Simulace.info - User contributions [en]

Savingforanapartment

2023-01-23T01:04:47Z

Miln02:

Introduction

=Problem definition=

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has 100.000 USD that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount of 10.000 USD at the beginning of every year. He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase USA Treasury notes 5Y

3. Invest in NASDAQ.

4. Invest in Tesla.

=Method=

The problem was solved using Monte Carlo simulation and Excel as an calculation environment. MC was chosen because it's widely used and accepted for solving similar investment problems.

=Model=

The simulation is based on historic data found on Yahoo Finance. The behaviour of four given options is projected for the next 15 years using the historic data, while economic crisis effects are included with ability to set up the probability of such event.

== Input parameters ==

* Initial investment - Amount of USD invested at the beginning of first year.

* Annual investment - Amount of USD invested at the beginning of every following year.

* Crisis probability - Probability that crisis will happen as percentage (no matter if we are simulating by months or years).

* Bank annual interest rate - For bank deposit scenario, in %.

* Standard deviations and means - For volatile scenarios for standard times.

* Standard deviations and means - For volatile scenarios for crisis times.

== Scenarios ==

'''Bank Deposit'''

This is the simplest scenario. Jon is putting his money in the bank savings account. The annual interest rate is fixed and he will receive known amount of money after 15 years. No randomness in included, so no MC was used for this scenario.

'''Treasury notes'''

USA government treasury notes for 5 years are bit more risker for Jon, but they can be more profitable than simple deposits and their payment is guaranteed by the USA government.
For the calculations of mean and standard deviation, the simulation uses historic yield data starting from 2010. For the crisis, mean and standard deviation are calculated from only historic yields that are below 1%.
Since the attribute of the bonds that interest is received annually and the full worth of bond is paid after 5 years, the total value of the portfolio is calculated as the total invested amount after 15 years. The simulations assumes that all earned interest rates and repaid mature bonds are reinvested immediately.
Portfolio is simulated for the 1000 times.

'''NASDAQ'''

The Nasdaq Composite Index covers more than 3,000 stocks, which makes them more riskier than government bonds, but still less volatile than investment in the individual stock.
For the calculations of mean and standard deviation, the simulation uses historic yield data starting from 2010. For the crisis, mean and standard deviation are calculated from only historic data where value is lower than Mean-2*Standard Deviation from whole dataset values.
The total portfolio value is calculated as sum of invested amount and gains. The simulations assumes that all gains are reinvested immediately.
Portfolio is simulated for the 1000 times.

'''TESLA'''

Tesla is multinational automotive company which has been popular investing option in the recent past. As this scenario assumes investment in only one stock, it's the most volatile one.
For the calculations of mean and standard deviation, the simulation uses historic yield data starting from 2010. For the crisis, mean and standard deviation are calculated from only historic data where value is lower than Mean-2*Standard Deviation from whole dataset values. As there was only one month like this, the mean is that value and std dev is set as 0.1%.
The total portfolio value is calculated as sum of invested amount and gains. The simulations assumes that all gains are reinvested immediately.
Portfolio is simulated for the 1000 times.

== Metrics ==

* End portfolio value in USD: Average, Median, Percentiles 5,25,75,95.
* Total yield in USD - Difference between average end portfolio value and total money that's invested from outside.
* Graph comparing total invested money and average end portfolio value during time.
* Number of crisis periods.

== Working with simulation ==

The first sheet in the excel is Overview page which has three main parts. Short instruction for using the simulation, table for adding input parameters and results overview. Input parameters are set up with the values that I used for my simulation, but Initial investment, Annual investment, Crisis probability, and Bank annual interest rate can be changed here and the simulation will be updated. There is also the option to change the Mean and Standard Deviation for the three volatile portfolios in the data sheets. Those can be changed by directly adding the values, or by changing the dataset and values will be again automatically calculated. The results overview contains main metrics of simulated results for all four scenarios. The sheets colored in red contain calculations, while the sheets in green contain data from Yahoo Finance and calculations of Mean and Std Dev.

=Results=

== Bank Deposit ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Annual interest rate - 1%

'''Portfolio results'''

Value - $267,066

Total yield - $27,066

== Treasury notes ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Crisis probability - 8%

Mean standard scenario - 1.60%

Std dev standard scenario - 0.81%

Mean crisis scenario - 0.66%

Std dev crisis scenario - 0.23%

'''Portfolio results'''

Number of crises years - 3

Average - $308,044

Median - $308,145

Std dev - $17,129

Percentiles 5% - $279,746

Percentiles 25% - $295,877

Percentiles 75% - $319,393

Percentiles 95% - $335,247

Total yield - $68,044

== NASDAQ ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Crisis probability - 8%

Mean standard scenario - 1.19%

Std dev standard scenario - 5%

Mean crisis scenario - -10.26%

Std dev crisis scenario - 2%

'''Portfolio results'''

Number of crises months - 16

Average - $339,269

Median - $288,593

Std dev - $212,507

Percentiles 5% - $119,763

Percentiles 25% - $195,623

Percentiles 75% - $414,463

Percentiles 95% - $740,428

Total yield - $99,269

== TESLA ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Crisis probability - 8%

Mean standard scenario - 4.58%

Std dev standard scenario - 18%

Mean crisis scenario - -36.73%%

Std dev crisis scenario - 0.1%

'''Portfolio results'''

Number of crises months - 18

Average - $1,146,819

Median - $107,767

Std dev - $6,838,684

Percentiles 5% - $13,187

Percentiles 25% - $40,935

Percentiles 75% - $435,656

Percentiles 95% - $3,412,859

Total yield - $906,819

=Conclusion=

The simulation was conducted for four different investment scenarios. Based on the simulation results, Jon should decide in which of the four options he should invest. The results are consistent with what would be expected on the real market. From the point of view of volatility, the most volatile option is investment in one stock, in our case Tesla, followed by investment in the index (NASQAD), government bond (USA 5Y Treasury note), and depositing the money in the bank. Since Jon has to buy an apartment, he probably would eliminate the deposit in the bank, as the gain is not big. The next two options are bonds and index funds, which in this case show similar results, even though index are a bit more volatile but also Jon can expect higher gains. Investment in one stock, Tesla, is potentially the most profitable option, where on average his yield would be around 9x bigger than in the next option (NASQAD), but the issue is that even on median value, he would be around 130.000 USD down on his portfolio - which means there is a 50% chance he won't have enough for an apartment.

Given all the options, investing either in the index or in government bonds seems like the most appropriate decision, depending on Jon's risk appetite.

=Sources=

* https://finance.yahoo.com/quote/TSLA

* https://finance.yahoo.com/quote/%5EFVX

* https://finance.yahoo.com/quote/%5EIXIC

=Code=
[[File:Savingforanapartment.xlsx]]

[[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 23:01, 22 January 2023 (CET)

Savingforanapartment

2023-01-23T00:40:36Z

Miln02:

Introduction

=Problem definition=

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has 100.000 USD that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount of 10.000 USD at the beginning of every year. He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase USA Treasury notes 5Y

3. Invest in NASDAQ.

4. Invest in Tesla.

=Method=

The problem was solved using Monte Carlo simulation and Excel as an calculation environment. MC was chosen because it's widely used and accepted for solving similar investment problems.

=Model=

The simulation is based on historic data found on Yahoo Finance. The behaviour of four given options is projected for the next 15 years using the historic data, while economic crisis effects are included with ability to set up the probability of such event.

== Input parameters ==

* Initial investment - Amount of USD invested at the beginning of first year.

* Annual investment - Amount of USD invested at the beginning of every following year.

* Crisis probability - Probability that crisis will happen as percentage (no matter if we are simulating by months or years).

* Bank annual interest rate - For bank deposit scenario, in %.

* Standard deviations and means - For volatile scenarios for standard times.

* Standard deviations and means - For volatile scenarios for crisis times.

== Scenarios ==

'''Bank Deposit'''

This is the simplest scenario. Jon is putting his money in the bank savings account. The annual interest rate is fixed and he will receive known amount of money after 15 years. No randomness in included, so no MC was used for this scenario.

'''Treasury notes'''

USA government treasury notes for 5 years are bit more risker for Jon, but they can be more profitable than simple deposits and their payment is guaranteed by the USA government.
For the calculations of mean and standard deviation, the simulation uses historic yield data starting from 2010. For the crisis, mean and standard deviation are calculated from only historic yields that are below 1%.
Since the attribute of the bonds that interest is received annually and the full worth of bond is paid after 5 years, the total value of the portfolio is calculated as the total invested amount after 15 years. The simulations assumes that all earned interest rates and repaid mature bonds are reinvested immediately.
Portfolio is simulated for the 1000 times.

'''NASDAQ'''

The Nasdaq Composite Index covers more than 3,000 stocks, which makes them more riskier than government bonds, but still less volatile than investment in the individual stock.
For the calculations of mean and standard deviation, the simulation uses historic yield data starting from 2010. For the crisis, mean and standard deviation are calculated from only historic data where value is lower than Mean-2*Standard Deviation from whole dataset values.
The total portfolio value is calculated as sum of invested amount and gains. The simulations assumes that all gains are reinvested immediately.
Portfolio is simulated for the 1000 times.

'''TESLA'''

Tesla is multinational automotive company which has been popular investing option in the recent past. As this scenario assumes investment in only one stock, it's the most volatile one.
For the calculations of mean and standard deviation, the simulation uses historic yield data starting from 2010. For the crisis, mean and standard deviation are calculated from only historic data where value is lower than Mean-2*Standard Deviation from whole dataset values. As there was only one month like this, the mean is that value and std dev is set as 0.1%.
The total portfolio value is calculated as sum of invested amount and gains. The simulations assumes that all gains are reinvested immediately.
Portfolio is simulated for the 1000 times.

== Metrics ==

* End portfolio value in USD: Average, Median, Percentiles 5,25,75,95.
* Total yield in USD - Difference between total invested money and average end portfolio value.
* Graph comparing total invested money and average end portfolio value during time.
* Number of crisis periods

== Working with simulation ==

The first sheet in the excel is Overview page which has three main parts. Short instruction for using the simulation, table for adding input parameters and results overview. Input parameters are set up with the values that I used for my simulation, but Initial investment, Annual investment, Crisis probability, and Bank annual interest rate can be changed here and the simulation will be updated. There is also the option to change the Mean and Standard Deviation for the three volatile portfolios directly in the data sheets. The results overview contains main metrics of simulated results for all four scenarios. The sheets colored in red contain calculations, while the sheets in green contain data from Yahoo Finance and calculations of Mean and Std Dev. If you change the dataset there, the new mean and std dev will be calculated and simulation will be automatically updated with new results.

