Simulace.info - User contributions [en]

LS 2022/2023

2023-06-21T00:04:23Z

Prin03vse:

{{DISPLAYTITLE:LS 2022/2023}}

Semestrální práce (simulace) z letního semestru 2022/2023. Sem přidejte odkaz na stránku s Vaší prací až bude hotová.
Nejprve je třeba nechat schválit '''[[Assignment SS 2022/2023/cs|zadání práce]]'''.

[https://www.simulace.info/index.php?title=User:Kouj09/Simulace Veřejné toalety] - [[User:Kouj09|Kouj09]] ([[User talk:Kouj09|talk]]) 14:48, 12 June 2023 (CET)

[https://www.simulace.info/index.php/Život_dítěte Život dítěte (Vensim)] - Tatiana Čižmáriková

[https://www.simulace.info/index.php/User:Trat13 Simulace odsouzených osob v ČR (Vensim)] - Thu Thuy Tranová

[https://www.simulace.info/index.php/Simulace_zálohovacího_systému] - Miloslav Vach

[https://www.simulace.info/index.php/Stádo_pakoňů Stádo pakoňů (NetLogo)] - Petr Müller

[https://www.simulace.info/index.php/Predpove%C4%8F_priebehu_pand%C3%A9mie Predpoveď priebehu pandémie(Vensim)] - Adam Daniš

[https://www.simulace.info/index.php/Doprava_na_semaforové_křižovatce_a_kruhové_křižovatce Doprava na semaforové křižovatce a kruhové křižovatce] -Jan Lampa

[https://www.simulace.info/index.php/Evakuacia_lietadla_v_pripade_nehody Evakuácia lietadla v prípade nehody (NetLogo) ] - Tomáš Blahut

[https://www.simulace.info/index.php/Prubeh_maratonu Průběh maratonu (NetLogo) ] - holj13

[https://www.simulace.info/index.php/NBA_Monte_Carlo_Simulation NBA Monte Carlo Simulation] - prin03

LS 2022/2023

2023-06-21T00:03:58Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-21T00:03:38Z

Prin03vse: Prin03vse moved page Simulace.info:NBA Monte Carlo Simulation to NBA Monte Carlo Simulation

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal

[[File:Tree.JPG]]

3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description
'''
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

[[File:Main menu.JPG]]

After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis
'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation
'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis
'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements
'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed, and the application can be used for real life predictions.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

[[File:NBA MC Simulation.zip]]

Data source (xlsx):

[[File:NBA PO Simulation Data.xlsx]]

Requirements (txt):

[[File:Requirements.zip]]

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

Simulace.info:NBA Monte Carlo Simulation

2023-06-21T00:03:38Z

Prin03vse: Prin03vse moved page Simulace.info:NBA Monte Carlo Simulation to NBA Monte Carlo Simulation

#REDIRECT [[NBA Monte Carlo Simulation]]

NBA Monte Carlo Simulation

2023-06-21T00:03:24Z

Prin03vse: Prin03vse moved page User:Prin03vse to Simulace.info:NBA Monte Carlo Simulation

User:Prin03vse

2023-06-21T00:03:24Z

Prin03vse: Prin03vse moved page User:Prin03vse to Simulace.info:NBA Monte Carlo Simulation

#REDIRECT [[Simulace.info:NBA Monte Carlo Simulation]]

LS 2022/2023

2023-06-21T00:02:04Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-21T00:00:57Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-21T00:00:41Z

Prin03vse:

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal

[[File:Tree.JPG]]

3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description
'''*
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

[[File:Main menu.JPG]]

After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis
'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation
'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis
'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements
'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed, and the application can be used for real life predictions.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

[[File:NBA MC Simulation.zip]]

Data source (xlsx):

[[File:NBA PO Simulation Data.xlsx]]

Requirements (txt):

[[File:Requirements.zip]]

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

NBA Monte Carlo Simulation

2023-06-21T00:00:15Z

Prin03vse:

File:Requirements.zip

2023-06-20T23:59:54Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-20T23:57:26Z

Prin03vse:

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal

[[File:Tree.JPG]]

3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description
'''
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

[[File:Main menu.JPG]]

After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis
'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation
'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis
'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements
'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed, and the application can be used for real life predictions.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

[[File:NBA MC Simulation.zip]]

