Assignments WS 2022/2023

Effect of leniency programs on cartel rates by 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.

Baumareb (talk) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

This isn't an easy topic. Be careful about available data. Approved 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? 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.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.

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).) Oleg.Svatos (talk) 07:05, 17 December 2022 (CET)

Electricity Spot Market Simulation by Ceta (talk) 01:13, 16 December 2022 (CET)

IDEA 2 Pumped-storage hydroelectricity (PSH), or pumped hydroelectric energy storage (PHES), is a type of hydroelectric energy storage used by electric power systems for load balancing. The method stores energy in the form of gravitational potential energy of water, pumped from a lower elevation reservoir to a higher elevation. Low-cost surplus off-peak electric power is typically used to run the pumps. During periods of high electrical demand, the stored water is released through turbines to produce electric power. Although the losses of the pumping process make the plant a net consumer of energy overall, the system increases revenue by selling more electricity during periods of peak demand, when electricity prices are highest. If the upper lake collects significant rainfall or is fed by a river then the plant may be a net energy producer in the manner of a traditional hydroelectric plant.

Pumped-storage hydroelectricity allows energy from intermittent sources (such as solar, wind) and other renewables, or excess electricity from continuous base-load sources (such as coal or nuclear) to be saved for periods of higher demand. The reservoirs used with pumped storage are quite small when compared to conventional hydroelectric dams of similar power capacity, and generating periods are often less than half a day.

Problem definition Company PMP wants to build a new pumped storage hydroelectricity power plant across a river. This facility will be generating power during the peak hours of the day because of its half day upstream reservoir capacity. And for the off-peak hours it will be pumping up the water that is collected in its downstream reservoir using the power from the grid. So, basically, during the day it will be generating power and selling it with peak prices, and during the evening it will be consuming energy and will need to buy it for off peak prices. The Company PMP is trying to decide about downstream reservoir capacity. To decide it they need to simulate the system with water inflow input and market prices to decide what proportion of upstream and downstream reservoirs would be optimal to decide on size of downstream reservoir.

Parameters:

Stream inflow (m3/s or m3/day or another conversion)

Upstream active reservoir volume = half day generation of hydro power plant

Installed capacity of hydro power generation (MW)

Precipitation (Random normal)

Evaporation (0.1 percentage lost every hour)

Generator power factor (How much water volume needed for per MWh generation)

Pump flow capacity (m3/s or m3/day or another conversion)

Pumping consumption factor (How much MWh needed per m3)

Initial active reservoir levels (percentage full)

Spot Market Prices (Usd/MWh)

Upstream reservoir maximum capacity (m3) (Fixed)

Downstream reservoir maximum capacity (m3) (Variable)

Compulsory flow schedule back to river (m3) (Fixed)

Method Most probably Vensim simulation will be done.

Upstream active reservoir volume = Initial reservoir volume level (UpS) + stream inflow + pumped inflow – penstock outflow (to generation) + precipitation – evaporation (Between 0 and Upstream reservoir maximum capacity (m3))

Downstream active reservoir volume = Initial reservoir level (DwS) + penstock outflow (from generation) – pumped inflow (Between 0 and Downstream reservoir maximum capacity (m3) (Variable)) - compulsory flow schedule back to river

Revenues = SUM (Power Generated x Spot Market Price) - SUM (Power Consumed x Spot Market Price)

Power Generated = Generator power factor x penstock outflow (to generation)

Power Consumed = Pumping consumption factor x pumped inflow

Goal To simulate and determine the ratio of Upstream reservoir maximum capacity / Downstream reservoir maximum capacity

Sources: https://en.wikipedia.org/wiki/Pumped-storage_hydroelectricity https://seffaflik.epias.com.tr/transparency

IDEA 1

Problem definition

Anthony is as a portfolio manager in the power company Goodpower. Goodpower has a portfolio of hydro power plants. Goodpower is a market participant in a liberated market structure. The power generation can be sold either in spot market with volatile prices, or it can be sold with a yearly fixed price on over the counter (OTC). Goodpower assigned Anthony responsible for optimization of power generation revenue. Now Anthony needs to decide on how much generation to risk in the volatile spot market and how much to risk with the fixed price. After contacting the power brokers in OTC market, he was offered the following deals:

1. A baseload deal with a fixed price.

2. An off-peak hours deal with a fixed price.

3. A peak hours deal with a fixed price.

All of the deals will have random limited volumes. A combination of peak and off-peak deal with the same volume is basically equal to a baseload deal with the same volume.