=Results=

== Bank Deposit ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Annual interest rate - 1%

'''Portfolio results'''

Value - $267,066

Total yield - $27,066

== Treasury notes ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Crisis probability - 8%

Mean standard scenario - 1.60%

Std dev standard scenario - 0.81%

Mean crisis scenario - 0.66%

Std dev crisis scenario - 0.23%

'''Portfolio results'''

Number of crises years - 3

Average - $308,044

Median - $308,145

Std dev - $17,129

5% - $279,746

25% - $295,877

75% - $319,393

95% - $335,247

Total yield - $68,044

== NASDAQ ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Crisis probability - 8%

Mean standard scenario - 1.19%

Std dev standard scenario - 5%

Mean crisis scenario - -10.26%

Std dev crisis scenario - 2%

'''Portfolio results'''

Number of crises months - 16

Average - $339,269

Median - $288,593

Std dev - $212,507

Percentiles 5% - $119,763

Percentiles 25% - $195,623

Percentiles 75% - $414,463

Percentiles 95% - $740,428

Total yield - $99,269

== TESLA ==
'''Input parameters'''

Initial investment - 100.000USD

Annual investment - 10.000USD

Crisis probability - 8%

Mean standard scenario - 4.58%

Std dev standard scenario - 18%

Mean crisis scenario - -36.73%%

Std dev crisis scenario - 0.1%

'''Portfolio results'''

Number of crises months - 18

Average - $1,146,819

Median - $107,767

Std dev -$6,838,684

Percentiles 5% - $13,187

Percentiles 25% - $40,935

Percentiles 75% - $435,656

Percentiles 95% - $3,412,859

Total yield - $906,819

=Conclusion=

=Sources=

* https://finance.yahoo.com/quote/TSLA

* https://finance.yahoo.com/quote/%5EFVX

* https://finance.yahoo.com/quote/%5EIXIC

=Code=
[[File:Savingforanapartment.xlsx]]

[[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 23:01, 22 January 2023 (CET)

Savingforanapartment

2023-01-22T23:48:55Z

Miln02:

Savingforanapartment

2023-01-22T23:00:14Z

Miln02:

Savingforanapartment

2023-01-22T22:52:29Z

Miln02:

File:Savingforanapartment.xlsx

2023-01-22T22:51:57Z

Miln02: Simulation Saving for an apartment.

== Summary ==
Simulation Saving for an apartment.

WS 2022/2023

2023-01-22T22:06:05Z

Miln02:

Semestral papers from winter term 2022/2023. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2022/2023|assignment approved]]

==Simulations==
--[[User:Julian Bleyer|Julian Bleyer]] ([[User talk:Xkrep33|talk]]) 0:44, 18 January 2023(CET) Aircraft Evacuation Simulation: [[Airplane_Evacuation]]

--[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 13:55, 18 January 2023 (CET) Cartel simulation with leniency program by Rebecca Baumann (baur00): [[cartel_simulation]]

--[[User:Kock06|Kock06]] ([[User talk:Kock06|talk]]) 14:03, 22 January(GTM+8) Receivables prediction by Monte Carlo simulation: [[Receivables]]

--[[User:Ruzv01|Ruzv01]] ([[User talk:Ruzv01|talk]]) 10:33, 22 January(CET) Household electricity consumption: [[Household electricity consumption]]

--[[User:Haki00|Haki00]] ([[User talk:Haki00|talk]]) 21:38, 22 January(CET) Artsakh blockade: [[Artsakh blockade]]

--[[User:Edema|Edema]] ([[User talk:Haki00|talk]]) 22:00, 22 January(CET) Mortgage Assessment: [[Mortgage Assessment]]

--[[User:BortnikSvitlana|BortnikSvitlana]] ([[User talk:BortnikSvitlana|talk]]) 22:36, 22 January(CET) Traffic Simulation at an Intersection: [[Traffic Simulation at an Intersection]]

--[[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 22:48, 22 January 2023 (CET) Pumped hydroelectric energy storage (PHES)System Simulation [[Pump_storage]]

--[[User:Botd00|Botd00]] ([[User talk:Botd00|talk]]) 22:46, 22 January(CET) Twitter Simulation: [[Twitter simulation]]

-- [[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 23:05, 22 January 2023 (CET) Saving for an apartment [[Savingforanapartment]]

Savingforanapartment

2023-01-22T22:01:36Z

Miln02: Created page with "Introduction =Problem definition= Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment,..."

User talk:Miln02

2023-01-07T19:19:41Z

Miln02:

Wikipedia username: Miln02 [[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 20:19, 7 January 2023 (CET)

User talk:Miln02

2023-01-07T19:18:44Z

Miln02:

Wikipedia username: Miln02

User talk:Miln02

2023-01-07T19:18:05Z

Miln02: Created page with "Wikipedia username: miln02"

Wikipedia username: miln02

Assignments WS 2022/2023

2022-12-17T15:20:47Z

Miln02: /* Saving for a car */

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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.
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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]].
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Topics on gambling, cards, etc. are not welcome.
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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.
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== Effect of leniency programs on cartel rates by [[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) ==

''' Simulation '''

The leniency program of the European Commission offers the companies involved in a cartel either complete or partial immunity from fines if they self-report and hand over evidence. It was introduced in 1996, following the surge in amnesty applications in the wake of the 1993 revision of the Corporate Leniency Program of the US Department of Justice’s Antitrust Division. Reports from various implemented leniency programs showed that such programs led to numerous applications. However, despite the clear increase in leniency applications, the question poses itself as to whether the programs were also successful in a sense that the actual cartel rate in those countries declined.
The simulation will be based on a study of Harrington and Chang from 2015, in which they concluded the following:

• The actual cartel rate decreases in case that the leniency program does not affect the non-leniency enforcement

• But: if the non-leniency enforcement is affected because resources are shifted to the prosecution of leniency application cases, there might be two possibilities, the cartel rate might increase.

This simulation focuses on the latter case. Assuming endogenized non-leniency enforcement, the introduction of a leniency program might have a differential impact on different industries. If a leniency program is introduced, the cartels that are about to collapse will seek to self-report. This in turn shifts resources from exposing active cartels to prosecuting cartels that are already collapsing. This creates more work for the authorities, who, instead of focusing on active cartels may now focus on dying cartels. This crowding-out effect coming about with the introduction of a leniency program shall be simulated in this project.

''' Goal '''

The simulation will have the following objectives:

* Illustrate the change in cartel rates and the change in the average life expectancy of a cartel triggered by the introduction of a leniency program in case of endogenized non-leniency enforcement for industries with unstable cartels (e.g. industries with a high number of competitors, or demand with more price elasticity) and for industries with stable cartels (e.g. industries with less competitors and demand with less price elasticity).
* Illustrate how many resources may be shifted from non-leniency enforcement to prosecuting leniency application cases without it having an undesired effect on the actual cartel rate.

''' Practical relevance '''

The simulation may be used by law enforcement officials to evaluate whether a leniency program leads to the desired effect (i.e. the decrease in the cartel rate) or not. Also, it can help for deciding whether the non-leniency enforcement needs to be strengthened to prevent the crowding-out effect.

''' Method '''

The described scenario is a multi-agent simulation in which the agents are pursuing a utility-based approach. Thus, the simulation will be done with NetLogo.
The following features will be included into the simulation:

- For both industries with stable and industries with unstable cartels:

* Number of active cartels (dying after reaching avg. life expectancy)
* Number of competitors
* Average life expectancy of a cartel
* “Birth” of new cartels

- For leniency/non-leniency enforcement:
* Resources and their assignment to either leniency or non-leniency enforcement
* Capacity of taking down an active cartel
* Capacity of taking down a cartel based on leniency applications

The simulation will be based on the 2015 research from Harrington and Chang as well as on publicly accessible data from the European Commission regarding antitrust cases from 1964 until today.

''' Sources '''
* Harrington Jr, J. E., & Chang, M. H. (2015). When can we expect a corporate leniency program to result in fewer cartels?. The Journal of Law and Economics, 58(2), 417-449.
* Ordóñez‐De‐Haro, J. M., Borrell, J. R., & Jiménez, J. L. (2018). The European commission's fight against cartels (1962–2014): A retrospective and forensic analysis. JCMS: Journal of Common Market Studies, 56(5), 1087-1107.

[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

: This isn't an easy topic. Be careful about available data. '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:46, 15 December 2022 (CET)

== The prediction of divorce rate in Czech Republic for the following 50 years ==

''' The goal of the simulation '''

Divorce in the Czech republic must always contain at least one hearing in front of the court. Legally, there are many more parties involved, such as a notary, who must verify the signatures on all the important documents and many times, divorce lawyers are also necessary. To be able to satisfy the needs of the public, all the involved parties must have an idea about how many married couples are likely to get divorced in the years to come. This simulation will help prepare the courts, notaries and lawyers by making a prediction on the amount of divorces in the next 50 years. This will also help law students choose the field of law that they will specialize in by answering the question whether divorce lawyers will be necessary in the future or not.

''' Method '''

Vensim will be used for this simulation. The used data will come from the Czech Statistical Office and possibly other sources (Refer to [1] and [2]), such as published studies on the most common reasons for divorce. When possible, the data about each reason of divorce will be also found and the simulation model will contain this data.

== Edit: additional details ==

''' What all parameters will the simulation work with and how?'''

1. Number of marriages – the more marriages, the more divorces

a/ Number of people in the age 25 to 34 (i.e., the most common age to get married) – the more there is of these people, the more marriages there will be

b/ Number of divorced people in the age 40 to 49 (i.e., the most common age to get re-married after a divorce) – the more there is of these people, the more marriages there will be, however not as much as the number above

2. Micro causes of divorces = Top 10 causes of divorce as researched by the Czech Statistical Office, published yearly – the more common are these causes (alcoholism, infidelity etc), the more divorces there will be

a/ Ill-considered marriage

b/ Alcoholism

c/ Infidelity

d/ Lack of interest in the family (incl. abandon. of living together)

e/ Ill-treatment, criminal conviction

f/ Different characters, views and interests

g/ Health reasons

h/ Sexual discord

i/ Other causes

j/ Cause not given

3. Number of people in the age 40 to 49 – the more there is of these people, the more divorces there will be (it is the most common age to get divorced)

4. Macro causes of divorces

a/ Economic independence of women = the more economically independent women are, the more likely they are to divorce in case of an unhappy marriage – this will be evaluated through a comparison of data of average income of men vs. women

b/ Being religious – divorce is far less common for religious people.

''' What data source will be used for deriving the equations?'''

Based on my current research of data sources, the Czech Statistical Office has the all the data necessary for this paper.

[1] Scott, S. B., Rhoades, G. K., Stanley, S. M., Allen, E. S., & Markman, H. J. (2013). Reasons for Divorce and Recollections of Premarital Intervention: Implications for Improving Relationship Education. Couple & family psychology, 2(2), 131–145. https://doi.org/10.1037/a0032025

[2] Hawkins, Alan & Willoughby, Brian & Doherty, William. (2012). Reasons for Divorce and Openness to Marital Reconciliation. Journal of Divorce & Remarriage. 53. 453-463. 10.1080/10502556.2012.682898.

: Sounds interesting, but I miss more detail about the simulation. What all parameters will the simulation work with and how? What data source will be used for deriving the equations? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 11:03, 15 December 2022 (CET)
:: ''' Approved'''. Just make sure that the equtions, reasons for divorce and their impact on divorce rate are properly quantified.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:23, 17 December 2022 (CET)

==Crop Yield Forecasting==

''' Simulation '''

Crop growth and development simulations and yield forecasting will be performed using variables such as crop type, planting date, soil type, soil texture, and climate data (temperature, rainfall, etc.).

'''Problem definition'''

Arable land is increasingly limited, while the world's population has steadily been increasing over the years. In order to meet rapidly rising demand, production must be increased while natural resources must be protected. New agricultural research is needed to provide information on how to achieve sustainable agriculture in the face of global climate variability. Predicting crop yield under different conditions, such as different irrigation regimes, planting dates, and crop management practices, has become critical for farmers and other stakeholders who use these predictions to make more informed decisions about how to allocate resources, such as labor, equipment, and inputs, to maximize yield and productivity.