Data source (xlsx):

[[File:NBA PO Simulation Data.xlsx]]

Requirements (txt):

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

File:NBA MC Simulation.zip

2023-06-20T23:56:59Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-20T23:56:37Z

Prin03vse:

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal

[[File:Tree.JPG]]

3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description
'''
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

[[File:Main menu.JPG]]

After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis
'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation
'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis
'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements
'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed, and the application can be used for real life predictions.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

Data source (xlsx):
[[File:NBA PO Simulation Data.xlsx]]
Requirements (txt):

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

File:NBA PO Simulation Data.xlsx

2023-06-20T23:56:13Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-20T23:55:40Z

Prin03vse:

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal

[[File:Tree.JPG]]

3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description
'''
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

[[File:Main menu.JPG]]

After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis
'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation
'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis
'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements
'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed, and the application can be used for real life predictions.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

Data source (xlsx):

Requirements (txt):

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

NBA Monte Carlo Simulation

2023-06-20T23:54:46Z

Prin03vse:

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal

[[File:Tree.JPG]]

3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description
'''
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

[[File:Main menu.JPG]]

After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis
'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation
'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis
'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements
'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

Data source (xlsx):

Requirements (txt):

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

File:Main menu.JPG

2023-06-20T23:53:28Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-20T23:53:04Z

Prin03vse:

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal

[[File:Tree.JPG]]

3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description
'''
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

[[File:Main_menu.jpg]]

After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis
'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation
'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis
'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements
'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

Data source (xlsx):

Requirements (txt):

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

File:Tree.jpg

2023-06-20T23:51:04Z

Prin03vse: Tree from jupyter notebook

== Summary ==
Tree from jupyter notebook

File:Tree.JPG

2023-06-20T23:49:45Z

Prin03vse:

NBA Monte Carlo Simulation

2023-06-20T23:48:25Z

Prin03vse: NBA Monte Carlo Simulation

==Introduction==
NBA Monte Carlo Simulation is a Jupyter Notebook application, which is used to predict NBA games result. There are different potential users of this app: from fantasy league players to sports betters. This simulation also can help advanced basketball analytics by predicting possible results.

==Installation==
Before starting the notebook, please, ensure, that you fullfilled the requirements (from requirements.txt).

1) install python (https://www.python.org/downloads/)
2) install jupyter notebook (https://jupyter.org/install)
3) install python libraries
1. run jupyter notebook
2. on opened window, click new -> terminal
[[File:Tree.jpg]]
3. in terminal, paste in order:
- pip install pandas
- pip install numpy
- pip install ipywidgets
4) go back to jupyter menu, click upload and select .ipynb file (from this page)

==Running the application==
'''Application description'''
After opening .ipynb file, click "Run" on the menu above.
Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)
[[File:Main_menu.jpg]]
After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.

'''Pre-Analysis'''
Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.

'''Simulation'''
As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).

==Results==
'''Post-Analysis'''
After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
The top 5 teams I got as NBA champions are:
* DEN (Denver Nuggets, real life champions, approx. 60 times)
* LAL (Los Angeles Lakers, real life Western Conferenc Finalist, 38 times)
* ATL (Atlanta Hawks, real life lost in first round, 35 times)
* NY (New York Knicks, real life lost in second round, 27 times)
* BOS (Boston Celtics, real life Eastern Conference Finalist, 24 times)

This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.

'''Improvements'''
From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.

From simulation perspective, the main improvement I see is adding more minutes and possibilities for other players from rotation. Also, I decided to exclude minutes played random variable, because when I tried to run the simulation with it, the results were very chaotic and unrealistic - one team could score only 50 points, other team more then 200, which, of course, does not make sense. I think there can be kind of algorithm to somehow limit the variable, because the cause of problem is tbat standard deviation of minutes per game is very huge (up to 16 minutes, which means with average 0.43 scoring rate makes +- 7 points per player).

==Conclusion==
To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed.

==Files==
All the documents can be downloaded here.
Simulation (ipynd):

Data source (xlsx):

Requirements (txt):

==Data sources==
https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

NBA game simulation

2023-01-25T22:37:02Z

Prin03vse: /* Source and Code */

Introduction
NBA games are hardly predictable. Although it is clear that leader has more chances against offsider, there can be situations like injuries, bad shooting or fouling out. This factors influence the result of the game. My idea is to try forecasting games' results based on individual players' statistics. It can be useful for advanced sport analytics, simulating the season and even for betting.

