Assignments WS 2017/2018

Simulation Proposal (feld00)

I am going to simulate a public transport system. The idea came from travelling around Europe, being surprised how often public transport systems fail in large cities and still they are very expensive. Prague public transport is rare exception. Buses, trams and subway trains arrive with minimal deviation from planned arrival time usually in seconds. I believe that there is sophisticated simulation software behind this.

In order to simplify the task let’s presume that it is not about money. We are not going to optimize costs and incomes. The purpose of this simulation is to optimize:

• Numbers of transportation units (TU) needed

• Frequency of releasing TUs

• their arrival time

All stated above in order to prevent people queueing on public transportation stops and to prevent transportation units from crowding. Microsoft Excel and SimProcess if needed will be used to perform this simulation.

for Monte Carlo or Simprocess you need solid quality hard data - what particular solid resources would you bas the simulation on? In addition I think you simplify it so much into some artificial "Numbers of transportation units", that would make the simulation unusable. I would suggest something less ambitious - spacial simulation(agent based, simprocess) of one type of transportation based on real data, if you find some.Oleg.Svatos (talk) 15:49, 15 December 2017 (CET)

You are right, maybe I can do less ambitious project. What about evacuation from a building:

It will be a Agent-based simulation model. I will use NetLogo software to build an building enviroment, which will include walls and staff inside. Agents will randomly move around the building. If they colide with a wall they turn different direction. If they colide with a staff member, he will navigate them to the exit. Is that possible?

feld00 (talk) 22:35, 15 December 2017 (CET)

The program in NetLogo would be able to import bitmaps with a building blueprints (plan of the building) where escape doors and agents position will be specified by color blocks.

feld00 (talk) 18:00, 18 December 2017 (CET)

There were similar simulations in past. Please, find it, and explain how you will differentiate.

Random movement of agents does not make sense to me. You should model people, not zombies. Tomáš (talk) 01:04, 19 December 2017 (CET)

I found project "school on fire". Is that it? This would be simulation of any planned evacuation. You can upload any building plan. There would be also navigators guiding the agents to the nearest exits. Agents will move towards an exit (it will simulate an intuition), but wouldnt know the real path. Navigators and wall will guide them.

feld00 (talk) 18:00, 18 December 2017 (CET)

What is the goal of the simultation? Tomáš (talk) 01:59, 26 December 2017 (CET)

The goal is to find out how people will act in these kind of events.

If there are still concerns, maybe we can discuss it personaly to avoid this endless conversation and figure it out at once. I understand you may not be at office, we can do it on phone as well (606 439 385). Please call or comment if further discussion is necessary, Thank you.

feld00 (talk) 10:43, 27 December 2017 (CET)

Ok, if you can deal with real building layouts, it is ok (you will need to simplify it reasonably because in some cases, the simulations of real buildings of all possible cases could be tricky). Nevertheless, I don't think you are really able to analyze how real people behave, but you can discover what influences the safety of the building. Also consider that people can in some cases leave not just through regular exits, but through another way. E.g. windows. If you agree, you have it approved. Tomáš (talk) 16:32, 3 January 2018 (CET)

Simulation Proposal (xvatj00)

Software: Vensim

I am a contemporary gospel choir conductor (choir and full professional band). We organize more or less 3 concerts a year. It has been a long time since we released our last CD and now we would like to earn money to be able to start recording a new one. For this purpose, I would like to find out those factors (such as choir performance, band performance, tickets’ price, concert’s location, etc.) that influence potential audience when choosing a band to go see and how to improve them, and thus get more people to come to our concerts - and earn more money for the tickets and in general. I will use a survey to get data about people’s preferences. Xvatj00 (talk) 17:45, 27 November 2017 (CET)

For this kind of simulation you would need ritch historical data so that you would be able to find premises you would then build the equations on (and to be able to verify the model when you compare its results with the historical data). Unfortunately the survey will not help you to quantify the parameters and the number of concerts is really low to be usable for such simulation. I probabbly would suggest a different topic. Oleg.Svatos (talk) 18:43, 30 November 2017 (CET)