'''Method'''

Crop yield simulation tools include AquaCrop, DSSAT, and CropSyst. These tools use mathematical models to simulate crop growth and development based on input data like weather, soil type, and management practices. These tools use this data to estimate the crop's potential yield, as well as other important factors like water use and crop evapotranspiration. For this assignment I will be using AquaCrop which is a crop water productivity model developed by the United Nations Food and Agriculture Organization (FAO). It is used to simulate crop growth and yield under various environmental and management conditions. AquaCrop simulates crop growth and development, and estimates yield based on soil conditions, climate, irrigation, and management practices. The application gives access to various FAO databases with all the necessary data needed to perform a comprehensive simulation of the crop yield.

'''Citations'''

* Y. Lu, C. Wei, M. F. McCabe, and J. Sheffield, “Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information,” Agricultural Water Management, vol. 266, p. 107576, May 2022, doi: 10.1016/j.agwat.2022.107576.
* P. N. Kephe, K. K. Ayisi, and B. M. Petja, “Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa,” Agriculture & Food Security, vol. 10, no. 1, p. 10, Apr. 2021, doi: 10.1186/s40066-020-00283-5.
* N. T. Olivera, O. B. Manrique, Y. G. Masjuan, and A. M. G. Alega, “Evaluation of AquaCrop model in crop dry bean growth simulation,” Revista Ciencias Técnicas Agropecuarias, vol. 25, no. 3, pp. 23–30, Accessed: Dec. 10, 2022. [Online]. Available: https://www.redalyc.org/journal/932/93246970003/html/
* N. Pirmoradian, Z. Saadati, M. Rezaei, and M. R. Khaledian, “Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran,” Appl Water Sci, vol. 10, no. 7, p. 161, Jun. 2020, doi: 10.1007/s13201-020-01249-5.

[[User:Pierreatekwana|Pierreatekwana]] ([[User talk:Pierreatekwana|talk]]) 15:06, 15 December 2022 (CET)

: Topic souds interesting, but the proposed simulation tool has to be one of the ones we have used in our class ( as specified in How to deal with the simulation assignment:
One of your key course requirements is a submission of simulation. You choose your topic yourself, the same as a method and a tool that you will use. It could be any of the development environments we have used (Excel, Simprocess, Netlogo, or Vensim).) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:05, 17 December 2022 (CET)

== Electricity Spot Market Simulation by [[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 01:13, 16 December 2022 (CET) ==

'''Simulation'''

I’ve been working as a professional in the Turkish Electricity Market (EXIST) for more than 10 years. I had the chance to practice in business some fundamental methods such as Stochastic Dual Dynamic Programming (SDDP) for Hydro-Thermal dispatch optimization. The spot exchange of power or the power market itself has many examples of simulation. And the simulation of the uncertainties both in the short term and the long term are very critical in terms of new investments, portfolio management, and resource optimization.

'''Method'''

Both Excel and Netlogo simulations can be derived

'''• Inputs
'''

- Portfolios

(Thermals: Coal-Fired, Natural Gas-Fired, Other Thermals; Renewables: Hydro, Wind, Solar PV, Other Renewables)

- Resource Prices (Eur/mwh)

- Installed Capacity (in MW)

'''• Variables'''

- Power Demand (MWh)

- Calendar Day

'''• Uncertain Events
'''
- Maintenance (availability)

- Climate Factors (Wind, Hydro Sources, Temperature effect on Load-Demand)

- Natural Gas Shortage (Due to Climate Factors and/or Supply)

'''• Outputs
'''
- Marginal Cost

- Market Clearing Prices (MCP)

•''' Goals
'''
- Objective Function Y= Min (MCP)

- Effects of Natural Gas Shortages

- Effects of Climate Factors

- Effects of Maintenance

- Effects of Calendar

- Sensitivity Analysis of commissioning a Nuclear Power Plant in the market

'''Citations'''

- Sensfuß, Frank; Ragwitz, Mario; Genoese, Massimo; Möst, Dominik (2007) : Agent-based simulation of electricity markets: a literature review, Working Paper Sustainability and Innovation, No. S5/2007, Fraunhofer-Institut für System- und Innovationsforschung ISI, Karlsruhe,

- https://nbn-resolving.de/urn:nbn:de:0011-n-661574

- https://www.econstor.eu/bitstream/10419/28520/1/570113083.pdf

- https://www.tudelft.nl/evenementen/2021/powerweb/electricity-market-simulation-game ==

''' Data '''

- EXIST Transparency Portal https://seffaflik.epias.com.tr/transparency/

- Electricity Transmission Operator https://www.teias.gov.tr/en-US

== Profit in store vs e-shop ==

''' Method:''' System Dynamics

'''Software:''' Vensim

'''Simulation'''

An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc.

'''Model parameters'''

*Expenses
**fixed
**variable
*Revenues
**customer satisfaction
**price
**a number of sales, etc.

''' The goal of the simulation '''

The goal of this simulation is to find out how the company's profits will develop in the next 5 years, individually in the store and e-shop. Find out what parameters can increase profits the most, individually for each type of store, and compare these parameters.

'''Data'''

Real data provided by the owners of the store

[[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 01:41, 16 December 2022 (CET)
:Please elaborate in more detail as we have discussed in class [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:17, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==Comparison of strategies for finding a lost person in the forest==

''' Author:''' Tomáš Kadaně (kadt02)

'''Type:''' Multi-agent

'''Software:''' NetLogo

'''Description:'''

The simulation will focus on comparing the times needed to find a lost person in a forest (area with trees).
The metric to compare the strategies will be the number of ticks needed to find the wanted person. Both the person being searched for and the searcher will be in a random location at the beginning of the simulation.
Within the simulation, I will take several measurements for each strategy and number of searchers (1 to 5), so that the number is statistically significant and use, for example, the means to compare which strategy is the most appropriate.

The model will be able to simulate several search strategies

*one step forward and then turn of random degree (-45 to 45 degrees), so random walk
*walk straight until it hits the edge of the forest or tree, then turn and continue walking straight
*first walk to the nearest corner of the forest and then a some kind of serpentine search
*possibly other strategies

'''Goals:'''

Finding the most appropriate strategy for finding a person in the forest depending on the number of people searching.

'''Agents:'''

*Searchers (e.g. police officers)
*Lost person

'''Parameters:'''

*Number of searchers
*Type of strategy
*Ticks needed to find person

'''Possible extensions:'''

*Searchers with certain pace of walking
*Finding the person won’t mean be at same location but seeing it for some distance (again certain ability of the searcher to see for certain distance)
*Cooperation of finders (formations, place distribution)
*Lost person will be moving when being looked for

[[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 16:01, 17 December 2022 (CET)

==Saving for an apartment==

Author: miln02

'''Problem definition'''

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has some money that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount every year.
He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase government bonds.

3. Invest in one of the world indices.

4. Invest all the money in one stock.

'''Goal'''

Create simulation that can be used as a support when making investment decision.

'''Method'''

For helping Jon to make a decision, I will use Monte Carlo simulation and Excel as an environment. The historical yield and volatility data will be used to calculate the average behaviour of all 4 options, and we will simulate possible results after 15 years. Since it can’t be expected that the market will be stable for all 15 years, economic crises will be generated.

'''Model parameters'''

* Investments:

-Initial one-time investment

-Fixed annual investment

* Market data:

-Deposits (Rate)

-Government bonds (Yield, volatility)

-Index (Yield, volatility)

-Stock (Yield, volatility)

* Economic crises probability

'''Sources'''

* https://finance.yahoo.com/

* http://www.worldgovernmentbonds.com/

* Bank website for deposit rates

[[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 16:17, 17 December 2022 (CET)

Assignments WS 2022/2023

2022-12-17T15:20:12Z

Miln02:

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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.
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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]].
</div>
}}

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Topics on gambling, cards, etc. are not welcome.
</div>
}}

{{Ambox
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| text = <div>
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.
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== Effect of leniency programs on cartel rates by [[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) ==

''' Simulation '''

The leniency program of the European Commission offers the companies involved in a cartel either complete or partial immunity from fines if they self-report and hand over evidence. It was introduced in 1996, following the surge in amnesty applications in the wake of the 1993 revision of the Corporate Leniency Program of the US Department of Justice’s Antitrust Division. Reports from various implemented leniency programs showed that such programs led to numerous applications. However, despite the clear increase in leniency applications, the question poses itself as to whether the programs were also successful in a sense that the actual cartel rate in those countries declined.
The simulation will be based on a study of Harrington and Chang from 2015, in which they concluded the following:

• The actual cartel rate decreases in case that the leniency program does not affect the non-leniency enforcement

• But: if the non-leniency enforcement is affected because resources are shifted to the prosecution of leniency application cases, there might be two possibilities, the cartel rate might increase.

This simulation focuses on the latter case. Assuming endogenized non-leniency enforcement, the introduction of a leniency program might have a differential impact on different industries. If a leniency program is introduced, the cartels that are about to collapse will seek to self-report. This in turn shifts resources from exposing active cartels to prosecuting cartels that are already collapsing. This creates more work for the authorities, who, instead of focusing on active cartels may now focus on dying cartels. This crowding-out effect coming about with the introduction of a leniency program shall be simulated in this project.

''' Goal '''

The simulation will have the following objectives:

* Illustrate the change in cartel rates and the change in the average life expectancy of a cartel triggered by the introduction of a leniency program in case of endogenized non-leniency enforcement for industries with unstable cartels (e.g. industries with a high number of competitors, or demand with more price elasticity) and for industries with stable cartels (e.g. industries with less competitors and demand with less price elasticity).
* Illustrate how many resources may be shifted from non-leniency enforcement to prosecuting leniency application cases without it having an undesired effect on the actual cartel rate.

''' Practical relevance '''

The simulation may be used by law enforcement officials to evaluate whether a leniency program leads to the desired effect (i.e. the decrease in the cartel rate) or not. Also, it can help for deciding whether the non-leniency enforcement needs to be strengthened to prevent the crowding-out effect.

''' Method '''

The described scenario is a multi-agent simulation in which the agents are pursuing a utility-based approach. Thus, the simulation will be done with NetLogo.
The following features will be included into the simulation:

- For both industries with stable and industries with unstable cartels:

* Number of active cartels (dying after reaching avg. life expectancy)
* Number of competitors
* Average life expectancy of a cartel
* “Birth” of new cartels

- For leniency/non-leniency enforcement:
* Resources and their assignment to either leniency or non-leniency enforcement
* Capacity of taking down an active cartel
* Capacity of taking down a cartel based on leniency applications

The simulation will be based on the 2015 research from Harrington and Chang as well as on publicly accessible data from the European Commission regarding antitrust cases from 1964 until today.

''' Sources '''
* Harrington Jr, J. E., & Chang, M. H. (2015). When can we expect a corporate leniency program to result in fewer cartels?. The Journal of Law and Economics, 58(2), 417-449.
* Ordóñez‐De‐Haro, J. M., Borrell, J. R., & Jiménez, J. L. (2018). The European commission's fight against cartels (1962–2014): A retrospective and forensic analysis. JCMS: Journal of Common Market Studies, 56(5), 1087-1107.