=Problem definition and Method=
As an input data I got a dataset for 2017/2018 season with boxscores of the games with different players' statistics. There were a lot of indicators for every team member per individual game. I had to clean the data, so the attributes that I needed for the simulation were: Player, Team, Position, Played Games, Points, Role, Arena, Minutes Played. For the simulation, I chose Monte Carlo simulation for forecasting.

=Model=
As I have already mentioned, I have cleaned the dataset, so only relevant attributes remain. After this, some manipulations were needed. All the calculated fields were diveded by arena - Home or Away.

First of all, I decided to find out in how many games individual player took part. Then I was able to calculate probability of '''missing the game''' and '''being a starter'''.

Then I added '''points rate''' (points per minute) for each game per player, found out '''minimum''' and '''maximum''' rates and defined '''minimum''' and '''maximum minutes played'''.

Before simulating results, I created a list called :"Preprocess", where the exact simulation is running.

So, if a user chooses 2 teams (Home and Away), it calculates starting 5, if player misses the game, minutes and points. There is an important constaint regarding the values: there are 5 starters and 5 positions only.

Starting 5 is defined by the Role (starter)/Games Played. If there are several players, who were switching between each other in start, there will be a random choice.

Minutes are calculated by random between minimum and maximum minutes played. For starting players are defined at once. There is a limit of 48 minutes per each position, so I decided to make a coefficient, which represents a part per each bench player to remaining time.

And finally points are calculated as random between minimum and maximum player's points rate multiply minutes played.

=Results=
As a result, if we combine all of the players' points, we can identify a winner.

=Conclusions=
After many simulations, I ended up with conclusion, that there is a clear home advantage. Some of the results were even quite similar to real ones. But mainly starting players influence the game: if they have more minutes, there is a higher possibility of winning. It comes up from points rate, and by average starting 5 has it higher.

=Source and Code=
Simulation with datasource and manipulations can be found there: https://www.simulace.info/images/MC_NBA_prin03.xlsx

(Original datasource: https://www.kaggle.com/datasets/pablote/nba-enhanced-stats?resource=download)

=Comments=
There can be some bugs when running a simulation. Mainly it occurs when a person opens it in English version of Excel. On my personal laptop it works perfectly, but I tried on other and it somehow crashed a couple of time (#SPILL). After some refreshes, you can get the result.

WS 2022/2023

2023-01-25T22:36:38Z

Prin03vse: prin03

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]]

-- [[User:Abizah1|Abizah1]] ([[User talk:Abizah1|talk]]) 23:55, 22 January 2023 (CET) https://www.simulace.info/index.php/Boxing_athlete

-- [[User:Pierreatekwana1|Pierreatekwana1]] ([[User talk:Pierreatekwana1|talk]]) 00:24, 23 January 2023 (CET) Crop yield simulation: [[Cropyield]]

-- [[User:Kane02|Kane02]] ([[User talk:Kane02|talk]]) 18:52, 23 January 2023 (CET) Car Park Solution for a New Cinema [[Car Park Solution for a New Cinema]]

-- [[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 19:43, 23 January 2023 (CET) Comparing searching stategies [[Finding strategies comparison]]

-- [[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 19:50, 25 January 2023 (CET) Profit in store vs e-shop [[Profit in store vs e-shop]]

-- [[User:Prin03vse|Prin03vse]] ([[User talk:Prin03vse|talk]]) 23:35, 25 January 2023 (CET) NBA Game Simulation [[NBA game simulation]]

NBA game simulation

2023-01-25T22:34:50Z

Prin03vse: NBA MC Simulation

Introduction
NBA games are hardly predictable. Although it is clear that leader has more chances against offsider, there can be situations like injuries, bad shooting or fouling out. This factors influence the result of the game. My idea is to try forecasting games' results based on individual players' statistics. It can be useful for advanced sport analytics, simulating the season and even for betting.

=Problem definition and Method=
As an input data I got a dataset for 2017/2018 season with boxscores of the games with different players' statistics. There were a lot of indicators for every team member per individual game. I had to clean the data, so the attributes that I needed for the simulation were: Player, Team, Position, Played Games, Points, Role, Arena, Minutes Played. For the simulation, I chose Monte Carlo simulation for forecasting.

