction master opens the envelopes and sees that John offered 400 dollars, Abraham offered 450 dollars, Simon offered 350 dollars and Nathaniel offered 500 dollars. The painting goes to Nathaniel, because he offered the best price. However, Nathaniel only needs to pay 450 dollars, because 450 is the second best price offered. <br />
<br />
<br />
<br />
== References ==<br />
<br />
MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf<br />
<br />
LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf <br />
<br />
ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf<br />
<br />
Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.<br />
<br />
Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.<br />
<br />
GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955.<br />
<br />
Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en<br />
<br />
M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</div>Manj01http://www.simulace.info/index.php?title=WS_2018/2019&diff=17356WS 2018/20192019-01-24T08:49:41Z<p>Manj01: </p>
<hr />
<div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br />
<br />
==Simulations==<br />
<br />
--[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br />
<br />
-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 16:50, 18 January 2019 (CET) [[Optimal size of HDD for virtual Digitization server]]<br />
<br />
-- Jiří Korčák [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 11:09, 19 January 2019 (CET) [[Vacuum cleaner]]<br />
<br />
-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 14:46, 19 January 2019 (CET) [[Ticket Solving Process at a Small IT dev Company]] <br />
<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:43, 19 January 2019 (CET) [[evacuation from burning building]]<br />
<br />
-- [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 12:11, 20 January 2019 (CET) [[Medieval Battle Simulation]]<br />
<br />
-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 16:19, 20 January 2019 (CET) [[Argentinska Intersection]]<br />
<br />
-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 16:41, 20 January 2019 (CET) [[You are what you eat]]<br />
<br />
-- [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:19, 20 January 2019 (CET) [[Slime mold]]<br />
<br />
-- [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 01:38, 21 January 2019 (CET) [[Simulation of north korea migration]]<br />
<br />
-- Tomáš Smysl [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 01:19, 24 January 2019 (CET) [[Cafe simulation]]<br />
<br />
==Papers==<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br />
<br />
-- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]<br />
<br />
-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 4:48, 20 January 2019 (CET) [https://en.wikipedia.org/wiki/Draft:MMABP MMABP in English]<br />
<br />
-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 17:19, 20 January 2019 (CET) [[Limits to Growth_ver2]] <br />
<br />
-- Tomáš Smysl (xsmyt00) [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 22:36, 20 January 2019 (CET) [[https://en.wikipedia.org/wiki/ArchiMate ArchiMate wiki]] Note: I had some issues with the Wikipedia image upload - they did not approve my images. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 13:53, 23 January 2019 (CET) EDIT: Solved.<br />
<br />
-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 11:09, 21 January 2019 (CET) [[http://www.simulace.info/index.php/Variance_reduction Variance reduction]]<br />
<br />
-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 21:52, 21 January 2019 (CET) [[Vickrey%27s_auction]] (work in progress, please read after deadline)<br />
<br />
-- [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 22:21, 23 January 2019 (CET) [[Serious Gaming - textbook text]]<br />
<br />
-- [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 23:14, 23 January 2019 (CET) [https://en.wikipedia.org/wiki/Database_normalization Database normalization (Wikipedia)] - introduced step by step normalization in examples - my username on Wikipedia is "Honzikec"</div>Manj01http://www.simulace.info/index.php?title=WS_2018/2019&diff=17148WS 2018/20192019-01-21T20:53:30Z<p>Manj01: /* Papers */ added my paper (final version)</p>
<hr />
<div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br />
<br />
==Simulations==<br />
<br />
--[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br />
<br />
-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 16:50, 18 January 2019 (CET) [[Optimal size of HDD for virtual Digitization server]]<br />
<br />
-- Jiří Korčák [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 11:09, 19 January 2019 (CET) [[Vacuum cleaner]]<br />
<br />
-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 14:46, 19 January 2019 (CET) [[Ticket Solving Process at a Small IT dev Company]] <br />
<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:43, 19 January 2019 (CET) [[evacuation from burning building]]<br />
<br />
-- [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 12:11, 20 January 2019 (CET) [[Medieval Battle Simulation]]<br />
<br />
-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 16:19, 20 January 2019 (CET) [[Argentinska Intersection]]<br />
<br />
-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 16:41, 20 January 2019 (CET) [[You are what you eat]]<br />
<br />
-- [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:19, 20 January 2019 (CET) [[Slime mold]]<br />
<br />
-- [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 01:38, 21 January 2019 (CET) [[Simulation of north korea migration]]<br />
<br />
==Papers==<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br />
<br />
-- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]<br />
<br />
-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 4:48, 20 January 2019 (CET) [https://en.wikipedia.org/wiki/Draft:MMABP MMABP in English]<br />
<br />
-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 17:19, 20 January 2019 (CET) [[Limits to Growth_ver2]] (WIP)<br />
<br />
-- Tomáš Smysl (xsmyt00) [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 22:36, 20 January 2019 (CET) [[https://en.wikipedia.org/wiki/ArchiMate ArchiMate wiki]] Note: I had some issues with the Wikipedia image upload - they did not approve my images. So the visual part of the Paper is missing examples of models that would ilustrate the text, which is quite unfortunate considering the topic is a visual modeling tool. I hope it can be solved later on. I even redrew some of the models so there would not be an issue with rights.<br />
<br />
-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 11:09, 21 January 2019 (CET) [[http://www.simulace.info/index.php/Variance_reduction Variance reduction]]<br />
<br />
-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 21:52, 21 January 2019 (CET) [[Vickrey%27s_auction]]</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17147Vickrey's auction2019-01-21T20:50:32Z<p>Manj01: </p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory.<br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing less than he demands)<br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, especially in environments where auction is processed automatically, extremely quickly and where socially-optimal outcome is preferable to maximization of the auction owner profit. <br />
<br />
'''Google AdWords'''<br />
<br />
As already mentioned, Vickrey auction in its modified version (Vickrey-Clark-Groves auction, to be more thoroughly described later) the main mechanism that Google uses to sell advertising in macro-scope. <br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <br />
Closed groups auctions<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <br />
<br />
<br />
== References ==<br />
<br />
MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf<br />
<br />
LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf <br />
<br />
ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf<br />
<br />
Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.<br />
<br />
Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.<br />
<br />
GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955.<br />
<br />
Auction [online]. Google Ads Help, 2019 [cit. 2019-01-21]. Available from: https://support.google.com/google-ads/answer/142918?hl=en<br />
<br />
M. Ausubel, Lawrence & Milgrom, Paul. (2006). The Lovely but Lonely Vickrey Auction. Comb. Auct.. 17. 10.7551/mitpress/9780262033428.003.0002.</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17146Vickrey's auction2019-01-21T20:38:40Z<p>Manj01: almost done</p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, the Vickrey auction seems to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but, weirdly enough, the winner does not have to pay the price he offered, instead he/she pays only the price that has been offered in the second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction (and derived types) in the year 2010 and this sum is probably rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of auction (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience, as this auction might not be the most profitable for the seller, but it is socially-optimal. <br />
<br />
==Vickrey auction==<br />
<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory.<br />
<br />
Vickrey auction some interesting perks that make this type of auction very popular in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. This is because the equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy (weakly). The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point leads to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the bidders taking part in the auction. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. This is however theory, while in the macro scope these traits of Vickrey auction are being observed, it can not be said the real value is going to unfold during just one instance of Vickrey auction. <br />
<br />
The interesting truth-revealing trait of Vickrey auction was discovered by famous German poet Johann Wolfgang Goethe. Following situation also provides an example of Vickrey auction instance:<br />
<br />
''""I am inclined to offer Mr. Vieweg from Berlin an epic poem, Hermann and Dorothea, which will have approximately 2000 hexameters…. Concerning the royalty we will proceed as follows: I will hand over to Mr. Counsel Bottiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my work. If his offer is lower than my demand, then I take my note back, unopened, and the negotiation is broken. If, however, his offer is higher, then I will not ask for more than what is written in the note to be opened by Mr. Bottiger.""''<br />
<br />
(the only difference is the Goethe is not willing less than he demands)<br />
<br />
==Proof that Vickrey action dominant strategy is to offer real value of the auction’s object==<br />
<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting and sometimes useful. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other bidders highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
<br />
==Uses of Vickrey auction==<br />
<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, aside from the already mentioned use to sell advertising on internet.<br />
<br />
'''Word of Warcraft Auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
<br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <br />
<br />
'''Network routing'''<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <br />
Closed groups auctions<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used. <br />
<br />
==Derived types of auctions==<br />
<br />
From Vickrey auction, another several types of auction can be derived to simplified it or use it more real-life scenarios. <br />
<br />
'''Uniform Price auction'''<br />
<br />
Vickrey auction is not suitable for trading divisible goods, such as water, oil or so. A modified version of Vickrey auction exists to be used when auctioning such goods. This type of auction is called Uniform Price auction. In such auction, every contestant declares how many pieces of good he wants and how much he is willing to pay for these pieces. After that, the number of pieces that is the subject of the auction is divided between the highest bidders according to their declared demands. The winners however do not pay their bid, but (mostly) only the lowest winning bid. Other versions of this auction exist, such a version in which the winners pay the highest non-winning bid or the highest winning bid. The latter has been successfully used to sell some of marketable treasury securities by the USA National Treasury. <br />
<br />