[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

: This isn't an easy topic. Be careful about available data. '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:46, 15 December 2022 (CET)

== The prediction of divorce rate in Czech Republic for the following 50 years ==

''' The goal of the simulation '''

Divorce in the Czech republic must always contain at least one hearing in front of the court. Legally, there are many more parties involved, such as a notary, who must verify the signatures on all the important documents and many times, divorce lawyers are also necessary. To be able to satisfy the needs of the public, all the involved parties must have an idea about how many married couples are likely to get divorced in the years to come. This simulation will help prepare the courts, notaries and lawyers by making a prediction on the amount of divorces in the next 50 years. This will also help law students choose the field of law that they will specialize in by answering the question whether divorce lawyers will be necessary in the future or not.

''' Method '''

Vensim will be used for this simulation. The used data will come from the Czech Statistical Office and possibly other sources (Refer to [1] and [2]), such as published studies on the most common reasons for divorce. When possible, the data about each reason of divorce will be also found and the simulation model will contain this data.

== Edit: additional details ==

''' What all parameters will the simulation work with and how?'''

1. Number of marriages – the more marriages, the more divorces

a/ Number of people in the age 25 to 34 (i.e., the most common age to get married) – the more there is of these people, the more marriages there will be

b/ Number of divorced people in the age 40 to 49 (i.e., the most common age to get re-married after a divorce) – the more there is of these people, the more marriages there will be, however not as much as the number above

2. Micro causes of divorces = Top 10 causes of divorce as researched by the Czech Statistical Office, published yearly – the more common are these causes (alcoholism, infidelity etc), the more divorces there will be

a/ Ill-considered marriage

b/ Alcoholism

c/ Infidelity

d/ Lack of interest in the family (incl. abandon. of living together)

e/ Ill-treatment, criminal conviction

f/ Different characters, views and interests

g/ Health reasons

h/ Sexual discord

i/ Other causes

j/ Cause not given

3. Number of people in the age 40 to 49 – the more there is of these people, the more divorces there will be (it is the most common age to get divorced)

4. Macro causes of divorces

a/ Economic independence of women = the more economically independent women are, the more likely they are to divorce in case of an unhappy marriage – this will be evaluated through a comparison of data of average income of men vs. women

b/ Being religious – divorce is far less common for religious people.

''' What data source will be used for deriving the equations?'''

Based on my current research of data sources, the Czech Statistical Office has the all the data necessary for this paper.

[1] Scott, S. B., Rhoades, G. K., Stanley, S. M., Allen, E. S., & Markman, H. J. (2013). Reasons for Divorce and Recollections of Premarital Intervention: Implications for Improving Relationship Education. Couple & family psychology, 2(2), 131–145. https://doi.org/10.1037/a0032025

[2] Hawkins, Alan & Willoughby, Brian & Doherty, William. (2012). Reasons for Divorce and Openness to Marital Reconciliation. Journal of Divorce & Remarriage. 53. 453-463. 10.1080/10502556.2012.682898.

: Sounds interesting, but I miss more detail about the simulation. What all parameters will the simulation work with and how? What data source will be used for deriving the equations? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 11:03, 15 December 2022 (CET)
:: ''' Approved'''. Just make sure that the equtions, reasons for divorce and their impact on divorce rate are properly quantified.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:23, 17 December 2022 (CET)

==Crop Yield Forecasting==

''' Simulation '''

Crop growth and development simulations and yield forecasting will be performed using variables such as crop type, planting date, soil type, soil texture, and climate data (temperature, rainfall, etc.).

'''Problem definition'''

Arable land is increasingly limited, while the world's population has steadily been increasing over the years. In order to meet rapidly rising demand, production must be increased while natural resources must be protected. New agricultural research is needed to provide information on how to achieve sustainable agriculture in the face of global climate variability. Predicting crop yield under different conditions, such as different irrigation regimes, planting dates, and crop management practices, has become critical for farmers and other stakeholders who use these predictions to make more informed decisions about how to allocate resources, such as labor, equipment, and inputs, to maximize yield and productivity.

'''Method'''

Crop yield simulation tools include AquaCrop, DSSAT, and CropSyst. These tools use mathematical models to simulate crop growth and development based on input data like weather, soil type, and management practices. These tools use this data to estimate the crop's potential yield, as well as other important factors like water use and crop evapotranspiration. For this assignment I will be using AquaCrop which is a crop water productivity model developed by the United Nations Food and Agriculture Organization (FAO). It is used to simulate crop growth and yield under various environmental and management conditions. AquaCrop simulates crop growth and development, and estimates yield based on soil conditions, climate, irrigation, and management practices. The application gives access to various FAO databases with all the necessary data needed to perform a comprehensive simulation of the crop yield.

'''Citations'''

* Y. Lu, C. Wei, M. F. McCabe, and J. Sheffield, “Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information,” Agricultural Water Management, vol. 266, p. 107576, May 2022, doi: 10.1016/j.agwat.2022.107576.
* P. N. Kephe, K. K. Ayisi, and B. M. Petja, “Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa,” Agriculture & Food Security, vol. 10, no. 1, p. 10, Apr. 2021, doi: 10.1186/s40066-020-00283-5.
* N. T. Olivera, O. B. Manrique, Y. G. Masjuan, and A. M. G. Alega, “Evaluation of AquaCrop model in crop dry bean growth simulation,” Revista Ciencias Técnicas Agropecuarias, vol. 25, no. 3, pp. 23–30, Accessed: Dec. 10, 2022. [Online]. Available: https://www.redalyc.org/journal/932/93246970003/html/
* N. Pirmoradian, Z. Saadati, M. Rezaei, and M. R. Khaledian, “Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran,” Appl Water Sci, vol. 10, no. 7, p. 161, Jun. 2020, doi: 10.1007/s13201-020-01249-5.

[[User:Pierreatekwana|Pierreatekwana]] ([[User talk:Pierreatekwana|talk]]) 15:06, 15 December 2022 (CET)

: Topic souds interesting, but the proposed simulation tool has to be one of the ones we have used in our class ( as specified in How to deal with the simulation assignment:
One of your key course requirements is a submission of simulation. You choose your topic yourself, the same as a method and a tool that you will use. It could be any of the development environments we have used (Excel, Simprocess, Netlogo, or Vensim).) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:05, 17 December 2022 (CET)

== Electricity Spot Market Simulation by [[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 01:13, 16 December 2022 (CET) ==

'''Simulation'''

I’ve been working as a professional in the Turkish Electricity Market (EXIST) for more than 10 years. I had the chance to practice in business some fundamental methods such as Stochastic Dual Dynamic Programming (SDDP) for Hydro-Thermal dispatch optimization. The spot exchange of power or the power market itself has many examples of simulation. And the simulation of the uncertainties both in the short term and the long term are very critical in terms of new investments, portfolio management, and resource optimization.

'''Method'''

Both Excel and Netlogo simulations can be derived

'''• Inputs
'''

- Portfolios

(Thermals: Coal-Fired, Natural Gas-Fired, Other Thermals; Renewables: Hydro, Wind, Solar PV, Other Renewables)

- Resource Prices (Eur/mwh)

- Installed Capacity (in MW)

'''• Variables'''

- Power Demand (MWh)

- Calendar Day

'''• Uncertain Events
'''
- Maintenance (availability)

- Climate Factors (Wind, Hydro Sources, Temperature effect on Load-Demand)

- Natural Gas Shortage (Due to Climate Factors and/or Supply)

'''• Outputs
'''
- Marginal Cost

- Market Clearing Prices (MCP)

•''' Goals
'''
- Objective Function Y= Min (MCP)

- Effects of Natural Gas Shortages

- Effects of Climate Factors

- Effects of Maintenance

- Effects of Calendar

- Sensitivity Analysis of commissioning a Nuclear Power Plant in the market

'''Citations'''

- Sensfuß, Frank; Ragwitz, Mario; Genoese, Massimo; Möst, Dominik (2007) : Agent-based simulation of electricity markets: a literature review, Working Paper Sustainability and Innovation, No. S5/2007, Fraunhofer-Institut für System- und Innovationsforschung ISI, Karlsruhe,

- https://nbn-resolving.de/urn:nbn:de:0011-n-661574

- https://www.econstor.eu/bitstream/10419/28520/1/570113083.pdf

- https://www.tudelft.nl/evenementen/2021/powerweb/electricity-market-simulation-game ==

''' Data '''

- EXIST Transparency Portal https://seffaflik.epias.com.tr/transparency/

- Electricity Transmission Operator https://www.teias.gov.tr/en-US

== Profit in store vs e-shop ==

''' Method:''' System Dynamics

'''Software:''' Vensim

'''Simulation'''

An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc.

'''Model parameters'''

*Expenses
**fixed
**variable
*Revenues
**customer satisfaction
**price
**a number of sales, etc.

''' The goal of the simulation '''

The goal of this simulation is to find out how the company's profits will develop in the next 5 years, individually in the store and e-shop. Find out what parameters can increase profits the most, individually for each type of store, and compare these parameters.

'''Data'''

Real data provided by the owners of the store

[[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 01:41, 16 December 2022 (CET)
:Please elaborate in more detail as we have discussed in class [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:17, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==Comparison of strategies for finding a lost person in the forest==

''' Author:''' Tomáš Kadaně (kadt02)

'''Type:''' Multi-agent

'''Software:''' NetLogo

'''Description:'''

The simulation will focus on comparing the times needed to find a lost person in a forest (area with trees).
The metric to compare the strategies will be the number of ticks needed to find the wanted person. Both the person being searched for and the searcher will be in a random location at the beginning of the simulation.
Within the simulation, I will take several measurements for each strategy and number of searchers (1 to 5), so that the number is statistically significant and use, for example, the means to compare which strategy is the most appropriate.

The model will be able to simulate several search strategies

*one step forward and then turn of random degree (-45 to 45 degrees), so random walk
*walk straight until it hits the edge of the forest or tree, then turn and continue walking straight
*first walk to the nearest corner of the forest and then a some kind of serpentine search
*possibly other strategies

'''Goals:'''

Finding the most appropriate strategy for finding a person in the forest depending on the number of people searching.

'''Agents:'''

*Searchers (e.g. police officers)
*Lost person

'''Parameters:'''

*Number of searchers
*Type of strategy
*Ticks needed to find person

'''Possible extensions:'''

*Searchers with certain pace of walking
*Finding the person won’t mean be at same location but seeing it for some distance (again certain ability of the searcher to see for certain distance)
*Cooperation of finders (formations, place distribution)
*Lost person will be moving when being looked for

[[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 16:01, 17 December 2022 (CET)

==Saving for a car==

Author: miln02

'''Problem definition'''

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has some money that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount every year.
He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase government bonds.

3. Invest in one of the world indices.

4. Invest all the money in one stock.

'''Goal'''

Create simulation that can be used as a support when making investment decision.

'''Method'''

For helping Jon to make a decision, I will use Monte Carlo simulation and Excel as an environment. The historical yield and volatility data will be used to calculate the average behaviour of all 4 options, and we will simulate possible results after 15 years. Since it can’t be expected that the market will be stable for all 15 years, economic crises will be generated.