=Model=
As I have already mentioned, I have cleaned the dataset, so only relevant attributes remain. After this, some manipulations were needed. All the calculated fields were diveded by arena - Home or Away.

First of all, I decided to find out in how many games individual player took part. Then I was able to calculate probability of '''missing the game''' and '''being a starter'''.

Then I added '''points rate''' (points per minute) for each game per player, found out '''minimum''' and '''maximum''' rates and defined '''minimum''' and '''maximum minutes played'''.

Before simulating results, I created a list called :"Preprocess", where the exact simulation is running.

So, if a user chooses 2 teams (Home and Away), it calculates starting 5, if player misses the game, minutes and points. There is an important constaint regarding the values: there are 5 starters and 5 positions only.

Starting 5 is defined by the Role (starter)/Games Played. If there are several players, who were switching between each other in start, there will be a random choice.

Minutes are calculated by random between minimum and maximum minutes played. For starting players are defined at once. There is a limit of 48 minutes per each position, so I decided to make a coefficient, which represents a part per each bench player to remaining time.

And finally points are calculated as random between minimum and maximum player's points rate multiply minutes played.

=Results=
As a result, if we combine all of the players' points, we can identify a winner.

=Conclusions=
After many simulations, I ended up with conclusion, that there is a clear home advantage. Some of the results were even quite similar to real ones. But mainly starting players influence the game: if they have more minutes, there is a higher possibility of winning. It comes up from points rate, and by average starting 5 has it higher.

=Source and Code=
Simulation with datasource and manipulations can be found there: https://www.simulace.info/images/MC_NBA_prin03.xlsx
(Original datasource: https://www.kaggle.com/datasets/pablote/nba-enhanced-stats?resource=download)

=Comments=
There can be some bugs when running a simulation. Mainly it occurs when a person opens it in English version of Excel. On my personal laptop it works perfectly, but I tried on other and it somehow crashed a couple of time (#SPILL). After some refreshes, you can get the result.

File:MC NBA prin03.xlsx

2023-01-25T22:28:59Z

Prin03vse: Monte Carlo Simulation NBA

== Summary ==
Monte Carlo Simulation NBA

Assignments WS 2022/2023

2022-12-20T20:18:54Z

Prin03vse:

{{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) ==
'''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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 21:47, 17 December 2022 (CET)

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

:: Idea 2 '''approved'''.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
::I changed the assignment a little bit. Can you please look at it? [[User:Ploo00|Ploo00]] ([[User talk: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. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

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

: '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:39, 19 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)
:: '''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:14, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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.

<nowiki>~~~~</nowiki>

[[User:Julian Bleyer|Julian Bleyer]] ([[User talk:Julian Bleyer|talk]]) 14:16, 18 December 2022 (CET)

: The only problem I see is that it looks too similar to the exercise we did in our class. If you will model a real building as you propose, you need to solve the problem of agent orientation - how it will navigate through the building. It was very simple in our exercise. '''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:54, 19 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/

[[User:Kane02|Kane02]] ([[User talk:Kane|talk]]) 14:10, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

: '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:57, 19 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

[[User:BortnikSvitlana|BortnikSvitlana]] ([[User talk:BortnikSvitlana|talk]]) 16:13, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

: Please, elaborate it a bit. I don't understand how the simulation should look like. Be more specific. Imagine that the assignment should be possible to solve by another person, so all the necessary information must be included. Building the model cannot be the goal itself, you have to solve an actual problem. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 23:07, 19 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

: Please, elaborate the assignment in detail. How will the simulation look like? What will be the limitatins, etc. etc.? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 00:34, 20 December 2022 (CET)

==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/

[[User:Ruzv01|Ruzv01]] ([[User talk: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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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.[[User:Ruzv01|Ruzv01]] ([[User talk: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'''.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 16:22, 19 December 2022 (CET)

<nowiki>~~~~</nowiki>

== Heating behaviour of households ==

Author: [[User:vala18|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

:: How will you derive the utility function? Based on what data/ parameters? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 16:24, 19 December 2022 (CET)