The uniform price auction is not as interesting as usual Vickrey auction, as in neither of the mentioned cases, the dominant strategy is to offer the real valuation of the auction’s object – this happens only in case a single, non-divisible object is being sold. <br />
<br />
'''Vickrey-Clark-Groves auction'''<br />
<br />
Already mentioned, Vickrey-Clark-Groves auction (abbreviated as VCG auction) is a derivative of Vickrey auction, or technically speaking, Vickrey auction is a derivative of Vickrey-Clark-Groves auction, even though Vickrey auction has been described and used first. Vickrey–Clarke–Groves auction allows the seller to sell multiple items at once, unlike Vickrey auction. <br />
<br />
In Vickrey–Clarke–Groves auction instance, a finite number of identical items is being sold. All bidders make offer, in such manner so that others do not see how much they are offering. The bids could also be described as (N, P) pair of numbers, where N is the desired number of items and P is the price for all these products. <br />
<br />
After all offers are set, all possible combinations of bids are calculated by the auction owner (in practice this is often a computer system that calculates everything in an instant). Out of these calculations, the one which would mean the highest profit for the auction owner is selected and items are distributed to the bidders who offered the best price per item until there are any items left. For example, three people (John, Stacy, Clarinda) want three oranges. John offers one dollar for one orange, Stacy offers four dollars for one orange and Clarinda offers four dollars for three oranges (but refuses to have only one or two oranges). Even though Clarinda offered better price per orange than John, Clarinda is not going to get any oranges, because the sum of Johns and Stacy’s offers is better than Clarinda’s offer.<br />
<br />
The bidders however do not need to pay the price they offered in their bid. They pay a different number instead, a number that is called Harm. This Harm is calculated a difference between the sum of bids of the auction from the second-best combination of bids and what other bidders have bid in the current combination of bids. <br />
<br />
Using this principle, Vickrey-Clark-Groves auction allows to use the most important propriety of Vickrey auction – the truth revealing – in more macroscopic sense. This allows to auction to be socially-optimal. This is desirable in automatically functioning net of macroscopic size, such as mentioned network routing or internet. <br />
<br />
<br />
== References ==<br />
<br />
MALWEY, Paul F., Christine M. ARCHIBALD and Sean T. FLINN. Uniform-Price Auctions: Evaluation of the Treasury Experience. Office Of Market Finance. Washington, D.C., 2002, 20(220), 85. Available from https://www.treasury.gov/resource-center/fin-mkts/Documents/final.pdf<br />
<br />
LEVIN, Jonathan. Auction Theory. In: . 2004, s. 18. Available from https://web.stanford.edu/~jdlevin/Econ%20286/Auctions.pdf <br />
<br />
ZHOU, Haojie, Ka-Cheong LEUNG and Victor O. K. LI. Auction-Based Schemes for Multipath Routing In Selfish Networks. 2013 IEEE Wireless Communications and Networking Conference (WCNC) [online]. The University of Hong Kong, 2013 [cit. 2019-01-21]. Available from https://www.eee.hku.hk/~kcleung/papers/conferences/auction-based_multipath_routing:WCNC_2013/06554864.pdf<br />
<br />
Chování účastníků modelové Vickrey 's 2nd price auction. Praha, 2008. Bachelor's thesis. Vysoká Škola Ekonomická Praha. Thesis partner Pert Bartoň.<br />
<br />
Peter Cramton, Yoav Shoham, Richard Steinberg (Eds), Combinatorial Auctions, MIT Press, 2006, Chapter 1. ISBN 0-262-03342-9.<br />
<br />
GOETHE, Johann Wolfgang von. Hermann und Dorothea. Leipzig: Koehler & Amelang, 1955.</div>Manj01http://www.simulace.info/index.php?title=Vickrey%27s_auction&diff=17143Vickrey's auction2019-01-21T16:49:24Z<p>Manj01: not done yet</p>
<hr />
<div>==Introduction==<br />
Out of all commonly described types of auction, Vickrey auction to be the one that sounds the weirdest, most complex and least useful. It is defined as type of auction using offers sealed in envelopes. As one would expect, the highest bid wins the auction, but the winner does not have to pay the price he offered, but only the price that has been offered in second highest bid. <br />
<br />
While this is obviously not the most effective type of auction from the view of the seller, around 30 billion dollars were reassigned using this type of auction in the year 2010 and this sum is rising every year. The reason for this is the fact the Google, Yahoo and other mayor players at the field of internet advertising are using this type of action (or more specifically generalized Vickrey Auction). Thus, every time you browse internet, dozens of Vickrey actions happen to determine which specific advert you will see. Using modified Vickreys auction, Google claims to aim for win-win-win situation by reaching ideal ratio of the advertiser’s benefit, Google’s profit and thanks to some modifications to the auction also user’s experience. <br />
==Vickrey auction==<br />
Vickrey auction is often also called Second Price auction because of the above-mentioned fact that the not the highest, but second highest price is to be paid by the winner of the auction. The concept of this auction was first thoroughly described by the Canadian Economy Nobel Prize holder William Vickrey in 1961. It was however used much earlier, for example, it is used by stamp collectors since 1893. <br />
<br />
Aside from its use in specific real-life scenarios, it is very interesting for theoretical research and demonstrating several matters commonly mentioned in Game Theory.<br />
<br />
There are three main reason for Vickerey auction popularity in academic and other theoretic circles. First of all, this type of auction is known to be truth revealing, or more specifically, the equilibrium strategy for this auction is truth revealing. The equilibrium strategy for Vickerey auction is to offer the true value of the auction’s object, thus reveal the ‘truth’. This is also the dominant strategy. The true value of the object also includes secondary considerations of the value, such as the possible loss of profit in case a bidder competitor wins the object. <br />
<br />
The first point lead to second – if all bidders (players) follow the equilibrium (dominant) strategy, it will lead to the maximal possible economic efficiency for all of the players. There is no possible loss of value for those who did not win the auction, because there is no chance that a bidder will wrongly estimate the completion and overpay, unlike in case of First Price (English) sealed offer auction. <br />
==Proof that Vickrey auction dominant strategy is to offer real value of the auction’s ==<br />
As mentioned, the truth revealing property of Vickrey action is what makes it really interesting. Let’s prove this statement:<br />
<br />
When dealing in Vickrey auction, it is a dominant strategy to offer the real value of the object.<br />
<br />
Proof. John wants to buy a car in an auction. The value of the car to John 100$. John is considering to offer more than the car’s value, let’s say 105$. The other’s highest bid is to John practically a random number. The auction can end in three ways for John – a) Someone else offers more, b) John offers the most, someone else offered more than is the perceived car value (eg. 102$), c) John offers the highest bid, the second highest bid is less that the car’s value to John (eg. 98$).<br />
<br />
In first case a), John is not getting the car. In case b), John gets the car, but he is overpaying the perceived value of the car – even though he is only paying the second highest bid. That would not happen if he only offered the car’s perceived value (Case c)). Thus, it is more reasonable to offer only the perceived value, as offering more can only lead to overpaying. Offering less than the perceived value is a similar case. <br />
This also show the main benefit of Vickrey auction – the bidder is never overpaying if playing the dominant strategy. This makes it useful in cases where the auctioneer profit is not the most important aspect of the auction, as other aspect has to be acknowledged, such as stability of a network of happiness of players. <br />
==Uses of Vickrey auction==<br />
Even though Vickrey auction is not as know or popular as other, more straightforward types of auctions, it still sees numerous uses in real-life, aside from the already mentioned use to sell advertising on internet.<br />
<br />
'''Word of Warcraft auctions'''<br />
<br />
Word of Warcraft is a well-known online Massively multiplayer online role-playing game (MMORPG) published in 2004 but still somehow popular. Players are often part of guilds and need specific items to achieve their in-game goals. As items are distributed somewhat randomly among players, players need to exchange them for different items, or, more commonly, for in game currency. Such exchange can be done though auction set in a player’s guild. Different types of auctions can be used, but Vickrey Auction is especially popular. <br />
In such in-guild auction, an item is offered to the other guild members. After that, a standard Vickrey auction takes place: All interested players offer a price, after which the second-best price is paid to the highest bidder. Compared to the other commonly used system – fixed prices – this allows better effectivity of the guild and higher joy from the game. <br />
<br />
'''Network routing''' <br />
<br />
<br />
A modified version of Vickrey auction, Vickrey-Clark-Groove auction, is used in one of the methods to fulfil networking request in the field of Network Routing. A scheme to assign a route through nodes in the network is necessary, as the nodes are not always able to fulfil all request given their technical limitations. Such nodes are known as “Selfish” nodes and are programmed to fulfil something that could be described as their utilities. The are pairs in the network which need a path with a given bandwidth to be assigned to them, and nodes are able to offer this bandwidth. The pair need to offer a payment to the nodes for the needed bandwidth. Given the fact that such transactions are done with automatically set prices, the Vickrey auction is a suitable option, because it prevents the pairs from overpaying and allows the most efficient paths in the network to be found. <br />
<br />
'''Closed groups auctions'''<br />
<br />
Vickrey auctions is sometimes used in groups where the seller prefers the group welfare and happiness to his own profit. As this requires an amount of solidarity, this is not so common. Of of such groups are philatelist groups (stamp collectors), where Vickrey auction is the preferred type of auction since the year 1893 when it was first used.</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17142Ticket Solving Process at a Small IT dev Company2019-01-21T16:14:06Z<p>Manj01: </p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. <br />
Answers to these questions could all be get from the results file, which is unfortunately impossible to upload. <br />
{| class="wikitable"<br />
<br />
|-<br />
|'''Number or Developers'''<br />
|2<br />
|5<br />
|10<br />
|100<br />
|-<br />
|'''Needed number of testers'''<br />
|1<br />
|1<br />
|1<br />
|1<br />
|-<br />
|'''Total cost of workers'''<br />
|13 152 748 CZK<br />
|32 605 759 CZK<br />
|63 842 645 CZK<br />
|76 281 384 CZK<br />
|-<br />
|'''Percentage of issues solved in given year'''<br />
|17%<br />
|43%<br />
|82%<br />
|98%<br />
|}<br />
Note that both testers and developers are paid per hour. This is reflected in the simulation and it works such in reality. If hiring too many developers, they might not be satisfied with the fact that they do not have much work.<br />
<br />
==Conclusion==<br />
A working and useful simulation has been created. Such simulation could provide good insight on what would happen in case more developers were hired. No specific requirement has been set for finding a corresponding value, but the management would be able to find it in the simulation on its own. <br />
<br />
==Code==<br />
The code itself, as well as numerous reports and input xlsx file can be found in this file: [[Media:Simulation results.zip]]<br />
==References== <br />
http://simprocess.com/about-simprocess/simprocess-documentation/</div>Manj01http://www.simulace.info/index.php?title=File:Simulation_results.zip&diff=17141File:Simulation results.zip2019-01-21T16:13:04Z<p>Manj01: </p>
<hr />
<div></div>Manj01http://www.simulace.info/index.php?title=File:ResultsCurrentState.xlsx&diff=17140File:ResultsCurrentState.xlsx2019-01-21T16:12:52Z<p>Manj01: </p>
<hr />
<div></div>Manj01http://www.simulace.info/index.php?title=File:JIRAb.xlsx&diff=17139File:JIRAb.xlsx2019-01-21T16:12:32Z<p>Manj01: </p>