'''Model parameters'''

* Investments:

-Initial one-time investment

-Fixed annual investment

* Market data:

-Deposits (Rate)

-Government bonds (Yield, volatility)

-Index (Yield, volatility)

-Stock (Yield, volatility)

* Economic crises probability

'''Sources'''

* https://finance.yahoo.com/

* http://www.worldgovernmentbonds.com/

* Bank website for deposit rates

[[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 16:17, 17 December 2022 (CET)

Assignments WS 2022/2023

2022-12-17T15:17:18Z

Miln02:

{{Ambox
| text = <div>
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.
</div>
}}

{{Ambox
| text = <div>
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]].
</div>
}}

{{Ambox
| text = <div>
Topics on gambling, cards, etc. are not welcome.
</div>
}}

{{Ambox
| type = content
| text = <div>
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.
</div>
}}

== Effect of leniency programs on cartel rates by [[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) ==

''' Simulation '''

The leniency program of the European Commission offers the companies involved in a cartel either complete or partial immunity from fines if they self-report and hand over evidence. It was introduced in 1996, following the surge in amnesty applications in the wake of the 1993 revision of the Corporate Leniency Program of the US Department of Justice’s Antitrust Division. Reports from various implemented leniency programs showed that such programs led to numerous applications. However, despite the clear increase in leniency applications, the question poses itself as to whether the programs were also successful in a sense that the actual cartel rate in those countries declined.
The simulation will be based on a study of Harrington and Chang from 2015, in which they concluded the following:

• The actual cartel rate decreases in case that the leniency program does not affect the non-leniency enforcement

• But: if the non-leniency enforcement is affected because resources are shifted to the prosecution of leniency application cases, there might be two possibilities, the cartel rate might increase.

This simulation focuses on the latter case. Assuming endogenized non-leniency enforcement, the introduction of a leniency program might have a differential impact on different industries. If a leniency program is introduced, the cartels that are about to collapse will seek to self-report. This in turn shifts resources from exposing active cartels to prosecuting cartels that are already collapsing. This creates more work for the authorities, who, instead of focusing on active cartels may now focus on dying cartels. This crowding-out effect coming about with the introduction of a leniency program shall be simulated in this project.

''' Goal '''

The simulation will have the following objectives:

* Illustrate the change in cartel rates and the change in the average life expectancy of a cartel triggered by the introduction of a leniency program in case of endogenized non-leniency enforcement for industries with unstable cartels (e.g. industries with a high number of competitors, or demand with more price elasticity) and for industries with stable cartels (e.g. industries with less competitors and demand with less price elasticity).
* Illustrate how many resources may be shifted from non-leniency enforcement to prosecuting leniency application cases without it having an undesired effect on the actual cartel rate.

''' Practical relevance '''

The simulation may be used by law enforcement officials to evaluate whether a leniency program leads to the desired effect (i.e. the decrease in the cartel rate) or not. Also, it can help for deciding whether the non-leniency enforcement needs to be strengthened to prevent the crowding-out effect.

''' Method '''

The described scenario is a multi-agent simulation in which the agents are pursuing a utility-based approach. Thus, the simulation will be done with NetLogo.
The following features will be included into the simulation:

- For both industries with stable and industries with unstable cartels:

* Number of active cartels (dying after reaching avg. life expectancy)
* Number of competitors
* Average life expectancy of a cartel
* “Birth” of new cartels

- For leniency/non-leniency enforcement:
* Resources and their assignment to either leniency or non-leniency enforcement
* Capacity of taking down an active cartel
* Capacity of taking down a cartel based on leniency applications

The simulation will be based on the 2015 research from Harrington and Chang as well as on publicly accessible data from the European Commission regarding antitrust cases from 1964 until today.

''' Sources '''
* Harrington Jr, J. E., & Chang, M. H. (2015). When can we expect a corporate leniency program to result in fewer cartels?. The Journal of Law and Economics, 58(2), 417-449.
* Ordóñez‐De‐Haro, J. M., Borrell, J. R., & Jiménez, J. L. (2018). The European commission's fight against cartels (1962–2014): A retrospective and forensic analysis. JCMS: Journal of Common Market Studies, 56(5), 1087-1107.

[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

: This isn't an easy topic. Be careful about available data. '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:46, 15 December 2022 (CET)

== The prediction of divorce rate in Czech Republic for the following 50 years ==

''' The goal of the simulation '''

Divorce in the Czech republic must always contain at least one hearing in front of the court. Legally, there are many more parties involved, such as a notary, who must verify the signatures on all the important documents and many times, divorce lawyers are also necessary. To be able to satisfy the needs of the public, all the involved parties must have an idea about how many married couples are likely to get divorced in the years to come. This simulation will help prepare the courts, notaries and lawyers by making a prediction on the amount of divorces in the next 50 years. This will also help law students choose the field of law that they will specialize in by answering the question whether divorce lawyers will be necessary in the future or not.

''' Method '''

Vensim will be used for this simulation. The used data will come from the Czech Statistical Office and possibly other sources (Refer to [1] and [2]), such as published studies on the most common reasons for divorce. When possible, the data about each reason of divorce will be also found and the simulation model will contain this data.

== Edit: additional details ==

''' What all parameters will the simulation work with and how?'''

1. Number of marriages – the more marriages, the more divorces

a/ Number of people in the age 25 to 34 (i.e., the most common age to get married) – the more there is of these people, the more marriages there will be

b/ Number of divorced people in the age 40 to 49 (i.e., the most common age to get re-married after a divorce) – the more there is of these people, the more marriages there will be, however not as much as the number above

2. Micro causes of divorces = Top 10 causes of divorce as researched by the Czech Statistical Office, published yearly – the more common are these causes (alcoholism, infidelity etc), the more divorces there will be

a/ Ill-considered marriage

b/ Alcoholism

c/ Infidelity

d/ Lack of interest in the family (incl. abandon. of living together)

e/ Ill-treatment, criminal conviction

f/ Different characters, views and interests

g/ Health reasons

h/ Sexual discord

i/ Other causes

j/ Cause not given

3. Number of people in the age 40 to 49 – the more there is of these people, the more divorces there will be (it is the most common age to get divorced)

4. Macro causes of divorces

a/ Economic independence of women = the more economically independent women are, the more likely they are to divorce in case of an unhappy marriage – this will be evaluated through a comparison of data of average income of men vs. women

b/ Being religious – divorce is far less common for religious people.

''' What data source will be used for deriving the equations?'''

Based on my current research of data sources, the Czech Statistical Office has the all the data necessary for this paper.

[1] Scott, S. B., Rhoades, G. K., Stanley, S. M., Allen, E. S., & Markman, H. J. (2013). Reasons for Divorce and Recollections of Premarital Intervention: Implications for Improving Relationship Education. Couple & family psychology, 2(2), 131–145. https://doi.org/10.1037/a0032025

[2] Hawkins, Alan & Willoughby, Brian & Doherty, William. (2012). Reasons for Divorce and Openness to Marital Reconciliation. Journal of Divorce & Remarriage. 53. 453-463. 10.1080/10502556.2012.682898.

: Sounds interesting, but I miss more detail about the simulation. What all parameters will the simulation work with and how? What data source will be used for deriving the equations? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 11:03, 15 December 2022 (CET)
:: ''' Approved'''. Just make sure that the equtions, reasons for divorce and their impact on divorce rate are properly quantified.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:23, 17 December 2022 (CET)

==Crop Yield Forecasting==

''' Simulation '''

Crop growth and development simulations and yield forecasting will be performed using variables such as crop type, planting date, soil type, soil texture, and climate data (temperature, rainfall, etc.).

'''Problem definition'''

Arable land is increasingly limited, while the world's population has steadily been increasing over the years. In order to meet rapidly rising demand, production must be increased while natural resources must be protected. New agricultural research is needed to provide information on how to achieve sustainable agriculture in the face of global climate variability. Predicting crop yield under different conditions, such as different irrigation regimes, planting dates, and crop management practices, has become critical for farmers and other stakeholders who use these predictions to make more informed decisions about how to allocate resources, such as labor, equipment, and inputs, to maximize yield and productivity.

'''Method'''

Crop yield simulation tools include AquaCrop, DSSAT, and CropSyst. These tools use mathematical models to simulate crop growth and development based on input data like weather, soil type, and management practices. These tools use this data to estimate the crop's potential yield, as well as other important factors like water use and crop evapotranspiration. For this assignment I will be using AquaCrop which is a crop water productivity model developed by the United Nations Food and Agriculture Organization (FAO). It is used to simulate crop growth and yield under various environmental and management conditions. AquaCrop simulates crop growth and development, and estimates yield based on soil conditions, climate, irrigation, and management practices. The application gives access to various FAO databases with all the necessary data needed to perform a comprehensive simulation of the crop yield.

'''Citations'''

* Y. Lu, C. Wei, M. F. McCabe, and J. Sheffield, “Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information,” Agricultural Water Management, vol. 266, p. 107576, May 2022, doi: 10.1016/j.agwat.2022.107576.
* P. N. Kephe, K. K. Ayisi, and B. M. Petja, “Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa,” Agriculture & Food Security, vol. 10, no. 1, p. 10, Apr. 2021, doi: 10.1186/s40066-020-00283-5.
* N. T. Olivera, O. B. Manrique, Y. G. Masjuan, and A. M. G. Alega, “Evaluation of AquaCrop model in crop dry bean growth simulation,” Revista Ciencias Técnicas Agropecuarias, vol. 25, no. 3, pp. 23–30, Accessed: Dec. 10, 2022. [Online]. Available: https://www.redalyc.org/journal/932/93246970003/html/
* N. Pirmoradian, Z. Saadati, M. Rezaei, and M. R. Khaledian, “Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran,” Appl Water Sci, vol. 10, no. 7, p. 161, Jun. 2020, doi: 10.1007/s13201-020-01249-5.

[[User:Pierreatekwana|Pierreatekwana]] ([[User talk:Pierreatekwana|talk]]) 15:06, 15 December 2022 (CET)

: Topic souds interesting, but the proposed simulation tool has to be one of the ones we have used in our class ( as specified in How to deal with the simulation assignment:
One of your key course requirements is a submission of simulation. You choose your topic yourself, the same as a method and a tool that you will use. It could be any of the development environments we have used (Excel, Simprocess, Netlogo, or Vensim).) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:05, 17 December 2022 (CET)

== Electricity Spot Market Simulation by [[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 01:13, 16 December 2022 (CET) ==

'''Simulation'''

I’ve been working as a professional in the Turkish Electricity Market (EXIST) for more than 10 years. I had the chance to practice in business some fundamental methods such as Stochastic Dual Dynamic Programming (SDDP) for Hydro-Thermal dispatch optimization. The spot exchange of power or the power market itself has many examples of simulation. And the simulation of the uncertainties both in the short term and the long term are very critical in terms of new investments, portfolio management, and resource optimization.

'''Method'''

Both Excel and Netlogo simulations can be derived

'''• Inputs
'''

- Portfolios

(Thermals: Coal-Fired, Natural Gas-Fired, Other Thermals; Renewables: Hydro, Wind, Solar PV, Other Renewables)

- Resource Prices (Eur/mwh)

- Installed Capacity (in MW)

'''• Variables'''

- Power Demand (MWh)

- Calendar Day

'''• Uncertain Events
'''
- Maintenance (availability)

- Climate Factors (Wind, Hydro Sources, Temperature effect on Load-Demand)

- Natural Gas Shortage (Due to Climate Factors and/or Supply)

'''• Outputs
'''
- Marginal Cost

- Market Clearing Prices (MCP)

•''' Goals
'''
- Objective Function Y= Min (MCP)

- Effects of Natural Gas Shortages

- Effects of Climate Factors

- Effects of Maintenance

- Effects of Calendar

- Sensitivity Analysis of commissioning a Nuclear Power Plant in the market

'''Citations'''

- Sensfuß, Frank; Ragwitz, Mario; Genoese, Massimo; Möst, Dominik (2007) : Agent-based simulation of electricity markets: a literature review, Working Paper Sustainability and Innovation, No. S5/2007, Fraunhofer-Institut für System- und Innovationsforschung ISI, Karlsruhe,

- https://nbn-resolving.de/urn:nbn:de:0011-n-661574

- https://www.econstor.eu/bitstream/10419/28520/1/570113083.pdf

- https://www.tudelft.nl/evenementen/2021/powerweb/electricity-market-simulation-game ==

''' Data '''

- EXIST Transparency Portal https://seffaflik.epias.com.tr/transparency/

- Electricity Transmission Operator https://www.teias.gov.tr/en-US

== Profit in store vs e-shop ==

''' Method:''' System Dynamics

'''Software:''' Vensim

'''Simulation'''

An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc.