==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

:: OK, this assignment is heavily dependent on how you will derive the revenues and expeses for the next 10Y - document it well. Do not forget the net present value of the 44 billion USD (in 10Y they will have different value too - when placed in some alternative investment).'''Approved'''.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 16:30, 19 December 2022 (CET)

==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

: Im not sure what is the result you propose - individual player’s state-lines (not enough), forecast the game (might be interesting), or season results (interesting, but maybe too hard)? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 16:38, 19 December 2022 (CET)
: As I have mentioned, I will predict the game results by simulating players' statistics. If the sum of their points is greater than of other team, they would win. Based on the game results, it might be possible to forecast even the season. Yes, might be too hard, but main point is to predict the game score. [[User:prin03vse|prin03vse]] ([[User talk:prin03vse|talk]]) 21:18, 20 December 2022 (CET)

==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/

:: How will you determine the Probability of function having bugs to repair and Probability of having trouble developing function? Based on what data? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 16:33, 19 December 2022 (CET)
::: Initially, I wanted to make it so every difficulty of the component based on the Fibonacci sequence, would also be proportionally equal to probability of the function having a bug, e.g. difficulty 8, means 8% chance of having a bug, difficulty 40 -> 40% chance. I would either use a typical scrum poker sequence: 0, 1/2, 1, 2, 3, 5, 8, 13, 20, 40 and 100 or more accurate one to Fibonacci sequence: 0, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89 and then end on 100. If that would be a problem, I could also try to conduct a small survey which I could send to colleagues, friends working in other developing companies and programming students and base the data on the average opinion of programmers. [[User:Olim02|Olim02]] ([[User talk:Olim02|talk]]) 17:51, 20 December 2022 (CET)
::: It's a bit artificial, but that could do. One more thing - since you will use the Vensim, what will be the feedback loops? So far it reminds more calculation than dynamic system. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:03, 20 December 2022 (CET)

==Receivables Payment==

'''Author:''' kock06

'''Type:''' Monte Carlo simulation

'''Software:''' Python or Excel

'''Problem definition'''

In the current economic crisis, companies face issues of low likvidity more than ever. Because of that, prediction model for credit notes payment could be introduced.

'''Goal'''

Simulation can be used to predict liquidity of the company.

'''Model parameters'''

*ration of count of payed credit notes to count of all credit notes
*ration of sum of payed credit notes to sum of all credit notes
*terms of payment
*economic crisis probability

'''Method'''

Based on model parameters, simulation will be run to know, if the customers will pay credit notes in time or not

'''Dataset'''

https://www.kaggle.com/code/mishadey/b2b-invoice-payment-date-prediction-model/data

:: In order to be able to do, what your goal is, you would have to simulate whole cashflow of a company. Do you have data for that? What would be the random parameters and from what data you would derive their probability distribution?[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 09:11, 20 December 2022 (CET)

==Impact of conditional skills on the performance of boxing athletes==

'''Author:''' Abdullah Zahir

'''Software:''' Vensim or Python

'''Problem''' In boxing, it is difficult to plan the right training for each individual athlete. The training must be adapted to the weaknesses and strengths of the boxing athletes. It is unclear which conditional skills need to be trained in which ratio and how this affects the athlete's performance. For example, a lot of conditioning training tends to decrease strength and vice versa.

'''Simulation'''
It should be possible to create a profile for the athlete with his strengths and weaknesses. It can be entered how strong the athlete is physically and how advanced he is in terms of technical and tactical skills. There are correlations between the physical skills. For example, high agility affects the condition of the boxer. In the simulation, the skills are weighted according to their importance for boxing. The output should be the overall performance of the athlete, so that it can be tested which changes have an influence on him.

'''Goal'''
The goal is to find out what influence the athlete's conditional and technical skills have on his overall performance, in order to specifically adapt the training to his weaknesses.

'''Methode'''
The correlations are to be fitted into the equations so that the balance between the skills is sensibly given. For this purpose, research must be carried out from existing studies. For the individual parameters, there are different types of tests, such as the cooper test, to test and evaluate endurance. For the overall evaluation of the athlete, the weighting is adjusted specifically for boxing depending on the relevance of the skills.