<hr />
<div></div>Manj01http://www.simulace.info/index.php?title=File:Cost10developers.xls&diff=17138File:Cost10developers.xls2019-01-21T16:12:17Z<p>Manj01: </p>
<hr />
<div></div>Manj01http://www.simulace.info/index.php?title=WS_2018/2019&diff=17103WS 2018/20192019-01-20T20:15:13Z<p>Manj01: </p>
<hr />
<div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br />
<br />
==Simulations==<br />
<br />
--[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br />
<br />
-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 16:50, 18 January 2019 (CET) [[Optimal size of HDD for virtual Digitization server]]<br />
<br />
-- Jiří Korčák [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 11:09, 19 January 2019 (CET) [[Vacuum cleaner]]<br />
<br />
-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 14:46, 19 January 2019 (CET) [[Ticket Solving Process at a Small IT dev Company]] <br />
<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:43, 19 January 2019 (CET) [[evacuation from burning building]]<br />
<br />
-- [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 12:11, 20 January 2019 (CET) [[Medieval Battle Simulation]]<br />
<br />
-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 16:19, 20 January 2019 (CET) [[Argentinska Intersection]]<br />
<br />
-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 16:41, 20 January 2019 (CET) [[You are what you eat]]<br />
<br />
==Papers==<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br />
<br />
-- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]<br />
<br />
-- Jan Pippal (xpipj04) [[User:Janpippal|Janpippal]] 4:48, 20 January 2019 (CET) [https://en.wikipedia.org/wiki/Draft:MMABP MMABP in English]<br />
<br />
-- [[User:Qnesa01|Qnesa01]] ([User talk:Qnesa01|talk]]) 17:19, 20 January 2019 (CET) [[Limits to Growth_ver2]] (WIP)</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17102Ticket Solving Process at a Small IT dev Company2019-01-20T20:09:00Z<p>Manj01: done</p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. <br />
Answers to these questions could all be get from the results file, which is unfortunately impossible to upload. <br />
{| class="wikitable"<br />
<br />
|-<br />
|'''Number or Developers'''<br />
|2<br />
|5<br />
|10<br />
|100<br />
|-<br />
|'''Needed number of testers'''<br />
|1<br />
|1<br />
|1<br />
|1<br />
|-<br />
|'''Total cost of workers'''<br />
|13 152 748 CZK<br />
|32 605 759 CZK<br />
|63 842 645 CZK<br />
|76 281 384 CZK<br />
|-<br />
|'''Percentage of issues solved in given year'''<br />
|17%<br />
|43%<br />
|82%<br />
|98%<br />
|}<br />
Note that both testers and developers are paid per hour. This is reflected in the simulation and it works such in reality. If hiring too many developers, they might not be satisfied with the fact that they do not have much work.<br />
<br />
==Conclusion==<br />
A working and useful simulation has been created. Such simulation could provide good insight on what would happen in case more developers were hired. No specific requirement has been set for finding a corresponding value, but the management would be able to find it in the simulation on its own. <br />
<br />
==Code==<br />
The code itself, as well as numerous reports and input xlsx file can be found [https://vse-my.sharepoint.com/:u:/g/personal/manj01_vse_cz/EU8mTuZtNKdEodPeOMH7EfMBXbz6EogDmv8xcyCwb3vOFQ?e=Nelsbg here] (OneDrive link). Feel free to contact me on manj01@vse.cz in case the link is not working. <br />
<br />
==References== <br />
http://simprocess.com/about-simprocess/simprocess-documentation/</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17004Ticket Solving Process at a Small IT dev Company2019-01-19T16:36:33Z<p>Manj01: /* Results */</p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. <br />
Answers to these questions could all be get from the results file, which is unfortunately impossible to upload. <br />
{| class="wikitable"<br />
<br />
|-<br />
|'''Number or Developers'''<br />
|2<br />
|5<br />
|10<br />
|100<br />
|-<br />
|'''Needed number of testers'''<br />
|1<br />
|1<br />
|1<br />
|1<br />
|-<br />
|'''Total cost of workers'''<br />
|13 152 748 CZK<br />
|32 605 759 CZK<br />
|63 842 645 CZK<br />
|76 281 384 CZK<br />
|-<br />
|'''Percentage of issues solved in given year'''<br />
|17%<br />
|43%<br />
|82%<br />
|98%<br />
|}<br />
Note that both testers and developers are paid per hour. This is reflected in the simulation and it works such in reality. If hiring too many developers, they might not be satisfied with the fact that they do not have much work.<br />
<br />
==Conclusion==<br />
A working and useful simulation has been created. Such simulation could provide good insight on what would happen in case more developers were hired. No specific requirement has been set for finding a corresponding value, but the management would be able to find it in the simulation on its own. <br />
<br />
==References== <br />
http://simprocess.com/about-simprocess/simprocess-documentation/</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17003Ticket Solving Process at a Small IT dev Company2019-01-19T16:31:58Z<p>Manj01: /* Results */</p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. <br />
Answers to these questions could all be get from the results file, which is unfortunately impossible to upload. <br />
{| class="wikitable"<br />
<br />
|-<br />
|'''Number or Developers'''<br />
|2<br />
|5<br />
|10<br />
|-<br />
|'''Needed number of testers'''<br />
|1<br />
|1<br />
|1<br />
|-<br />
|'''Total cost of workers'''<br />
|13 152 748 CZK<br />
|32 605 759 CZK<br />
|63 842 645 CZK<br />
|-<br />
|'''Percentage of issues solved in given year'''<br />
|17%<br />
|43%<br />
|82%<br />
|}<br />
<br />
==Conclusion==<br />
A working and useful simulation has been created. Such simulation could provide good insight on what would happen in case more developers were hired. No specific requirement has been set for finding a corresponding value, but the management would be able to find it in the simulation on its own. <br />
<br />
==References== <br />
http://simprocess.com/about-simprocess/simprocess-documentation/</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17002Ticket Solving Process at a Small IT dev Company2019-01-19T16:31:30Z<p>Manj01: done, but I did not manage to upload any file</p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. <br />
Answers to these questions could all be get from the results file, which is unfortunately impossible to upload. <br />
{| class="wikitable"<br />
<br />
|-<br />
|'''Number or Developers'''<br />
|2<br />
|5<br />
|10<br />
|-<br />
|'''Needed number of testers'''<br />
|1<br />
|1<br />
|1<br />
|-<br />
|'''Total cost of workers'''<br />
|13 152 748 CZK<br />
|32 605 759 CZK<br />
|63 842 645 CZK<br />
|'''Percentage of issues solved in given year'''<br />
|17%<br />
|43%<br />
|82%<br />
|}<br />
<br />
==Conclusion==<br />
A working and useful simulation has been created. Such simulation could provide good insight on what would happen in case more developers were hired. No specific requirement has been set for finding a corresponding value, but the management would be able to find it in the simulation on its own. <br />
<br />
==References== <br />
http://simprocess.com/about-simprocess/simprocess-documentation/</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17001Ticket Solving Process at a Small IT dev Company2019-01-19T16:10:59Z<p>Manj01: /* Results */</p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. <br />
Answers to these questions could all be get from the results file, available here: [[Media:Simulation_results.zip]]</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=17000Ticket Solving Process at a Small IT dev Company2019-01-19T16:08:00Z<p>Manj01: </p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Those are all the tickets that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. Given the fact the developer currently works on the issues 9% of the time of the week, a have multiplied the differences between created and updated by 0.089. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes and I have used normal distribution to define these. <br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(157.822, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
The model is set in the year 2019, does not include issues that are already in the queue by the begging of the year. The most import output of the model is issues in queue/issues solved by the end of the year, based on the expenses. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually. Currently there are five developers working 12 hour per week on fixing issues, but for the sake of simulation, I use developers that would work only on the tickets. Thus, 2 developers in the simulation are equal to the five real developers. <br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Results==<br />
For all the results, 20 replications were used to get the best data while not wasting too much time. <br />
With the setting that is currently in the company (2 developers working full time on ticket solving and one tester), only around 17% of tickets are solved, while other get stuck in the queue. This roughly correspond with the real-life situation observed in the 2018. <br />
On the current state, developer has very high utilization, while tester has very low utilization.<br />
The model could answer some interesting questions which could be used by company management:<br />
How many developers are need to reach 50% fixed issues per year?<br />
From what number of developers does their utilization go down?<br />
We will ever need a new tester?<br />
How many testers<br />
From testing various values of developers, we can seen that their utilization goes down?<br />
and so on. Answers to all these questions can be read in the report Logs, such as [[Media:Example.ogg]]</div>Manj01http://www.simulace.info/index.php?title=WS_2018/2019&diff=16999WS 2018/20192019-01-19T14:50:57Z<p>Manj01: </p>
<hr />
<div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br />
<br />
==Simulations==<br />
<br />
--[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br />
<br />
-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 16:50, 18 January 2019 (CET) [[Optimal size of HDD for virtual Digitization server]]<br />
<br />
-- Jiří Korčák [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 11:09, 19 January 2019 (CET) [[Vacuum cleaner]]<br />
<br />
-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 14:46, 19 January 2019 (CET) [[Ticket Solving Process at a Small IT dev Company]] (work in progress)<br />
<br />
==Papers==<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br />
<br />
-- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]</div>Manj01http://www.simulace.info/index.php?title=Ticket_Solving_Process_at_a_Small_IT_dev_Company&diff=16997Ticket Solving Process at a Small IT dev Company2019-01-19T14:44:25Z<p>Manj01: A</p>
<hr />
<div>==Information==<br />
• Project name: Ticket Solving Process at a Small IT dev Company<br />
<br />
• Class: 4IT496 (WS 2018/2019)<br />
<br />
• Author: Bc. Jan Mandík<br />
<br />
• Model type: Discrete-event simulation<br />
<br />
• Software used: SimProcess, trial version<br />
<br />
<br />
==Problem definition==<br />
<br />
There is a small IT company, which developed an IT Product (an email client) a long time ago. Today, customers or QA personnel report bugs, which are then analyzed by testers and given to developers to fix. After fixing the issue, the ticket is given back to testers to test the solution. The time required to fix the bug is extremely long, as there are not so many developers and the bugs are often difficult to fix. A single developer spend only three hours per day fixing the issues, as he spends most of the time developing new features of the program. The company management wonders how many full time ticket-solving only developers would be needed to fix the issues at the same pace as the issues are being created.<br />
As to the other parts of the ticket life cycle - testing and retesting takes neglible amount of time compared to the development time, it is however an essential part of the process. <br />
<br />
==Aggregated real data==<br />
<br />
The company uses a very simple setting of Jira ticketing software. It is possible to export data to CSV from Jira. In this export, there is a number of columns, most of which is useless. The columns I used are Created Date, Updated Date and Status. Status Column can have values ToDo, In Review, Done, In Progress. Out of these, only items with Done status were used, as only these have usable Updated Date.<br />