'''Model parameters'''

*Expenses
**fixed
**variable
*Revenues
**customer satisfaction
**price
**a number of sales, etc.

''' The goal of the simulation '''

The goal of this simulation is to find out how the company's profits will develop in the next 5 years, individually in the store and e-shop. Find out what parameters can increase profits the most, individually for each type of store, and compare these parameters.

'''Data'''

Real data provided by the owners of the store

[[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 01:41, 16 December 2022 (CET)
:Please elaborate in more detail as we have discussed in class [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:17, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==Comparison of strategies for finding a lost person in the forest==

''' Author:''' Tomáš Kadaně (kadt02)

'''Type:''' Multi-agent

'''Software:''' NetLogo

'''Description:'''

The simulation will focus on comparing the times needed to find a lost person in a forest (area with trees).
The metric to compare the strategies will be the number of ticks needed to find the wanted person. Both the person being searched for and the searcher will be in a random location at the beginning of the simulation.
Within the simulation, I will take several measurements for each strategy and number of searchers (1 to 5), so that the number is statistically significant and use, for example, the means to compare which strategy is the most appropriate.

The model will be able to simulate several search strategies

*one step forward and then turn of random degree (-45 to 45 degrees), so random walk
*walk straight until it hits the edge of the forest or tree, then turn and continue walking straight
*first walk to the nearest corner of the forest and then a some kind of serpentine search
*possibly other strategies

'''Goals:'''

Finding the most appropriate strategy for finding a person in the forest depending on the number of people searching.

'''Agents:'''

*Searchers (e.g. police officers)
*Lost person

'''Parameters:'''

*Number of searchers
*Type of strategy
*Ticks needed to find person

'''Possible extensions:'''

*Searchers with certain pace of walking
*Finding the person won’t mean be at same location but seeing it for some distance (again certain ability of the searcher to see for certain distance)
*Cooperation of finders (formations, place distribution)
*Lost person will be moving when being looked for

[[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 16:01, 17 December 2022 (CET)

==Saving for a car==

Author: Nikola Milosavljevic - miln02

'''Problem definition'''

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has some money that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount every year.
He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase government bonds.

3. Invest in one of the world indices.

4. Invest all the money in one stock.

'''Goal'''

Create simulation that can be used as a support when making investment decision.

'''Method'''

For helping Jon to make a decision, I will use Monte Carlo simulation and Excel as an environment. The historical yield and volatility data will be used to calculate the average behaviour of all 4 options, and we will simulate possible results after 15 years. Since it can’t be expected that the market will be stable for all 15 years, economic crises will be generated.

'''Model parameters'''

Investments:

1. Initial one-time investment

2. Fixed annual investment

Market data:

1. Deposits (Rate)

2. Government bonds (Yield, volatility)

3. Index (Yield, volatility)

4. Stock (Yield, volatility)

Economic crises probability

'''Sources'''

https://finance.yahoo.com/

http://www.worldgovernmentbonds.com/

Bank website for deposit rates

[[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 16:17, 17 December 2022 (CET)

Assignments WS 2022/2023

2022-12-17T15:15:03Z

Miln02: /* Saving for a car */

{{Ambox
| text = <div>
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.
</div>
}}

{{Ambox
| text = <div>
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]].
</div>
}}

{{Ambox
| text = <div>
Topics on gambling, cards, etc. are not welcome.
</div>
}}

{{Ambox
| type = content
| text = <div>
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.
</div>
}}

== Effect of leniency programs on cartel rates by [[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) ==

''' Simulation '''

The leniency program of the European Commission offers the companies involved in a cartel either complete or partial immunity from fines if they self-report and hand over evidence. It was introduced in 1996, following the surge in amnesty applications in the wake of the 1993 revision of the Corporate Leniency Program of the US Department of Justice’s Antitrust Division. Reports from various implemented leniency programs showed that such programs led to numerous applications. However, despite the clear increase in leniency applications, the question poses itself as to whether the programs were also successful in a sense that the actual cartel rate in those countries declined.
The simulation will be based on a study of Harrington and Chang from 2015, in which they concluded the following:

• The actual cartel rate decreases in case that the leniency program does not affect the non-leniency enforcement

• But: if the non-leniency enforcement is affected because resources are shifted to the prosecution of leniency application cases, there might be two possibilities, the cartel rate might increase.

This simulation focuses on the latter case. Assuming endogenized non-leniency enforcement, the introduction of a leniency program might have a differential impact on different industries. If a leniency program is introduced, the cartels that are about to collapse will seek to self-report. This in turn shifts resources from exposing active cartels to prosecuting cartels that are already collapsing. This creates more work for the authorities, who, instead of focusing on active cartels may now focus on dying cartels. This crowding-out effect coming about with the introduction of a leniency program shall be simulated in this project.

''' Goal '''

The simulation will have the following objectives:

* Illustrate the change in cartel rates and the change in the average life expectancy of a cartel triggered by the introduction of a leniency program in case of endogenized non-leniency enforcement for industries with unstable cartels (e.g. industries with a high number of competitors, or demand with more price elasticity) and for industries with stable cartels (e.g. industries with less competitors and demand with less price elasticity).
* Illustrate how many resources may be shifted from non-leniency enforcement to prosecuting leniency application cases without it having an undesired effect on the actual cartel rate.

''' Practical relevance '''

The simulation may be used by law enforcement officials to evaluate whether a leniency program leads to the desired effect (i.e. the decrease in the cartel rate) or not. Also, it can help for deciding whether the non-leniency enforcement needs to be strengthened to prevent the crowding-out effect.

''' Method '''

The described scenario is a multi-agent simulation in which the agents are pursuing a utility-based approach. Thus, the simulation will be done with NetLogo.
The following features will be included into the simulation:

- For both industries with stable and industries with unstable cartels:

* Number of active cartels (dying after reaching avg. life expectancy)
* Number of competitors
* Average life expectancy of a cartel
* “Birth” of new cartels

- For leniency/non-leniency enforcement:
* Resources and their assignment to either leniency or non-leniency enforcement
* Capacity of taking down an active cartel
* Capacity of taking down a cartel based on leniency applications

The simulation will be based on the 2015 research from Harrington and Chang as well as on publicly accessible data from the European Commission regarding antitrust cases from 1964 until today.

''' Sources '''
* Harrington Jr, J. E., & Chang, M. H. (2015). When can we expect a corporate leniency program to result in fewer cartels?. The Journal of Law and Economics, 58(2), 417-449.
* Ordóñez‐De‐Haro, J. M., Borrell, J. R., & Jiménez, J. L. (2018). The European commission's fight against cartels (1962–2014): A retrospective and forensic analysis. JCMS: Journal of Common Market Studies, 56(5), 1087-1107.

[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

: This isn't an easy topic. Be careful about available data. '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:46, 15 December 2022 (CET)

== The prediction of divorce rate in Czech Republic for the following 50 years ==

''' The goal of the simulation '''

Divorce in the Czech republic must always contain at least one hearing in front of the court. Legally, there are many more parties involved, such as a notary, who must verify the signatures on all the important documents and many times, divorce lawyers are also necessary. To be able to satisfy the needs of the public, all the involved parties must have an idea about how many married couples are likely to get divorced in the years to come. This simulation will help prepare the courts, notaries and lawyers by making a prediction on the amount of divorces in the next 50 years. This will also help law students choose the field of law that they will specialize in by answering the question whether divorce lawyers will be necessary in the future or not.

''' Method '''

Vensim will be used for this simulation. The used data will come from the Czech Statistical Office and possibly other sources (Refer to [1] and [2]), such as published studies on the most common reasons for divorce. When possible, the data about each reason of divorce will be also found and the simulation model will contain this data.

== Edit: additional details ==

''' What all parameters will the simulation work with and how?'''

1. Number of marriages – the more marriages, the more divorces

a/ Number of people in the age 25 to 34 (i.e., the most common age to get married) – the more there is of these people, the more marriages there will be

b/ Number of divorced people in the age 40 to 49 (i.e., the most common age to get re-married after a divorce) – the more there is of these people, the more marriages there will be, however not as much as the number above

2. Micro causes of divorces = Top 10 causes of divorce as researched by the Czech Statistical Office, published yearly – the more common are these causes (alcoholism, infidelity etc), the more divorces there will be

a/ Ill-considered marriage

b/ Alcoholism

c/ Infidelity

d/ Lack of interest in the family (incl. abandon. of living together)

e/ Ill-treatment, criminal conviction

f/ Different characters, views and interests

g/ Health reasons

h/ Sexual discord

i/ Other causes

j/ Cause not given

3. Number of people in the age 40 to 49 – the more there is of these people, the more divorces there will be (it is the most common age to get divorced)

4. Macro causes of divorces

a/ Economic independence of women = the more economically independent women are, the more likely they are to divorce in case of an unhappy marriage – this will be evaluated through a comparison of data of average income of men vs. women

b/ Being religious – divorce is far less common for religious people.

''' What data source will be used for deriving the equations?'''

Based on my current research of data sources, the Czech Statistical Office has the all the data necessary for this paper.

[1] Scott, S. B., Rhoades, G. K., Stanley, S. M., Allen, E. S., & Markman, H. J. (2013). Reasons for Divorce and Recollections of Premarital Intervention: Implications for Improving Relationship Education. Couple & family psychology, 2(2), 131–145. https://doi.org/10.1037/a0032025

[2] Hawkins, Alan & Willoughby, Brian & Doherty, William. (2012). Reasons for Divorce and Openness to Marital Reconciliation. Journal of Divorce & Remarriage. 53. 453-463. 10.1080/10502556.2012.682898.

: Sounds interesting, but I miss more detail about the simulation. What all parameters will the simulation work with and how? What data source will be used for deriving the equations? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 11:03, 15 December 2022 (CET)
:: ''' Approved'''. Just make sure that the equtions, reasons for divorce and their impact on divorce rate are properly quantified.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:23, 17 December 2022 (CET)

==Crop Yield Forecasting==

''' Simulation '''

Crop growth and development simulations and yield forecasting will be performed using variables such as crop type, planting date, soil type, soil texture, and climate data (temperature, rainfall, etc.).

'''Problem definition'''

Arable land is increasingly limited, while the world's population has steadily been increasing over the years. In order to meet rapidly rising demand, production must be increased while natural resources must be protected. New agricultural research is needed to provide information on how to achieve sustainable agriculture in the face of global climate variability. Predicting crop yield under different conditions, such as different irrigation regimes, planting dates, and crop management practices, has become critical for farmers and other stakeholders who use these predictions to make more informed decisions about how to allocate resources, such as labor, equipment, and inputs, to maximize yield and productivity.