'''Parameters'''
*Personal (weight class, age, mental strength)
*Conditional Skills (condition, strength, quickness)
*Experience (combat record: wins, losses, draws)
*Technical skills (offense, defenes)
*Tactical skills (standard situations)

'''Sources'''
*Sports textbooks: Bewegung, Training, Leistung und Gesundheit : Handbuch Sport und Sportwissenschaft / Arne Güllich, Michael Krüger
*Boxing literature: Managerboxen : gesundes Kampfsporttraining in der Praxis / Jürgen Fritzsche, Christoph Raschka

:: Please elaborate the simulation in more detail - what particular parameters you will simulate, what data you will derive the equations from, what particular literature you will use etc. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 19:57, 20 December 2022 (CET)

Assignments WS 2022/2023

2022-12-19T11:27:35Z

Prin03vse: /* Predicting NBA players’ statelines */

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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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== 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) ==
'''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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 21:47, 17 December 2022 (CET)

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

:: Idea 2 '''approved'''.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
::I changed the assignment a little bit. Can you please look at it? [[User:Ploo00|Ploo00]] ([[User talk: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. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
:: '''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:14, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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.

<nowiki>~~~~</nowiki>

[[User:Julian Bleyer|Julian Bleyer]] ([[User talk: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/

[[User:Kane02|Kane02]] ([[User talk:Kane|talk]]) 14:10, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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

[[User:BortnikSvitlana|BortnikSvitlana]] ([[User talk:BortnikSvitlana|talk]]) 16:13, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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/

[[User:Ruzv01|Ruzv01]] ([[User talk: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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:48, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

== Heating behaviour of households ==

Author: [[User:vala18|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

Assignments WS 2022/2023

2022-12-19T11:27:17Z

Prin03vse: /* Predicting NBA players’ statelines */

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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) ==
'''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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 21:47, 17 December 2022 (CET)

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

:: Idea 2 '''approved'''.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
::I changed the assignment a little bit. Can you please look at it? [[User:Ploo00|Ploo00]] ([[User talk: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. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
:: '''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:14, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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.

<nowiki>~~~~</nowiki>

[[User:Julian Bleyer|Julian Bleyer]] ([[User talk: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/

[[User:Kane02|Kane02]] ([[User talk:Kane|talk]]) 14:10, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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

[[User:BortnikSvitlana|BortnikSvitlana]] ([[User talk:BortnikSvitlana|talk]]) 16:13, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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/

[[User:Ruzv01|Ruzv01]] ([[User talk: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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:48, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

== Heating behaviour of households ==

Author: [[User:vala18|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

Assignments WS 2022/2023

2022-12-19T11:26:55Z

Prin03vse: /* Predicting NBA players’ statelines */

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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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== 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) ==
'''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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 21:47, 17 December 2022 (CET)

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

:: Idea 2 '''approved'''.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
::I changed the assignment a little bit. Can you please look at it? [[User:Ploo00|Ploo00]] ([[User talk: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. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
:: '''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:14, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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.

<nowiki>~~~~</nowiki>

[[User:Julian Bleyer|Julian Bleyer]] ([[User talk: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/

[[User:Kane02|Kane02]] ([[User talk:Kane|talk]]) 14:10, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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

[[User:BortnikSvitlana|BortnikSvitlana]] ([[User talk:BortnikSvitlana|talk]]) 16:13, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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/

[[User:Ruzv01|Ruzv01]] ([[User talk: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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:48, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

== Heating behaviour of households ==

Author: [[User:vala18|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

Assignments WS 2022/2023

2022-12-19T11:26:20Z

Prin03vse: For approval

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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) ==
'''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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 21:47, 17 December 2022 (CET)

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

:: Idea 2 '''approved'''.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
::I changed the assignment a little bit. Can you please look at it? [[User:Ploo00|Ploo00]] ([[User talk: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. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk: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

[[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)
:: '''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:14, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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.

<nowiki>~~~~</nowiki>

[[User:Julian Bleyer|Julian Bleyer]] ([[User talk: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/

[[User:Kane02|Kane02]] ([[User talk:Kane|talk]]) 14:10, 18 December 2022 (CET)

<nowiki>~~~~</nowiki>

==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

[[User:BortnikSvitlana|BortnikSvitlana]] ([[User talk:BortnikSvitlana|talk]]) 16:13, 18 December 2022 (CET)

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==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/

[[User:Ruzv01|Ruzv01]] ([[User talk: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? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:48, 18 December 2022 (CET)

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== Heating behaviour of households ==

Author: [[User:vala18|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