Updated Date present time at which a tester marked this issue as done. Created Date present a point in time in which the issue was created (after its analysis).<br />
Out of these points in time, it is possible to get numbers of created issues per day and difference between time it was created and the time it was marked as Done (in hours). The distribution of these values seems to be random, but I have Simprocess in-build ModelFit function to describe these distributions mathematically. The distribution of created issues per day seems to be Hyper Exponential Distribution and the distribution of hours in repair is Gamma Distribution. These are the expected distributions for this type of input, and simprocess ModelFit function was able to provide the best possible parametrs.<br />
The CSV file with has 345 usable lines. Thise are all the ticket that have been marked as solved in the company's Jira since the Jira system launch. These 345 are enough to provide a distribution that allows the model to resemble real life workflow. <br />
As to the analysis and retesting, these are not visible in the Jira. As I was the only employee responsible for these process, I have a good overview of how long it takes. Given the fact these are truly negligible, I have set the analysis to take one hour and and the retest to take half one hour. <br />
<br />
<br />
<br />
==Model==<br />
<br />
The model is very simple given the company small size and the low level of Jira implementation. The model consist of one Generate Stage, three wait stages, and one Dispose Stage. The stages are described below:<br />
Generate: Simulates the real life inflow of new tickets from customers. Based on 345 cases, the inflow is defined as Hex(0.203, 1.632, 0.75) issues per that, that is is 0.56 issues per day on average.<br />
Wait 1 (Analysis): Simulates the analysis need to give the ticket to developers. It is simplified to take Nor(0.7,0.2,1) hours, based on my experience in this job. One tester is needed as a resource for this stage.<br />
Wait 2 (Repair): Simulates the repair process itself, which constitutes for most of the time. It is based on the before-mentioned date from Jire and the distribution is created by Simprocess from this date. It takes Gam(1773.276, 0.597) hours - as you can see, this amount is truly neglible compared to Analysis and Retesting. <br />
Wait 3 (Retesting): Simulates the retesting necessary to marked the issue as done. It is a very quick process, it takes Nor(0.5,0.2,1) hours. <br />
Dispose: The issue is fixed and retested. <br />
<br />
<br />
<br />
==Entities==<br />
The only entity is defect, that is not prioritized. <br />
<br />
==Resources==<br />
There are two resources. Its properties are based on the real data seen in the company:<br />
Developer: Needed for Repair stage. Works only on working day from 10am do 6pm. Costs 750 CZK per hour and 25000 annually.<br />
Tester: Needed for Analysis and Retesting stages. Willing to work every day from 10am to 8pm because his utilization is low. Costs 250 CZK per hour and 25000 annually. <br />
<br />
==Processes==<br />
<br />
<br />
<br />
==Optimal model==<br />
<br />
==Code==</div>Manj01http://www.simulace.info/index.php?title=WS_2018/2019&diff=16988WS 2018/20192019-01-19T13:47:05Z<p>Manj01: </p>
<hr />
<div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br />
<br />
==Simulations==<br />
<br />
--[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br />
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-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 16:50, 18 January 2019 (CET) [[Optimal size of HDD for virtual Digitization server]]<br />
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-- Jiří Korčák [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 11:09, 19 January 2019 (CET) [[Vacuum cleaner]]<br />
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-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 14:46, 19 January 2019 (CET) [[Ticket Solving Process at a Small IT dev Company]]<br />
<br />
==Papers==<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br />
<br />
-- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]</div>Manj01http://www.simulace.info/index.php?title=WS_2018/2019&diff=16987WS 2018/20192019-01-19T13:46:44Z<p>Manj01: /* Simulations */</p>
<hr />
<div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br />
<br />
==Simulations==<br />
<br />
--[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br />
<br />
-- Jan Doležálek [[User:Dolj04|Dolj04]] ([[User talk:Dolj04|talk]]) 16:50, 18 January 2019 (CET) [[Optimal size of HDD for virtual Digitization server]]<br />
<br />
-- Jiří Korčák [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 11:09, 19 January 2019 (CET) [[Vacuum cleaner]]<br />
<br />
-- Jan Mandík [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 14:46, 19 January 2019 (CET) [[Ticket Solving Process at a small company]]<br />
<br />
==Papers==<br />
-- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br />
<br />
-- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]</div>Manj01http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16835Assignments WS 2018/20192018-12-31T13:38:44Z<p>Manj01: /* Simulation proposal Manj01 (talk) 15:21, 21 December 2018 (CET) */</p>
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Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br />
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Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br />
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Topics on gambling, cards, etc. are not welcome.<br />
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In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br />
</div><br />
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== Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae (Not approved yet)==<br />
<br />
This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br />
This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br />
Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br />
<br />
The algae should work in two modes: food search and optimisation. The food search part involves the algae spreading in a radial pattern, until it finds a foodsource. When it succeeds, it should optimise its pathways to allow for fastest nutrient transport.<br />
The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well. A test of function could be for the algae to find a path through a wall with three holes and to choose the proper hole (with shortest path).<br />
<br />
Initial plan<br />
There is an initial node, where the algae begins and from which it spreads.<br />
Food is randomly distributed across the area, defined be a patch of certain color.<br />
Algae spreads as agents, leaving behind patches as temporary algae. Temporary algae have a time to live, a timeout. When foodsource is found, nutrients travel as agents back to to initial node. When nutrients visit an algae patch, they reset its timeout. Thus, only the algae which has nutrients travelling on it can survive. With a properly applied randonmness, for the travel of nutrients as well as the agents, this should optimise the pathways.<br />
<br />
''Assignment''<br />
Title: Simulation of semi-intelligent algae<br />
<br />
Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br />
<br />
Author: Bc. Martin Vegner<br />
<br />
Model type: Multiagent<br />
<br />
Modeling tool: NetLogo<br />
<br />
[[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br />
<br />
: Generally, it could be done, but it is necessary to elaborate this assignment into greater detail. How you will measure the results? How you can say if it was successful or not? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:21, 31 December 2018 (CET)<br />
:: Asiggnment updated. Please revise. [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 14:33, 31 December 2018 (CET)<br />
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== Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br />
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This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br />
<br />
<br />
''Assignment''<br />
<br />
Title: Find the thief<br />
<br />
Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br />
<br />
Author: Bc. Jiří Korčák<br />
<br />
Model type: Multiagent<br />
<br />
Modeling tool: NetLogo<br />
<br />
[[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br />
<br />
: Sorry, but I don't understand what is this simulation good for. What meaningful problem do you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:44, 31 December 2018 (CET)<br />
::The problem should be about how memory, number of police officers and shared informations influence searching for a thief. It could have applications for AI in computer games (thats where you meet agents like this ones) or even in real life - most important thing when looking for something is: ... (when there is 20 of us looking for something, memory and shared information is not that important, etc.). [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 10:36, 31 December 2018 (CET)<br />
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== Simulation proposal (dolj04) ==<br />
<br />
Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br />
<br />
Definition of the problem: <br/><br />
- each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)<br/><br />
- the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)<br/><br />
- the average size is not changing that much<br/><br />
- (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)<br/><br />
- backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)<br/><br />
- successfully processed batches older than 14 days are deleted every day<br/><br />
- for precaution lets say around 5% of batches wont be processed correctly<br/><br />
- those will stay on the server and will be processed every month by admins <br/><br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br />
<br />
:::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br />
<br />
::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br />
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== Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br />
<br />
Topic: '''likelihood of infection with flu'''<br />
<br />
Definition of the problem: <br/><br />
- everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population<br/><br />
<br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br />
<br />
:::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br />
:::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br />
:::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br />
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== Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ==<br />
<br />
Topic: '''Social and economical effects of reunification of North & South Korea''' <br/> <br/><br />
'''Definition of the problem:''' <br/><br />
Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: <br/><br />
- North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. <br/><br />
- South Koreans will invest in North Korea area and create jobs and factories there. <br/> <br/><br />
''I would like to simulate:'' <br/><br />
- What will be the population ratio of the two areas per square meter. <br/><br />
- How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. <br/><br />
- How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. <br/><br />
- What will be the housing situation after the reunification. <br/><br />
- How will the GDP of the new country possibly develop. <br/><br />
- Possibly how much money will be needed from international help. <br/> <br/><br />
'''Simulation environment:''' NetLogo<br />
<br />
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: I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br />
----<br />
:: Thank you for the feedback - I tried to break down and simplify the simulation:<br />
<br />
:: '''Agents:'''<br />
<br />
:: - North Koreans (0-14 years)<br />
:: - North Koreans (15-24 years)<br />
:: - North Koreans (25-54 years)<br />
:: - North Koreans (55-64 years)<br />
:: - North Koreans (65 years and over)<br />
<br />
:: - South Koreans (0-14 years)<br />
:: - South Koreans (15-24 years)<br />
:: - South Koreans (25-54 years)<br />
:: - South Koreans (55-64 years)<br />
:: - South Koreans (65 years and over)<br />
<br />
:: - South Korea listed companies<br />
:: - Foregin investors<br />
<br />
:: '''Parameters'''<br />
:: - North Korea GDP<br />
:: - North Korea GDP growth<br />
<br />
:: - South Korea GDP<br />
:: - South Korea GDP growth<br />
<br />
:: - North Korea area [sq. meters]<br />
:: - South Korea area [sq. meters] <br />
<br />
:: - North Korea unemployment rate<br />
:: - South Korea unemployment rate<br />
:: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br />
<br />
:: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br />
:: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br />
<br />
:: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br />
<br />
:: '''Targets of the simulation:'''<br />
<br />
:: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br />
:: - Determine unemployment in the reunited country<br />
:: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br />
:: - Determine new GDP based on above data (foreign investors being random)<br />
<br />
:: '''Data sources:'''<br />
<br />
:: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br />
<br />
:: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br />
<br />
:: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br />
<br />
:: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br />
<br />
::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br />
<br />
:::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br />
:::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br />
:::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br />
:::: - On each tick, the simulation will determine:<br />
::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br />
::::: - The wealth of the patch and turtle -> if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br />
::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br />
:::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br />
<br />
::::: To be honest, I am a bit skeptical about the results. As the assignment is based on vague assumptions, I think the results will be very general. On the other hand, I appreciate the level of preparation, which is above average. Hence, it is exceptionally '''approved''' and I hope you invest the same level of effort into the simulation itself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:52, 31 December 2018 (CET)<br />
:::::: Thank you! [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 12:58, 31 December 2018 (CET)<br />
<br />
== Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br />
<br />
Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).<br/><br />
- Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).<br/><br />
- Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.<br/><br />
- Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.<br/><br />
<br />
'''Simulation environment:''' Netlogo<br />
'''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br />
<br />
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<br />
== Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Maze runner vs fire'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br />
<br />
'''Simulation environment''': Netlogo<br />
<br />
: What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br />
:: to find out whether a person who is lost in a bulding will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br />
::: It is just a modification of our Building escape example. Ok, it could be doable, but there must be a clear and substantial added value. Your assignment proposal must be much much more deeply elaborated. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:56, 31 December 2018 (CET)<br />
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==Simulation proposal [[User:qnesa01|qnesa01]] ==<br />
<br />
Topic/goal: '''Traffic simulation Argentinska street'''<br />
<br />
'''Situation:'''<br/><br />
- Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br />
<br />
'''Definition of the problem:'''<br/><br />
- There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br />
<br />
<br />
'''Purpose of simulation:'''<br/><br />
In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br />
<br />
<br />
'''Simulation environment''': Simprocess; SUMO (for traffic representation) <br />
<br />
'''Brief process of simulation :'''<br/><br />
1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br />
2) Real situation simulation <br />
3) Based on simulation of real situation will be checked the efficiency of lights changes <br />
4) The hypothetical model of adding one more line will be created based on simulation of real situation <br />
5) Summary<br />
<br />
: Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br />
: The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br />
:: Sorry, I still don't understand. Please, could you describe, how the simulation will look like in Simprocess and in SUMO? What will be the data you pass from SUMO to Simprocess (or vice versa)?<br />
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<br />
== Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br />
<br />
<br />
<br />
Topic/goal: '''Survival of the apocalypse'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. <br/><br />
<br />
The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br />
for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br />
the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br />
<br />
Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br />
effectivnes of the plague.<br />
<br />
'''Simulation environment''': Netlogo<br />
<br />
: Please, no zombie-topics! The problem is that you cannot verify such an assignment with real data. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:43, 21 December 2018 (CET)<br />
<br />
:: Alright then. My second suggestion would be simulating slime mold searching for food. (gif representing the mold [[https://www.reddit.com/r/gifs/comments/a1geie/slime_mold_searching_for_food/|here]] ) I should be able to confront that with a reality and the number of “foods” and speed of the mold could be variable. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:36, 21 December 2018 (CET)<br />
::: Why not, just please, elaborate the assignment into detail. What will be the agents? What will be the parameters, and why...?<br />
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<br />
== Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Medieval Battle Simulation'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.<br/><br />
Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).<br/><br />
Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.<br/><br />
When they meet in battle, they fight to the last man who wins the dispute for his king.</br><br />
<br />
'''Purpose of simulation:'''<br/><br />
It simulates medieval combat on battlefield between two sides.<br/><br />
The user can select which type of units and how many will each kingdom have.<br/><br />
Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br />
<br />
'''Simulation environment:''' NetLogo<br />
<br />
: I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br />
<br />
:: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br />
<br />
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<br />
== Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
At the moment we have a venue in a small town under construction which we would like to turn into a café.<br/> <br />
The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. <br/><br />
<br />
'''Purpose of simulation:'''<br/><br />
As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.<br/> <br />
I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.<br/><br />
<br/><br />
The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br />
<br />
'''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br />
<br />
: I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br />
<br />
::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br />
<br />
::: If you need to deal with spatial factors, NetLogo is definitely a better choice. If you omit the spatial characteristics, the simulation become trivial, what would not be enough for this paper. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:06, 31 December 2018 (CET)<br />
<br />
:::: Ok, I can do the simulation in NetLogo and mind the spatial characteristics. I just thought the original idea was complex enought as it would put together several rather simple simulations and tasks - like finding out the right amount of visitors based on data from people that run cafés in different places (recalculating the number of visitors based on the wealthiness of the region, based on the capacity of the venue, the number of citizens and so on). <br />
::::I believe I can go with netLogo for simulating the venue as well, although it is not completely clear now for me, how do I find out the right ammount of seats for example?<br />
::::I can imagine a random movement of agents coming in taking a first not-taken seat. It can also be like the agent has a capacity (it can be group of 4 or just a pair or a single person visitor) and it could take the table/seat only in case there is enough space for the whole group. There could also be a quality parameters - like grading system for the seats - how far is it from the bar, is it near the toilet and so on. So yes, that might show that some places will be more popular then others. Also the agents might have a limit under which they dont go, like if they dont find a seat with quality high enough they will leave. Now when I am writing this down I think it might work although I still have some questions. Is this something you had in mind as well or am I on completely different track? Thanks. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 11:26, 31 December 2018 (CET)<br />
<br />
== Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br />
<br />
'''Purpose of simulation:'''<br/><br />
Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br />
<br />
'''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br />
<br />
:: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br />
<br />
<br />
::: Ok, these are not available, so I will try a completely different idea:<br />
<br />
<br />
Topic/goal: '''Archiving maximum efficiency in ticket management of a small company (real-life based data)'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
A small company has support personnel, testers and developers in one office. They together solve tickets created by customers. These tickets sometimes get stuck in ticketing system, because the numbers of people working there are not perfect. Campany uses Kayako and Jira to manage tickets - from these systems, there will be a lot of real-life data to base the simulation on. <br />
<br />
'''Purpose of simulation:'''<br/><br />
The simulation aims to provide the ideal amount of support people, testers/analysts and developers to solve tickets as quick as possible, while there are constraints, such as company budget and lowered effectivity of too many people working on the same ticket. <br />
<br />
'''Simulation environment:''' I would like to use Simprocess to model and optimize the workflow.<br />
<br />
== Simulation proposal (xpipj04) ==<br />
<br />
Topic/goal: You are what you eat<br />
<br />
'''Definition of the problem:'''<br/><br />
Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br />
<br />
'''Purpose of simulation:'''<br/><br />
Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br />
<br />
'''Input parameters'''<br/><br />
- Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) <br/><br />
- Time spent at gym (kardio, workout, culturist) <br/><br />
- Time spent at work - type of work (manual, manager) <br/><br />
- Time spent sleeping <br/><br />
<br />
'''Output'''<br/><br />
- Your BMI <br/><br />
- Your fitness level <br/><br />
- Your work focus <br/><br />
- Your energy level <br/><br />
<br />
'''Where I will get the data? '''<br/><br />
- My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. <br/><br />
- Nutrition plans.<br/> <br />
<br />
'''How will I get the output?'''<br/><br />
I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br />
If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br />
<br />
:: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br />
:::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br />
:::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>Manj01http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16834Assignments WS 2018/20192018-12-31T13:37:55Z<p>Manj01: /* Simulation proposal Manj01 (talk) 15:21, 21 December 2018 (CET) */</p>
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Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br />
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Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br />
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Topics on gambling, cards, etc. are not welcome.<br />
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In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br />
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== Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae (Not approved yet)==<br />
<br />