'''Method'''

Crop yield simulation tools include AquaCrop, DSSAT, and CropSyst. These tools use mathematical models to simulate crop growth and development based on input data like weather, soil type, and management practices. These tools use this data to estimate the crop's potential yield, as well as other important factors like water use and crop evapotranspiration. For this assignment I will be using AquaCrop which is a crop water productivity model developed by the United Nations Food and Agriculture Organization (FAO). It is used to simulate crop growth and yield under various environmental and management conditions. AquaCrop simulates crop growth and development, and estimates yield based on soil conditions, climate, irrigation, and management practices. The application gives access to various FAO databases with all the necessary data needed to perform a comprehensive simulation of the crop yield.

'''Citations'''

* Y. Lu, C. Wei, M. F. McCabe, and J. Sheffield, “Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information,” Agricultural Water Management, vol. 266, p. 107576, May 2022, doi: 10.1016/j.agwat.2022.107576.
* P. N. Kephe, K. K. Ayisi, and B. M. Petja, “Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa,” Agriculture & Food Security, vol. 10, no. 1, p. 10, Apr. 2021, doi: 10.1186/s40066-020-00283-5.
* N. T. Olivera, O. B. Manrique, Y. G. Masjuan, and A. M. G. Alega, “Evaluation of AquaCrop model in crop dry bean growth simulation,” Revista Ciencias Técnicas Agropecuarias, vol. 25, no. 3, pp. 23–30, Accessed: Dec. 10, 2022. [Online]. Available: https://www.redalyc.org/journal/932/93246970003/html/
* N. Pirmoradian, Z. Saadati, M. Rezaei, and M. R. Khaledian, “Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran,” Appl Water Sci, vol. 10, no. 7, p. 161, Jun. 2020, doi: 10.1007/s13201-020-01249-5.

[[User:Pierreatekwana|Pierreatekwana]] ([[User talk:Pierreatekwana|talk]]) 15:06, 15 December 2022 (CET)

: Topic souds interesting, but the proposed simulation tool has to be one of the ones we have used in our class ( as specified in How to deal with the simulation assignment:
One of your key course requirements is a submission of simulation. You choose your topic yourself, the same as a method and a tool that you will use. It could be any of the development environments we have used (Excel, Simprocess, Netlogo, or Vensim).) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:05, 17 December 2022 (CET)

== Electricity Spot Market Simulation by [[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 01:13, 16 December 2022 (CET) ==

'''Simulation'''

I’ve been working as a professional in the Turkish Electricity Market (EXIST) for more than 10 years. I had the chance to practice in business some fundamental methods such as Stochastic Dual Dynamic Programming (SDDP) for Hydro-Thermal dispatch optimization. The spot exchange of power or the power market itself has many examples of simulation. And the simulation of the uncertainties both in the short term and the long term are very critical in terms of new investments, portfolio management, and resource optimization.

'''Method'''

Both Excel and Netlogo simulations can be derived

'''• Inputs
'''

- Portfolios

(Thermals: Coal-Fired, Natural Gas-Fired, Other Thermals; Renewables: Hydro, Wind, Solar PV, Other Renewables)

- Resource Prices (Eur/mwh)

- Installed Capacity (in MW)

'''• Variables'''

- Power Demand (MWh)

- Calendar Day

'''• Uncertain Events
'''
- Maintenance (availability)

- Climate Factors (Wind, Hydro Sources, Temperature effect on Load-Demand)

- Natural Gas Shortage (Due to Climate Factors and/or Supply)

'''• Outputs
'''
- Marginal Cost

- Market Clearing Prices (MCP)

•''' Goals
'''
- Objective Function Y= Min (MCP)

- Effects of Natural Gas Shortages

- Effects of Climate Factors

- Effects of Maintenance

- Effects of Calendar

- Sensitivity Analysis of commissioning a Nuclear Power Plant in the market

'''Citations'''

- Sensfuß, Frank; Ragwitz, Mario; Genoese, Massimo; Möst, Dominik (2007) : Agent-based simulation of electricity markets: a literature review, Working Paper Sustainability and Innovation, No. S5/2007, Fraunhofer-Institut für System- und Innovationsforschung ISI, Karlsruhe,

- https://nbn-resolving.de/urn:nbn:de:0011-n-661574

- https://www.econstor.eu/bitstream/10419/28520/1/570113083.pdf

- https://www.tudelft.nl/evenementen/2021/powerweb/electricity-market-simulation-game ==

''' Data '''

- EXIST Transparency Portal https://seffaflik.epias.com.tr/transparency/

- Electricity Transmission Operator https://www.teias.gov.tr/en-US

== Profit in store vs e-shop ==

''' Method:''' System Dynamics

'''Software:''' Vensim

'''Simulation'''

An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc.

'''Model parameters'''

*Expenses
**fixed
**variable
*Revenues
**customer satisfaction
**price
**a number of sales, etc.

''' The goal of the simulation '''

The goal of this simulation is to find out how the company's profits will develop in the next 5 years, individually in the store and e-shop. Find out what parameters can increase profits the most, individually for each type of store, and compare these parameters.

'''Data'''

Real data provided by the owners of the store

[[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 01:41, 16 December 2022 (CET)
:Please elaborate in more detail as we have discussed in class [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:17, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==Comparison of strategies for finding a lost person in the forest==

''' Author:''' Tomáš Kadaně (kadt02)

'''Type:''' Multi-agent

'''Software:''' NetLogo

'''Description:'''

The simulation will focus on comparing the times needed to find a lost person in a forest (area with trees).
The metric to compare the strategies will be the number of ticks needed to find the wanted person. Both the person being searched for and the searcher will be in a random location at the beginning of the simulation.
Within the simulation, I will take several measurements for each strategy and number of searchers (1 to 5), so that the number is statistically significant and use, for example, the means to compare which strategy is the most appropriate.

The model will be able to simulate several search strategies

*one step forward and then turn of random degree (-45 to 45 degrees), so random walk
*walk straight until it hits the edge of the forest or tree, then turn and continue walking straight
*first walk to the nearest corner of the forest and then a some kind of serpentine search
*possibly other strategies

'''Goals:'''

Finding the most appropriate strategy for finding a person in the forest depending on the number of people searching.

'''Agents:'''

*Searchers (e.g. police officers)
*Lost person

'''Parameters:'''

*Number of searchers
*Type of strategy
*Ticks needed to find person

'''Possible extensions:'''

*Searchers with certain pace of walking
*Finding the person won’t mean be at same location but seeing it for some distance (again certain ability of the searcher to see for certain distance)
*Cooperation of finders (formations, place distribution)
*Lost person will be moving when being looked for

[[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 16:01, 17 December 2022 (CET)

==Saving for a car==

Author: Nikola Milosavljevic - miln02

'''Problem definition'''

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has some money that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount every year.
He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase government bonds.

3. Invest in one of the world indices.

4. Invest all the money in one stock.

'''Goal'''

Create simulation that can be used as a support when making investment decision.

'''Method'''

For helping Jon to make a decision, I will use Monte Carlo simulation and Excel as an environment. The historical yield and volatility data will be used to calculate the average behaviour of all 4 options, and we will simulate possible results after 15 years. Since it can’t be expected that the market will be stable for all 15 years, economic crises will be generated.

'''Model parameters'''

Investments:

1. Initial one-time investment

2. Fixed annual investment

Market data:

1. Deposits (Rate)

2. Government bonds (Yield, volatility)

3. Index (Yield, volatility)

4. Stock (Yield, volatility)

Economic crises probability

'''Sources'''

https://finance.yahoo.com/

http://www.worldgovernmentbonds.com/

Bank website for deposit rates

Assignments WS 2022/2023

2022-12-17T15:13:41Z

Miln02:

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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.
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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]].
</div>
}}

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Topics on gambling, cards, etc. are not welcome.
</div>
}}

{{Ambox
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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.
</div>
}}

== Effect of leniency programs on cartel rates by [[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) ==

''' Simulation '''

The leniency program of the European Commission offers the companies involved in a cartel either complete or partial immunity from fines if they self-report and hand over evidence. It was introduced in 1996, following the surge in amnesty applications in the wake of the 1993 revision of the Corporate Leniency Program of the US Department of Justice’s Antitrust Division. Reports from various implemented leniency programs showed that such programs led to numerous applications. However, despite the clear increase in leniency applications, the question poses itself as to whether the programs were also successful in a sense that the actual cartel rate in those countries declined.
The simulation will be based on a study of Harrington and Chang from 2015, in which they concluded the following:

• The actual cartel rate decreases in case that the leniency program does not affect the non-leniency enforcement

• But: if the non-leniency enforcement is affected because resources are shifted to the prosecution of leniency application cases, there might be two possibilities, the cartel rate might increase.

This simulation focuses on the latter case. Assuming endogenized non-leniency enforcement, the introduction of a leniency program might have a differential impact on different industries. If a leniency program is introduced, the cartels that are about to collapse will seek to self-report. This in turn shifts resources from exposing active cartels to prosecuting cartels that are already collapsing. This creates more work for the authorities, who, instead of focusing on active cartels may now focus on dying cartels. This crowding-out effect coming about with the introduction of a leniency program shall be simulated in this project.

''' Goal '''

The simulation will have the following objectives:

* Illustrate the change in cartel rates and the change in the average life expectancy of a cartel triggered by the introduction of a leniency program in case of endogenized non-leniency enforcement for industries with unstable cartels (e.g. industries with a high number of competitors, or demand with more price elasticity) and for industries with stable cartels (e.g. industries with less competitors and demand with less price elasticity).
* Illustrate how many resources may be shifted from non-leniency enforcement to prosecuting leniency application cases without it having an undesired effect on the actual cartel rate.

''' Practical relevance '''

The simulation may be used by law enforcement officials to evaluate whether a leniency program leads to the desired effect (i.e. the decrease in the cartel rate) or not. Also, it can help for deciding whether the non-leniency enforcement needs to be strengthened to prevent the crowding-out effect.

''' Method '''

The described scenario is a multi-agent simulation in which the agents are pursuing a utility-based approach. Thus, the simulation will be done with NetLogo.
The following features will be included into the simulation:

- For both industries with stable and industries with unstable cartels:

* Number of active cartels (dying after reaching avg. life expectancy)
* Number of competitors
* Average life expectancy of a cartel
* “Birth” of new cartels

- For leniency/non-leniency enforcement:
* Resources and their assignment to either leniency or non-leniency enforcement
* Capacity of taking down an active cartel
* Capacity of taking down a cartel based on leniency applications

The simulation will be based on the 2015 research from Harrington and Chang as well as on publicly accessible data from the European Commission regarding antitrust cases from 1964 until today.

''' Sources '''
* Harrington Jr, J. E., & Chang, M. H. (2015). When can we expect a corporate leniency program to result in fewer cartels?. The Journal of Law and Economics, 58(2), 417-449.
* Ordóñez‐De‐Haro, J. M., Borrell, J. R., & Jiménez, J. L. (2018). The European commission's fight against cartels (1962–2014): A retrospective and forensic analysis. JCMS: Journal of Common Market Studies, 56(5), 1087-1107.