Peak deal represents the constant load between 08:00 - 20:00 (12 Hours).

Off-Peak deal represents the constant load between 20:00 - 08:00 (12 Hours).

Baseload deal represents the constant load for 24 hours.

Goal

Simulation that can be used as a decision support tool when trading a power portfolio.

Method

Monte Carlo simulation in Excel environment will be created. The historical spot prices will be used to determine fixed deal prices. The historical generation values will be used to determine generation scenarios (wet season - high.generation, average generation, dry season – low generation). The volatility of spot market prices will be based on again historical spot prices. The simulation of 1 year = 8760 hours will be generated. Since, the stability spot market prices in winter are dependent on natural gas shortages, these shortage scenarios will be added to the simulation.

The natural gas shortage effect will be extracted from past price data. There's a price cap for the highest bid and the spot market prices equal to highest bid means the supplied amount of power in the market can barely match the demand. So the highest bid gets selected to match the demand as much as possible.

Model parameters

• Hydro Generation scenarios:

- Wet season – high generation (MWh)

- Average season - average generation (MWh)

- Dry season – low generation (MWh)

• Market data:

- Volatile spot market prices (USD/MWh)

- Fixed deal prices will be based on past year spot market prices (While OTC market prices can’t be publicly viewed)

Data

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

From the description I am not sure that I understnad what simulation is being proposed. What will the simulation actully look like, what is it going simulate exactly? If you want to take into consideration the effcts like the Effects of Natural Gas Shortages, how will you quantify the strentg of such effect? Oleg.Svatos (talk) 21:47, 17 December 2022 (CET)

Professor I made changes in my simulation. Can you please check?

Idea 2 approved.Oleg.Svatos (talk) 20:52, 18 December 2022 (CET)

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. To ensure customer satisfaction the company should make some expenses.

Model parameters

• Expenses
  • fixed
  • variable
• Revenues
  • customer satisfaction -> influence amount of expenses
    • Product quality,
    • Speed of orders/purchases processing,
    • Opening hours, working on weekends and holidays,
    • The possibility of picking up the order in the store/speed of delivery
    • Increasing customer satisfaction using sales and giving gifts for the order
    • Store availability
    • Parking
    • Complaints fees
    • Services: floor coverings including consultations and estimates, whipstitch of carpet
  • price
  • a number of sales, etc.

The goal of the simulation

The goal of this simulation is to find out what parameters can increase profit the most (individually for each type of store), to find a balance between expenses to satisfy the customers in order to achieve the profit, and in the end to compare these parameters.

Data

Real data provided by the owners of the store

Ploo00 (talk) 01:41, 16 December 2022 (CET)

Please elaborate in more detail as we have discussed in class Oleg.Svatos (talk) 07:17, 17 December 2022 (CET)

I changed the assignment a little bit. Can you please look at it? Ploo00 (talk) 19:50, 17 December 2022 (CET)

If you have the data to derive the parameters from, than Approved. Describe in the report how you have derived the effects of and on the customer satisfaction. Oleg.Svatos (talk) 21:52, 17 December 2022 (CET)

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

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

Miln02 (talk) 16:17, 17 December 2022 (CET)

Approved Oleg.Svatos (talk) 22:14, 17 December 2022 (CET)

~~~~

Szenario Evacuation Model

Author: Julian Bleyer

Simulation

My idea is to recreate a building (e.g. a stadium, hall, e.g. in Prague) in Prague and simulate how long it takes to evacuate all the people. Possible are also other Szenarios, e.g. with fire, or water is flooding into the room. The model should be based on a real event site or building, which is then specified.

Goal

The aim is to adapt different strategies and parameters during the escape. For example, people can panic, genders act differently, or the age of the people has an influence on how quickly they leave the place. The aim is to simulate how long it takes to leave the stadium, for example. If it takes too long, it is recommended to adjust the number of escape routes.

Practical relevance

This is especially important because in case of attack plans, a quick evacuation must be possible. The model can be used to adapt escape plans if necessary.

Method The idea is to simulate in Netlogo, alternatively a similar software.

Model Parameters

-Number of persons -Age distribution (children, adults, seniors) -Panic tendency -escape strategy -other factors

Sources

Location maps of venues, e.g. the o2 station in Prague or a Cinema room, number of tickets sold, gender distribution depending on the event, etc.