This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br />
This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br />
Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br />
<br />
The algae should work in two modes: food search and optimisation. The food search part involves the algae spreading in a radial pattern, until it finds a foodsource. When it succeeds, it should optimise its pathways to allow for fastest nutrient transport.<br />
The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well. A test of function could be for the algae to find a path through a wall with three holes and to choose the proper hole (with shortest path).<br />
<br />
Initial plan<br />
There is an initial node, where the algae begins and from which it spreads.<br />
Food is randomly distributed across the area, defined be a patch of certain color.<br />
Algae spreads as agents, leaving behind patches as temporary algae. Temporary algae have a time to live, a timeout. When foodsource is found, nutrients travel as agents back to to initial node. When nutrients visit an algae patch, they reset its timeout. Thus, only the algae which has nutrients travelling on it can survive. With a properly applied randonmness, for the travel of nutrients as well as the agents, this should optimise the pathways.<br />
<br />
''Assignment''<br />
Title: Simulation of semi-intelligent algae<br />
<br />
Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br />
<br />
Author: Bc. Martin Vegner<br />
<br />
Model type: Multiagent<br />
<br />
Modeling tool: NetLogo<br />
<br />
[[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br />
<br />
: Generally, it could be done, but it is necessary to elaborate this assignment into greater detail. How you will measure the results? How you can say if it was successful or not? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:21, 31 December 2018 (CET)<br />
:: Asiggnment updated. Please revise. [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 14:33, 31 December 2018 (CET)<br />
<br />
== Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br />
<br />
This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br />
<br />
<br />
''Assignment''<br />
<br />
Title: Find the thief<br />
<br />
Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br />
<br />
Author: Bc. Jiří Korčák<br />
<br />
Model type: Multiagent<br />
<br />
Modeling tool: NetLogo<br />
<br />
[[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br />
<br />
: Sorry, but I don't understand what is this simulation good for. What meaningful problem do you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:44, 31 December 2018 (CET)<br />
::The problem should be about how memory, number of police officers and shared informations influence searching for a thief. It could have applications for AI in computer games (thats where you meet agents like this ones) or even in real life - most important thing when looking for something is: ... (when there is 20 of us looking for something, memory and shared information is not that important, etc.). [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 10:36, 31 December 2018 (CET)<br />
<br />
== Simulation proposal (dolj04) ==<br />
<br />
Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br />
<br />
Definition of the problem: <br/><br />
- each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)<br/><br />
- the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)<br/><br />
- the average size is not changing that much<br/><br />
- (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)<br/><br />
- backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)<br/><br />
- successfully processed batches older than 14 days are deleted every day<br/><br />
- for precaution lets say around 5% of batches wont be processed correctly<br/><br />
- those will stay on the server and will be processed every month by admins <br/><br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br />
<br />
:::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br />
<br />
::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br />
<br />
== Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br />
<br />
Topic: '''likelihood of infection with flu'''<br />
<br />
Definition of the problem: <br/><br />
- everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population<br/><br />
<br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br />
<br />
:::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br />
:::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br />
:::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br />
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== Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ==<br />
<br />
Topic: '''Social and economical effects of reunification of North & South Korea''' <br/> <br/><br />
'''Definition of the problem:''' <br/><br />
Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: <br/><br />
- North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. <br/><br />
- South Koreans will invest in North Korea area and create jobs and factories there. <br/> <br/><br />
''I would like to simulate:'' <br/><br />
- What will be the population ratio of the two areas per square meter. <br/><br />
- How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. <br/><br />
- How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. <br/><br />
- What will be the housing situation after the reunification. <br/><br />
- How will the GDP of the new country possibly develop. <br/><br />
- Possibly how much money will be needed from international help. <br/> <br/><br />
'''Simulation environment:''' NetLogo<br />
<br />
----<br />
: I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br />
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:: Thank you for the feedback - I tried to break down and simplify the simulation:<br />
<br />
:: '''Agents:'''<br />
<br />
:: - North Koreans (0-14 years)<br />
:: - North Koreans (15-24 years)<br />
:: - North Koreans (25-54 years)<br />
:: - North Koreans (55-64 years)<br />
:: - North Koreans (65 years and over)<br />
<br />
:: - South Koreans (0-14 years)<br />
:: - South Koreans (15-24 years)<br />
:: - South Koreans (25-54 years)<br />
:: - South Koreans (55-64 years)<br />
:: - South Koreans (65 years and over)<br />
<br />
:: - South Korea listed companies<br />
:: - Foregin investors<br />
<br />
:: '''Parameters'''<br />
:: - North Korea GDP<br />
:: - North Korea GDP growth<br />
<br />
:: - South Korea GDP<br />
:: - South Korea GDP growth<br />
<br />
:: - North Korea area [sq. meters]<br />
:: - South Korea area [sq. meters] <br />
<br />
:: - North Korea unemployment rate<br />
:: - South Korea unemployment rate<br />
:: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br />
<br />
:: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br />
:: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br />
<br />
:: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br />
<br />
:: '''Targets of the simulation:'''<br />
<br />
:: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br />
:: - Determine unemployment in the reunited country<br />
:: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br />
:: - Determine new GDP based on above data (foreign investors being random)<br />
<br />
:: '''Data sources:'''<br />
<br />
:: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br />
<br />
:: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br />
<br />
:: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br />
<br />
:: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br />
<br />
::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br />
<br />
:::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br />
:::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br />
:::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br />
:::: - On each tick, the simulation will determine:<br />
::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br />
::::: - The wealth of the patch and turtle -> if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br />
::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br />
:::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br />
<br />
::::: To be honest, I am a bit skeptical about the results. As the assignment is based on vague assumptions, I think the results will be very general. On the other hand, I appreciate the level of preparation, which is above average. Hence, it is exceptionally '''approved''' and I hope you invest the same level of effort into the simulation itself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:52, 31 December 2018 (CET)<br />
:::::: Thank you! [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 12:58, 31 December 2018 (CET)<br />
<br />
== Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br />
<br />
Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).<br/><br />
- Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).<br/><br />
- Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.<br/><br />
- Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.<br/><br />
<br />
'''Simulation environment:''' Netlogo<br />
'''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br />
<br />
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== Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Maze runner vs fire'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br />
<br />
'''Simulation environment''': Netlogo<br />
<br />
: What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br />
:: to find out whether a person who is lost in a bulding will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br />
::: It is just a modification of our Building escape example. Ok, it could be doable, but there must be a clear and substantial added value. Your assignment proposal must be much much more deeply elaborated. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:56, 31 December 2018 (CET)<br />
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==Simulation proposal [[User:qnesa01|qnesa01]] ==<br />
<br />
Topic/goal: '''Traffic simulation Argentinska street'''<br />
<br />
'''Situation:'''<br/><br />
- Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br />
<br />
'''Definition of the problem:'''<br/><br />
- There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br />
<br />
<br />
'''Purpose of simulation:'''<br/><br />
In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br />
<br />
<br />
'''Simulation environment''': Simprocess; SUMO (for traffic representation) <br />
<br />
'''Brief process of simulation :'''<br/><br />
1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br />
2) Real situation simulation <br />
3) Based on simulation of real situation will be checked the efficiency of lights changes <br />
4) The hypothetical model of adding one more line will be created based on simulation of real situation <br />
5) Summary<br />
<br />
: Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br />
: The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br />
:: Sorry, I still don't understand. Please, could you describe, how the simulation will look like in Simprocess and in SUMO? What will be the data you pass from SUMO to Simprocess (or vice versa)?<br />
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<br />
== Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br />
<br />
<br />
<br />
Topic/goal: '''Survival of the apocalypse'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. <br/><br />
<br />
The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br />
for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br />
the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br />
<br />
Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br />
effectivnes of the plague.<br />
<br />
'''Simulation environment''': Netlogo<br />
<br />
: Please, no zombie-topics! The problem is that you cannot verify such an assignment with real data. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:43, 21 December 2018 (CET)<br />
<br />
:: Alright then. My second suggestion would be simulating slime mold searching for food. (gif representing the mold [[https://www.reddit.com/r/gifs/comments/a1geie/slime_mold_searching_for_food/|here]] ) I should be able to confront that with a reality and the number of “foods” and speed of the mold could be variable. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:36, 21 December 2018 (CET)<br />
::: Why not, just please, elaborate the assignment into detail. What will be the agents? What will be the parameters, and why...?<br />
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<br />
== Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Medieval Battle Simulation'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.<br/><br />
Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).<br/><br />
Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.<br/><br />
When they meet in battle, they fight to the last man who wins the dispute for his king.</br><br />
<br />
'''Purpose of simulation:'''<br/><br />
It simulates medieval combat on battlefield between two sides.<br/><br />
The user can select which type of units and how many will each kingdom have.<br/><br />
Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br />
<br />
'''Simulation environment:''' NetLogo<br />
<br />
: I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br />
<br />
:: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br />
<br />
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<br />
== Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
At the moment we have a venue in a small town under construction which we would like to turn into a café.<br/> <br />
The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. <br/><br />
<br />
'''Purpose of simulation:'''<br/><br />
As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.<br/> <br />
I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.<br/><br />
<br/><br />
The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br />
<br />
'''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br />
<br />
: I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br />
<br />
::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br />
<br />
::: If you need to deal with spatial factors, NetLogo is definitely a better choice. If you omit the spatial characteristics, the simulation become trivial, what would not be enough for this paper. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:06, 31 December 2018 (CET)<br />
<br />
:::: Ok, I can do the simulation in NetLogo and mind the spatial characteristics. I just thought the original idea was complex enought as it would put together several rather simple simulations and tasks - like finding out the right amount of visitors based on data from people that run cafés in different places (recalculating the number of visitors based on the wealthiness of the region, based on the capacity of the venue, the number of citizens and so on). <br />
::::I believe I can go with netLogo for simulating the venue as well, although it is not completely clear now for me, how do I find out the right ammount of seats for example?<br />
::::I can imagine a random movement of agents coming in taking a first not-taken seat. It can also be like the agent has a capacity (it can be group of 4 or just a pair or a single person visitor) and it could take the table/seat only in case there is enough space for the whole group. There could also be a quality parameters - like grading system for the seats - how far is it from the bar, is it near the toilet and so on. So yes, that might show that some places will be more popular then others. Also the agents might have a limit under which they dont go, like if they dont find a seat with quality high enough they will leave. Now when I am writing this down I think it might work although I still have some questions. Is this something you had in mind as well or am I on completely different track? Thanks. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 11:26, 31 December 2018 (CET)<br />
<br />
== Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br />
<br />
'''Purpose of simulation:'''<br/><br />
Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br />
<br />
'''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br />
<br />
:: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br />
<br />
<br />
::: Ok, these are not available, so I will try a completely different idea:<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
A small company has support personnel, testers and developers in one office. They together solve tickets created by customers. These tickets sometimes get stuck in ticketing system, because the numbers of people working there are not perfect. Campany uses Kayako and Jira to manage tickets - from these systems, there will be a lot of real-life data to base the simulation on. <br />
<br />
'''Purpose of simulation:'''<br/><br />
The simulation aims to provide the ideal amount of support people, testers/analysts and developers to solve tickets as quick as possible, while there are constraints, such as company budget and lowered effectivity of too many people working on the same ticket. <br />
<br />
'''Simulation environment:''' I would like to use Simprocess to model and optimize the workflow.<br />
<br />
== Simulation proposal (xpipj04) ==<br />
<br />
Topic/goal: You are what you eat<br />
<br />
'''Definition of the problem:'''<br/><br />
Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br />
<br />
'''Purpose of simulation:'''<br/><br />
Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br />
<br />
'''Input parameters'''<br/><br />
- Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) <br/><br />
- Time spent at gym (kardio, workout, culturist) <br/><br />
- Time spent at work - type of work (manual, manager) <br/><br />
- Time spent sleeping <br/><br />
<br />
'''Output'''<br/><br />
- Your BMI <br/><br />
- Your fitness level <br/><br />
- Your work focus <br/><br />
- Your energy level <br/><br />
<br />
'''Where I will get the data? '''<br/><br />
- My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. <br/><br />
- Nutrition plans.<br/> <br />
<br />
'''How will I get the output?'''<br/><br />
I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br />
If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br />
<br />
:: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br />
:::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br />
:::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>Manj01http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16756Assignments WS 2018/20192018-12-21T14:26:31Z<p>Manj01: /* Simulation proposal Manj01 (talk) 15:21, 21 December 2018 (CET) */</p>
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Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br />
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Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br />
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Topics on gambling, cards, etc. are not welcome.<br />
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In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br />
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[[Xvegm00|Xvegm00]]<br />
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[[Xkorj58|Xkorj58 (not approved yet)]]<br />
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== Simulation proposal (dolj04) ==<br />
<br />
Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br />
<br />
Definition of the problem: <br/><br />
- each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)<br/><br />
- the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)<br/><br />
- the average size is not changing that much<br/><br />
- (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)<br/><br />
- backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)<br/><br />
- successfully processed batches older than 14 days are deleted every day<br/><br />
- for precaution lets say around 5% of batches wont be processed correctly<br/><br />
- those will stay on the server and will be processed every month by admins <br/><br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br />
<br />
:::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br />
<br />
::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br />
<br />
== Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br />
<br />
Topic: '''likelihood of infection with flu'''<br />
<br />
Definition of the problem: <br/><br />
- everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population<br/><br />
<br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br />
<br />
:::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br />
:::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br />
:::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br />
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== Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ''(Not approved yet)'' ==<br />
<br />
Topic: '''Social and economical effects of reunification of North & South Korea''' <br/> <br/><br />
'''Definition of the problem:''' <br/><br />
Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: <br/><br />
- North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. <br/><br />
- South Koreans will invest in North Korea area and create jobs and factories there. <br/> <br/><br />
''I would like to simulate:'' <br/><br />
- What will be the population ratio of the two areas per square meter. <br/><br />
- How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. <br/><br />
- How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. <br/><br />
- What will be the housing situation after the reunification. <br/><br />
- How will the GDP of the new country possibly develop. <br/><br />
- Possibly how much money will be needed from international help. <br/> <br/><br />
'''Simulation environment:''' NetLogo<br />
<br />
----<br />
: I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br />
----<br />
:: Thank you for the feedback - I tried to break down and simplify the simulation:<br />
<br />
:: '''Agents:'''<br />
<br />
:: - North Koreans (0-14 years)<br />
:: - North Koreans (15-24 years)<br />
:: - North Koreans (25-54 years)<br />
:: - North Koreans (55-64 years)<br />
:: - North Koreans (65 years and over)<br />
<br />
:: - South Koreans (0-14 years)<br />
:: - South Koreans (15-24 years)<br />
:: - South Koreans (25-54 years)<br />
:: - South Koreans (55-64 years)<br />
:: - South Koreans (65 years and over)<br />
<br />
:: - South Korea listed companies<br />
:: - Foregin investors<br />
<br />
:: '''Parameters'''<br />
:: - North Korea GDP<br />
:: - North Korea GDP growth<br />
<br />
:: - South Korea GDP<br />
:: - South Korea GDP growth<br />
<br />
:: - North Korea area [sq. meters]<br />
:: - South Korea area [sq. meters] <br />
<br />
:: - North Korea unemployment rate<br />
:: - South Korea unemployment rate<br />
:: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br />
<br />
:: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br />
:: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br />
<br />
:: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br />
<br />
:: '''Targets of the simulation:'''<br />
<br />
:: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br />
:: - Determine unemployment in the reunited country<br />
:: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br />
:: - Determine new GDP based on above data (foreign investors being random)<br />
<br />
:: '''Data sources:'''<br />
<br />
:: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br />
<br />
:: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br />
<br />
:: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br />
<br />
:: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br />
<br />
== Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br />
<br />
Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).<br/><br />
- Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).<br/><br />
- Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.<br/><br />
- Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.<br/><br />
<br />
'''Simulation environment:''' Netlogo<br />
----<br />
<br />
== Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Maze runner vs fire'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br />
<br />
'''Simulation environment''': Netlogo<br />
<br />
----<br />
<br />
==Simulation proposal [[User:qnesa01|qnesa01]] ''(Not approved yet)'' ==<br />
<br />
Topic/goal: '''Traffic simulation Argentinska street'''<br />
<br />
'''Situation:'''<br/><br />
- Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br />
<br />
'''Definition of the problem:'''<br/><br />
- There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br />
<br />
<br />
'''Purpose of simulation:'''<br/><br />
In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br />
<br />
<br />
'''Simulation environment''': Simprocess; SUMO (for traffic representation) <br />
<br />
'''Brief process of simulation :'''<br/><br />
1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br />
2) Real situation simulation <br />
3) Based on simulation of real situation will be checked the efficiency of lights changes <br />
4) The hypothetical model of adding one more line will be created based on simulation of real situation <br />
5) Summary<br />
<br />
----<br />
<br />
== Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br />