[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

: This isn't an easy topic. Be careful about available data. '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:46, 15 December 2022 (CET)

== The prediction of divorce rate in Czech Republic for the following 50 years ==

''' The goal of the simulation '''

Divorce in the Czech republic must always contain at least one hearing in front of the court. Legally, there are many more parties involved, such as a notary, who must verify the signatures on all the important documents and many times, divorce lawyers are also necessary. To be able to satisfy the needs of the public, all the involved parties must have an idea about how many married couples are likely to get divorced in the years to come. This simulation will help prepare the courts, notaries and lawyers by making a prediction on the amount of divorces in the next 50 years. This will also help law students choose the field of law that they will specialize in by answering the question whether divorce lawyers will be necessary in the future or not.

''' Method '''

Vensim will be used for this simulation. The used data will come from the Czech Statistical Office and possibly other sources (Refer to [1] and [2]), such as published studies on the most common reasons for divorce. When possible, the data about each reason of divorce will be also found and the simulation model will contain this data.

== Edit: additional details ==

''' What all parameters will the simulation work with and how?'''

1. Number of marriages – the more marriages, the more divorces

a/ Number of people in the age 25 to 34 (i.e., the most common age to get married) – the more there is of these people, the more marriages there will be

b/ Number of divorced people in the age 40 to 49 (i.e., the most common age to get re-married after a divorce) – the more there is of these people, the more marriages there will be, however not as much as the number above

2. Micro causes of divorces = Top 10 causes of divorce as researched by the Czech Statistical Office, published yearly – the more common are these causes (alcoholism, infidelity etc), the more divorces there will be

a/ Ill-considered marriage

b/ Alcoholism

c/ Infidelity

d/ Lack of interest in the family (incl. abandon. of living together)

e/ Ill-treatment, criminal conviction

f/ Different characters, views and interests

g/ Health reasons

h/ Sexual discord

i/ Other causes

j/ Cause not given

3. Number of people in the age 40 to 49 – the more there is of these people, the more divorces there will be (it is the most common age to get divorced)

4. Macro causes of divorces

a/ Economic independence of women = the more economically independent women are, the more likely they are to divorce in case of an unhappy marriage – this will be evaluated through a comparison of data of average income of men vs. women

b/ Being religious – divorce is far less common for religious people.

''' What data source will be used for deriving the equations?'''

Based on my current research of data sources, the Czech Statistical Office has the all the data necessary for this paper.

[1] Scott, S. B., Rhoades, G. K., Stanley, S. M., Allen, E. S., & Markman, H. J. (2013). Reasons for Divorce and Recollections of Premarital Intervention: Implications for Improving Relationship Education. Couple & family psychology, 2(2), 131–145. https://doi.org/10.1037/a0032025

[2] Hawkins, Alan & Willoughby, Brian & Doherty, William. (2012). Reasons for Divorce and Openness to Marital Reconciliation. Journal of Divorce & Remarriage. 53. 453-463. 10.1080/10502556.2012.682898.

: Sounds interesting, but I miss more detail about the simulation. What all parameters will the simulation work with and how? What data source will be used for deriving the equations? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 11:03, 15 December 2022 (CET)
:: ''' Approved'''. Just make sure that the equtions, reasons for divorce and their impact on divorce rate are properly quantified.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:23, 17 December 2022 (CET)

==Crop Yield Forecasting==

''' Simulation '''

Crop growth and development simulations and yield forecasting will be performed using variables such as crop type, planting date, soil type, soil texture, and climate data (temperature, rainfall, etc.).

'''Problem definition'''

Arable land is increasingly limited, while the world's population has steadily been increasing over the years. In order to meet rapidly rising demand, production must be increased while natural resources must be protected. New agricultural research is needed to provide information on how to achieve sustainable agriculture in the face of global climate variability. Predicting crop yield under different conditions, such as different irrigation regimes, planting dates, and crop management practices, has become critical for farmers and other stakeholders who use these predictions to make more informed decisions about how to allocate resources, such as labor, equipment, and inputs, to maximize yield and productivity.

'''Method'''

Crop yield simulation tools include AquaCrop, DSSAT, and CropSyst. These tools use mathematical models to simulate crop growth and development based on input data like weather, soil type, and management practices. These tools use this data to estimate the crop's potential yield, as well as other important factors like water use and crop evapotranspiration. For this assignment I will be using AquaCrop which is a crop water productivity model developed by the United Nations Food and Agriculture Organization (FAO). It is used to simulate crop growth and yield under various environmental and management conditions. AquaCrop simulates crop growth and development, and estimates yield based on soil conditions, climate, irrigation, and management practices. The application gives access to various FAO databases with all the necessary data needed to perform a comprehensive simulation of the crop yield.

'''Citations'''

* Y. Lu, C. Wei, M. F. McCabe, and J. Sheffield, “Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information,” Agricultural Water Management, vol. 266, p. 107576, May 2022, doi: 10.1016/j.agwat.2022.107576.
* P. N. Kephe, K. K. Ayisi, and B. M. Petja, “Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa,” Agriculture & Food Security, vol. 10, no. 1, p. 10, Apr. 2021, doi: 10.1186/s40066-020-00283-5.
* N. T. Olivera, O. B. Manrique, Y. G. Masjuan, and A. M. G. Alega, “Evaluation of AquaCrop model in crop dry bean growth simulation,” Revista Ciencias Técnicas Agropecuarias, vol. 25, no. 3, pp. 23–30, Accessed: Dec. 10, 2022. [Online]. Available: https://www.redalyc.org/journal/932/93246970003/html/
* N. Pirmoradian, Z. Saadati, M. Rezaei, and M. R. Khaledian, “Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran,” Appl Water Sci, vol. 10, no. 7, p. 161, Jun. 2020, doi: 10.1007/s13201-020-01249-5.

[[User:Pierreatekwana|Pierreatekwana]] ([[User talk:Pierreatekwana|talk]]) 15:06, 15 December 2022 (CET)

: Topic souds interesting, but the proposed simulation tool has to be one of the ones we have used in our class ( as specified in How to deal with the simulation assignment:
One of your key course requirements is a submission of simulation. You choose your topic yourself, the same as a method and a tool that you will use. It could be any of the development environments we have used (Excel, Simprocess, Netlogo, or Vensim).) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:05, 17 December 2022 (CET)

== Electricity Spot Market Simulation by [[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 01:13, 16 December 2022 (CET) ==

'''Simulation'''

I’ve been working as a professional in the Turkish Electricity Market (EXIST) for more than 10 years. I had the chance to practice in business some fundamental methods such as Stochastic Dual Dynamic Programming (SDDP) for Hydro-Thermal dispatch optimization. The spot exchange of power or the power market itself has many examples of simulation. And the simulation of the uncertainties both in the short term and the long term are very critical in terms of new investments, portfolio management, and resource optimization.

'''Method'''

Both Excel and Netlogo simulations can be derived

'''• Inputs
'''

- Portfolios

(Thermals: Coal-Fired, Natural Gas-Fired, Other Thermals; Renewables: Hydro, Wind, Solar PV, Other Renewables)

- Resource Prices (Eur/mwh)

- Installed Capacity (in MW)

'''• Variables'''

- Power Demand (MWh)

- Calendar Day

'''• Uncertain Events
'''
- Maintenance (availability)

- Climate Factors (Wind, Hydro Sources, Temperature effect on Load-Demand)

- Natural Gas Shortage (Due to Climate Factors and/or Supply)

'''• Outputs
'''
- Marginal Cost

- Market Clearing Prices (MCP)

•''' Goals
'''
- Objective Function Y= Min (MCP)

- Effects of Natural Gas Shortages

- Effects of Climate Factors

- Effects of Maintenance

- Effects of Calendar

- Sensitivity Analysis of commissioning a Nuclear Power Plant in the market

'''Citations'''

- Sensfuß, Frank; Ragwitz, Mario; Genoese, Massimo; Möst, Dominik (2007) : Agent-based simulation of electricity markets: a literature review, Working Paper Sustainability and Innovation, No. S5/2007, Fraunhofer-Institut für System- und Innovationsforschung ISI, Karlsruhe,

- https://nbn-resolving.de/urn:nbn:de:0011-n-661574

- https://www.econstor.eu/bitstream/10419/28520/1/570113083.pdf

- https://www.tudelft.nl/evenementen/2021/powerweb/electricity-market-simulation-game ==

''' Data '''

- EXIST Transparency Portal https://seffaflik.epias.com.tr/transparency/

- Electricity Transmission Operator https://www.teias.gov.tr/en-US

== Profit in store vs e-shop ==

''' Method:''' System Dynamics

'''Software:''' Vensim

'''Simulation'''

An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc.

'''Model parameters'''

*Expenses
**fixed
**variable
*Revenues
**customer satisfaction
**price
**a number of sales, etc.

''' The goal of the simulation '''

The goal of this simulation is to find out how the company's profits will develop in the next 5 years, individually in the store and e-shop. Find out what parameters can increase profits the most, individually for each type of store, and compare these parameters.

'''Data'''

Real data provided by the owners of the store

[[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 01:41, 16 December 2022 (CET)
:Please elaborate in more detail as we have discussed in class [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:17, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==Comparison of strategies for finding a lost person in the forest==

''' Author:''' Tomáš Kadaně (kadt02)

'''Type:''' Multi-agent

'''Software:''' NetLogo

'''Description:'''

The simulation will focus on comparing the times needed to find a lost person in a forest (area with trees).
The metric to compare the strategies will be the number of ticks needed to find the wanted person. Both the person being searched for and the searcher will be in a random location at the beginning of the simulation.
Within the simulation, I will take several measurements for each strategy and number of searchers (1 to 5), so that the number is statistically significant and use, for example, the means to compare which strategy is the most appropriate.

The model will be able to simulate several search strategies

*one step forward and then turn of random degree (-45 to 45 degrees), so random walk
*walk straight until it hits the edge of the forest or tree, then turn and continue walking straight
*first walk to the nearest corner of the forest and then a some kind of serpentine search
*possibly other strategies

'''Goals:'''

Finding the most appropriate strategy for finding a person in the forest depending on the number of people searching.

'''Agents:'''

*Searchers (e.g. police officers)
*Lost person

'''Parameters:'''

*Number of searchers
*Type of strategy
*Ticks needed to find person

'''Possible extensions:'''

*Searchers with certain pace of walking
*Finding the person won’t mean be at same location but seeing it for some distance (again certain ability of the searcher to see for certain distance)
*Cooperation of finders (formations, place distribution)
*Lost person will be moving when being looked for

[[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 16:01, 17 December 2022 (CET)

==Saving for a car==

'''Problem definition'''

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has some money that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount every year.
He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase government bonds.

3. Invest in one of the world indices.

4. Invest all the money in one stock.

'''Goal'''

Create simulation that can be used as a support when making investment decision.

'''Method'''

For helping Jon to make a decision, I will use Monte Carlo simulation and Excel as an environment. The historical yield and volatility data will be used to calculate the average behaviour of all 4 options, and we will simulate possible results after 15 years. Since it can’t be expected that the market will be stable for all 15 years, economic crises will be generated.

'''Model parameters'''

Investments:

1. Initial one-time investment

2. Fixed annual investment

Market data:

1. Deposits (Rate)

2. Government bonds (Yield, volatility)

3. Index (Yield, volatility)

4. Stock (Yield, volatility)

Economic crises probability

'''Sources'''

https://finance.yahoo.com/

http://www.worldgovernmentbonds.com/

Bank website for deposit rates