~~~~

Julian Bleyer (talk) 14:16, 18 December 2022 (CET)

Car Park Solution for a New Cinema

Author: kane02

Problem definition

A brand-new cinema is opening at Vypich in 6 months at one of the busiest streets in the region. The ambitious owners decided to use their extra budget to operate a small parking space right in front of the cinemas entrance for providing a space to park for customers and generate further profits. Planned parking space will have fixed expense for each month but the land itself can be extended. Owners are now in need of expertise on how to approach this issue. Their requirements consist of.

1. Counter on when a car enters and departs. 2. Create a receipt depending on hours. 3. Take reservations and allocate the space.

Goal

1. Create simulation that optimizes the potential waiting time, price, and number of the parking space for stake holders. 2. Offer solution on how to increase profits.

Method

For getting the job done I shall be using NetLogo to create the simulation based on client-side metrics and goals.

Model parameters

1. Park Timer

 a. Counter for calculating total minutes
 b. Boolean checker for availability

2. Billing

 a. Set up rates per hours
 b. Conditions on specific days

3. Reservation and Allocation

 a. Reservation timer will adjust the potential waiting timer
 b. When space is reserved new set of behaviour and conditions apply but price is fixed

Sources

https://ccl.northwestern.edu/netlogo/models/

https://jmvidal.cse.sc.edu/netlogomas/

Kane02 (talk) 14:10, 18 December 2022 (CET)

~~~~

Ukrainian refugee crisis 2022

Author: BortnikSvitlana

Problem definition

The current war in Ukraine has created the greatest refugee migration to OECD countries since World War II. People have been forced to evacuate their homes in search of safety, protection, and aid due to the escalation of the conflict in Ukraine, which has resulted in civilian casualties and the damage of civilian infrastructure. Since the Russian Federation's invasion of Ukraine began in February 2022, there have been more than 5.3 million refugees in Europe.

Goal

The goal of this simulation is to build an agent-based model (ABM) to study dynamics of refugee migration due to the war in Ukraine 2022.

Method

The apparent choice should be a multi-agent simulation because we are dealing with people moving around and having attributes. For this simulation, I've chosen to use NetLogo 6.3.0.

Model parameters

-Population

-Country

-Number of refugees

-Age distribution (children, adults, seniors)

Sources

https://www.statista.com/statistics/1312584/ukrainian-refugees-by-country/

https://data.unhcr.org/en/situations/ukraine

https://www.migrationdataportal.org/ukraine/crisis-movements

BortnikSvitlana (talk) 16:13, 18 December 2022 (CET)

~~~~

Blockade in Artsakh on its way to humanitarian disaster

Author: Hakobyan Irena

Problem definition

Currently, 120.000 people in Artsakh are in blockade. It has become impossible to deliver food, particularly bread and flour, as well as other basic necessities to these communities. It is stated that the provocative actions of Azerbaijan may lead to a large-scale humanitarian disaster. The goal of the simulation is to examine how many days the imported food would be enough for this number of population.

Method

For this simulation, I'll use NetLogo 6.3.0, as it's based on population and supply attributes.

Model variables

- Population

- Food supply

- Country

Sources

https://en.wikipedia.org/wiki/2022_Artsakh_blockade

Household electricity consumption

Author: ruzv01

Problem definition

Household electric energy consumption is a significant problem because it contributes to climate change and can be expensive for individuals and families. The production of electricity often involves the burning of fossil fuels, which releases greenhouse gases into the atmosphere and contributes to global warming. In addition, as more and more devices and appliances in homes become electrified, the demand for electricity increases, leading to higher energy bills.