<br />
<br />
<br />
Topic/goal: '''Survival of the apocalypse'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. <br/><br />
<br />
The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br />
for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br />
the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br />
<br />
Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br />
effectivnes of the plague.<br />
<br />
'''Simulation environment''': Netlogo<br />
<br />
----<br />
<br />
== Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Medieval Battle Simulation'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.<br/><br />
Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).<br/><br />
Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.<br/><br />
When they meet in battle, they fight to the last man who wins the dispute for his king.</br><br />
<br />
'''Purpose of simulation:'''<br/><br />
It simulates medieval combat on battlefield between two sides.<br/><br />
The user can select which type of units and how many will each kingdom have.<br/><br />
Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br />
<br />
'''Simulation environment:''' NetLogo<br />
<br />
----<br />
<br />
== Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
At the moment we have a venue in a small town under construction which we would like to turn into a café.<br/> <br />
The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. <br/><br />
<br />
'''Purpose of simulation:'''<br/><br />
As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.<br/> <br />
I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.<br/><br />
<br/><br />
The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br />
<br />
'''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br />
<br />
== Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br />
<br />
'''Definition of the problem:'''<br/><br />
<br />
There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br />
<br />
'''Purpose of simulation:'''<br/><br />
Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br />
<br />
'''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.</div>Manj01http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16755Assignments WS 2018/20192018-12-21T14:22:10Z<p>Manj01: Simulation proposal manj01</p>
<hr />
<div>{{Ambox<br />
| text = <div><br />
Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br />
</div><br />
}}<br />
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{{Ambox<br />
| text = <div><br />
Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br />
</div><br />
}}<br />
<br />
{{Ambox<br />
| text = <div><br />
Topics on gambling, cards, etc. are not welcome.<br />
</div><br />
}}<br />
<br />
{{Ambox<br />
| type = content<br />
| text = <div><br />
In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br />
</div><br />
}}<br />
<br />
[[Xvegm00|Xvegm00]]<br />
<br />
[[Xkorj58|Xkorj58 (not approved yet)]]<br />
<br />
<br />
----<br />
<br />
== Simulation proposal (dolj04) ==<br />
<br />
Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br />
<br />
Definition of the problem: <br/><br />
- each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)<br/><br />
- the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)<br/><br />
- the average size is not changing that much<br/><br />
- (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)<br/><br />
- backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)<br/><br />
- successfully processed batches older than 14 days are deleted every day<br/><br />
- for precaution lets say around 5% of batches wont be processed correctly<br/><br />
- those will stay on the server and will be processed every month by admins <br/><br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br />
<br />
:::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br />
<br />
::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br />
<br />
== Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br />
<br />
Topic: '''likelihood of infection with flu'''<br />
<br />
Definition of the problem: <br/><br />
- everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population<br/><br />
<br />
<br />
Simulation environment: Vensim<br />
<br />
:: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br />
<br />
:::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br />
:::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br />
:::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br />
----<br />
<br />
== Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ''(Not approved yet)'' ==<br />
<br />
Topic: '''Social and economical effects of reunification of North & South Korea''' <br/> <br/><br />
'''Definition of the problem:''' <br/><br />
Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: <br/><br />
- North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. <br/><br />
- South Koreans will invest in North Korea area and create jobs and factories there. <br/> <br/><br />
''I would like to simulate:'' <br/><br />
- What will be the population ratio of the two areas per square meter. <br/><br />
- How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. <br/><br />
- How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. <br/><br />
- What will be the housing situation after the reunification. <br/><br />
- How will the GDP of the new country possibly develop. <br/><br />
- Possibly how much money will be needed from international help. <br/> <br/><br />
'''Simulation environment:''' NetLogo<br />
<br />
----<br />
: I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br />
----<br />
:: Thank you for the feedback - I tried to break down and simplify the simulation:<br />
<br />
:: '''Agents:'''<br />
<br />
:: - North Koreans (0-14 years)<br />
:: - North Koreans (15-24 years)<br />
:: - North Koreans (25-54 years)<br />
:: - North Koreans (55-64 years)<br />
:: - North Koreans (65 years and over)<br />
<br />
:: - South Koreans (0-14 years)<br />
:: - South Koreans (15-24 years)<br />
:: - South Koreans (25-54 years)<br />
:: - South Koreans (55-64 years)<br />
:: - South Koreans (65 years and over)<br />
<br />
:: - South Korea listed companies<br />
:: - Foregin investors<br />
<br />
:: '''Parameters'''<br />
:: - North Korea GDP<br />
:: - North Korea GDP growth<br />
<br />
:: - South Korea GDP<br />
:: - South Korea GDP growth<br />
<br />
:: - North Korea area [sq. meters]<br />
:: - South Korea area [sq. meters] <br />
<br />
:: - North Korea unemployment rate<br />
:: - South Korea unemployment rate<br />
:: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br />
<br />
:: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br />
:: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br />
<br />
:: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br />
<br />
:: '''Targets of the simulation:'''<br />
<br />
:: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br />
:: - Determine unemployment in the reunited country<br />
:: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br />
:: - Determine new GDP based on above data (foreign investors being random)<br />
<br />
:: '''Data sources:'''<br />
<br />
:: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br />
<br />
:: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br />
<br />
:: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br />
<br />
:: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br />
<br />
== Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br />
<br />
Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).<br/><br />
- Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).<br/><br />
- Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.<br/><br />
- Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.<br/><br />
<br />
'''Simulation environment:''' Netlogo<br />
----<br />
<br />
== Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br />
<br />
Topic/goal: '''Maze runner vs fire'''<br />
<br />
'''Definition of the problem:'''<br/><br />
- There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br />
<br />
'''Simulation environment''': Netlogo<br />
<br />
----<br />
<br />
==Simulation proposal [[User:qnesa01|qnesa01]] ''(Not approved yet)'' ==<br />
<br />
Topic/goal: '''Traffic simulation Argentinska street'''<br />
<br />
'''Situation:'''<br/><br />
- Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br />
<br />
'''Definition of the problem:'''<br/><br />
- There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br />
<br />
<br />
'''Purpose of simulation:'''<br/><br />
In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br />
<br />
<br />
'''Simulation environment''': Simprocess; SUMO (for traffic representation) <br />
<br />
'''Brief process of simulation :'''<br/><br />
1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br />
2) Real situation simulation <br />
3) Based on simulation of real situation will be checked the efficiency of lights changes <br />
4) The hypothetical model of adding one more line will be created based on simulation of real situation <br />
5) Summary<br />
<br />
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== Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br />
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Topic/goal: '''Survival of the apocalypse'''<br />
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'''Definition of the problem:'''<br/><br />
- This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. <br/><br />
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The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br />
for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br />
the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br />
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Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br />
effectivnes of the plague.<br />
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'''Simulation environment''': Netlogo<br />
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== Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br />
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Topic/goal: '''Medieval Battle Simulation'''<br />
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'''Definition of the problem:'''<br/><br />
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Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.<br/><br />
Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).<br/><br />
Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.<br/><br />
When they meet in battle, they fight to the last man who wins the dispute for his king.</br><br />
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'''Purpose of simulation:'''<br/><br />
It simulates medieval combat on battlefield between two sides.<br/><br />
The user can select which type of units and how many will each kingdom have.<br/><br />
Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br />
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'''Simulation environment:''' NetLogo<br />
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== Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br />
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Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br />
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'''Definition of the problem:'''<br/><br />
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At the moment we have a venue in a small town under construction which we would like to turn into a café.<br/> <br />
The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. <br/><br />
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'''Purpose of simulation:'''<br/><br />
As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.<br/> <br />
I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.<br/><br />
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The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br />
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'''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br />
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== Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br />
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Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br />
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'''Definition of the problem:'''<br/><br />
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There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. <br />
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'''Purpose of simulation:'''<br/><br />
Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br />
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'''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.</div>Manj01