Goal

The goal of simulating household electric energy consumption is to model and predict the electricity usage of a home. This can help individuals and families understand their energy consumption patterns and identify ways to reduce their energy use, as well as help utilities and energy companies predict and plan for electricity demand. By understanding and optimizing household electric energy consumption, we can work towards a more sustainable and efficient energy system.

Method

The simulation will be done in Vensim.

Model parameters

- Household size (m2)

- Number of appliances and devices

- The number of people living in a household

- Renewable energy sources (If a household has installed renewable energy sources)

- Energy price

- Home age (older homes may be less energy efficient than newer homes)

Sources

https://www.czso.cz/csu/czso/spotreba-paliv-a-energii-v-domacnostech

https://www.kurzy.cz/komodity/cena-elektriny-graf-vyvoje-ceny/

Ruzv01 (talk) 18:18, 18 December 2022 (CET)

I dont see a simulation in it yet. Elaborate it in more detail - what will you simulate exactly? since so far it looks like the more appliances the higher consumption ... Since you use the Vensim, what would be the feed back loops? Oleg.Svatos (talk) 20:48, 18 December 2022 (CET)

Thank you for the feedback. I wanted to keep the assignment more general to have a space for tweaking during the actual modeling. The main feedback loops would be: The "conservation feedback loop," in which household reduce their electricity consumption in response to high energy prices, which in turn leads to lower demand for electricity and lower energy prices. And the "technology feedback loop," in which the adoption of energy-efficient appliances and devices leads to lower electricity consumption, which in turn reduces the demand for electricity and encourages further adoption of energy-efficient technologies. In addition to this, I think it would also make sense to add "household income" as a model parameter.Ruzv01 (talk) 13:20, 19 December 2022 (CET)

OK, Im still not very convinced about the usefullness of the simulation (especially how you will incorporate the Renewable energy sources and energy-efficient appliances), but you have the chance to persuade me, Approved.Oleg.Svatos (talk) 16:22, 19 December 2022 (CET)

~~~~

Heating behaviour of households

Author: Adam Valtr

Simulation

I would like to simulate a heating behavour of a household - which would in turn allow for prediction of its energy bill. Based on a weather data (temperature in approximate location of a supposed building), energy price (per kWh) and technological and spacial parametrs of a heated building (size, heat loss, etc. [see Variables]) it would model the actual temperature the building would be heated to according to it's inhabitants preferences - such as the minimal inside temperature and optimal temperature and their trade-off fuction between comfortable temperature and energy costs (utility gained by temperature increase and utility lost by money spent).

Suppose a person wants its house to be heated to 21 degrees Celsius, to achieve it, it needs to burn X kWh of natural gas. The amout of natural gas needed will vary based on outside temperature. If the outside temperature is 10 °C the energy needed is lower than when it's freezing. This fenomenon is known as temperature gradient - the bigger the gradient the bigger the heat loss, therefore the bigger the energy consuption to maintain the inside temperature goal. If the marginal increase in energy consuption would result in large enough increase in energy bill the person may choose to withstand lower inside temperature, because the utility gained by heating more, would result in higher utility loss caused by higher expenses.

Goal

The goal is to predict energy consuption (electricity or gas) of a household, based on one's utility function (trade of between comfortable temperature and energy bill), weather and technological parameters of a building.

Practical relevance

In face of a so called "energy crisis" - meaning dramatical increase in year over year prices of natural gas and electricity, which are the main comodities used for heating by households, modeling one's projected energy costs, may improve one's budgeting and fiscal planning abality and inform one's decision to invest in energy-saving measures.

Method

Excel and maybe Vensim

Variables

• outside temperature
• inside temperature
• energy required to heat m3 of air
• volume of heated building
• heat loss (based on certificate of energy efficiency of building)
• heat source efficiency
• minimal acceptable inside temperature
• optimal inside temperature
• energy price
• utility of 1 degree change of inside temperature
• utility of 1 Kč

Sell Twitter, Inc. or hold?

Author: botd00

Problem definition

The idea for this simulation is based on a recent acquisition of a social media company Twitter, Inc. for 44 billion USD. Let's suppose that we would like to see whether the acquisition price is appropriate or if it would be better to keep the company and generate income. Let's say we want to see the outcome in 10 years. There is also a probability that the social network would get replaced by another competitor (other social network) or the general interest in the network just drops off. On the other hand there is a chance that Twitter, Inc. could acquire another social network which could possibly lead to a bigger revenue in the upcoming years.

Goal

The goal of the simulation is to find out whether we should accept the 44 billion USD acquisition price or if we would be better off owning the company and making profit in the next 10 years.

Method

The simulation will be done in Excel via Monte Carlo method.

Model parameters

• Initial market value
• Annual revenue based on historical data
• Annual expenses based on historical data

• Probability of being replaced by an all new social network or a general drop of interest in the network
• Probability of acquisition of another network – which could possibly lead to a bigger revenue

Sources

https://companiesmarketcap.com/twitter/revenue/

https://investor.twitterinc.com/financial-information/annual-reports/default.aspx

Predicting NBA players’ statelines

Author: prin03

Simulation

Over the past 10 years basketball dynamics have been changing rapidly: LeBron James, Stephen Curry, Luka Doncic and progressing rookies made the game really unpredictable and surprising. Forecasting of matches has a practical use, e.g. in advanced analytics or betting. In this simulation I will try to predict players’ statistics.

Goal

The goal of this simulation is to predict player’s state-lines, based on which it would be possible to forecast the game, or even season results. So, there will be 3 dimensions: players, games and season. I will simulate the player’s points based on his stats from current and previous season.

Method

I will use Monte Carlo simulation with Excel and maybe Python for preparing the data.

Variables

• Points
• Assists_rate
• Game arena (Home/away)
• 3PA
• 3P%
• 2PA
• 2P%
• FTA
• FT%
• Age
• Season_num
• Minutes
• Minutes per game
• Blocks
• Steals

Data

NBA.com Basketballreference.com

Price of web application development

Author: Matěj Oliva (olim02)

Problem definition

Software development has two variables which are the hardest to accomplish and estimate correctly - price and how long it will take. At the same time those are the first questions customers ask even before they specify their idea of web application. The usual process is to breakdown all customers demands to as small parts as possible and for every single one of them, so the price and time consumption estimations are as precise as possible. After receiving all demands on web application, there are several methods of price and duration estimation, but even with the most precise estimation, there are always complications during development, which cannot be predicted and thus the delivery is delayed and the price is increasing. These risks can be at least calculated in the prediction as possible price hikes. Because servers and web app administration would add enough parameters to create another simulation, I will only simulate the development of it itself. Also let's assume all developers are Full-stack, so they can work on any part of the application. Because the estimation would be different for different languages and technologies, I will choose the environment I know and as a developer have experience with for better estimations. So I will assume, the estimations are calculated for web application made completely new, customer doesn't have anything yet and the company should develop the app with these technologies:

• React (frontend framework)
• Material UI (CSS Framework)
• MySQL, PHPMySQL (DBMS)
• Node.js (backend framework)
• Express.js (REST API)

Goal

The goal of the simulation is to be able to estimate price and time delivery based on the customers expectations.

Method

My idea is, that I would use the planning poker technique, which is based on Fibonnaci sequence. Every elementary task is given a value on this sequence based on how demanding it is. I would specify and try to estimate both price and time delivery for as much atomic function as I can think of (by atomic functions I mean for example account management => atomic ones would be: login, sign up, user table, changing users roles, etc.). So the random variable would be customer choosing which functions he wants to be used in the web application. Every function he adds to the simulation will have a risk of having bugs or unexpected problems during development, so the more functions customer adds, the bigger risk of price increase and time delivery delay. Also the more complex the function the more severe would the consequences be.

For simulation I would use Vensim.

Model parameters

• Demanded functions (number of tables, number of pages, login, reservations,...)
• Function complexity
• Number of developers
• Developer hour rate

• Probability of function having bugs to repair
• Probability of having trouble developing function

Sources

https://www.planningpoker.com/