I can get data up to 10 years back. The number of concerts included only those concert that are organized by us only, but we perform in many other concerts as well. Do you have any suggestions how to make the simulation possible? Thank you. Xvatj00 (talk) 18:59, 30 November 2017 (CET)

You would have to be able to define parameters that determine the demand for the individual concerts, and based on the data quantify them and quantify their impact on the demand for a concert. Based on that one could then discuss how the concert and its content should be se up so that you get maximum profit out of it. That is not a really easy ...Oleg.Svatos (talk) 15:07, 6 December 2017 (CET)

Intersection Optimalization (NEW ASSIGNMENT)

Software: NetLogo

Almost every day, I walk by the intersection of Anglická and Bělehradská / Škrétova. During peak hours, there are traffic jams on only one street leading to the intersection, which I find interesting. Because of this fact, I would like to simulate the intersection in order to find out if the lights are really optimally set there, and potentially, find out the optimal setting of the intersection’s lights.

As I’ve already mentioned, there are lights directing the intersection. Also, there is a tram track on the Bělehradská / Škrétova street which goes straight, while most of the cars coming from Bělehradská street turn left. I will use real intervals of all of the lights from a chosen time during peak hours, and the number of cars and trams coming to the intersection (including their speed, direction, etc.). At first, I will set the lights to constant ticks according to the reality to simulate the real situation. After that, I will try to find out an optimal setting of the lights and evaluate, what the optimal setting is or if it meets with the reality. Xvatj00 (talk) 18:48, 8 December 2017 (CET)

Please, try to obtain real data. Approved. Tomáš (talk) 04:05, 12 December 2017 (CET)

Social media post (Amelievh)

Software: Netlogo

Nowadays social media is a hot topic, and a lot of recruiters and other business people use linked in to attract new employees or just to share their thoughts.

For my simulation, I was thinking about researching the reach of a social media post. Someone posts something on LinkedIn, and depending on the amount of connection and amount of sharing I want to check how many people you can reach with one post. I will try to find real-world numbers and make it a useful tool for the business world.

Simulations on social media are typically problematic, mostly due to the lack of real data. I would recommend to try finding, something else. Tomáš (talk) 04:11, 12 December 2017 (CET)

New proposal: gender pay gap

Software: Vensim

The gender pay gap is a difficult problem to solve because it is caused by different reasons (education, age, part-time working ...). These are main reasons, but all these reasons are influenced by other aspects and factors.I want to simulate these different reasons + influences in Vensim and work out the most effective solutions to reduce the pay gap. I would specify on 1 country because data is different per country. Easiest and most interesting for me is Belgium.

What particular literature and data will you base it on? Oleg.Svatos (talk) 12:07, 15 December 2017 (CET)

I wrote a paper on this topic for another course wherefore I found a lot of statistical data. The EU publishes statistical data about this topic and reasons for it. I came up with the idea after seeing the Vensim example of SchoolLife which also showed a lot of influence on your future career. Only googling 'Gender Pay Gap Belgium' give you already a lot of publications and statistical information on this topic. --Amelievh (talk) 12:20, 15 December 2017 (CET)

OK, approved, but you have to well argument the parameters and equations of the simulation (including the citations to accesable resources) in the report which has to accompany the simulation so that we can verify that it is based on real data and makes sense.

Simulation Proposal (A_V)

Software: NetLogo

We own a zoo. We have a huge kennel for hamsters. We have observed a strange behavior of the hamsters. When a female hamster gives birth to babies (usually up to 12), the mother may come under the pressure of nurturing each and everyone of them. After giving the birth a female hamster becomes weak and may die if does not have enough food and vitamins. Also when the mother is weak, she can not lactate milk for all of her babies. Since the quality of food provided by the zoo does not always satisfy the hamster, the mother eats her weakest babies to get extra protein to feed other babies, which increases the probability of survival of her and the rest babies. Another reason of the deaths of hamsters, as mentioned above, is the adequate quality of food. If the food does not satisfy the hamsters, they do not eat it and the food rots by polluting the kernel which leads to an increased number of hamster deaths. In the simulation I will focus on how much the food quality, the amount of food and keeping the kernel clean influences the number of hamster deaths.

Makes sense, however it is necessary to obtain real data. Implementation of some of them will not be easy. Approved. Tomáš (talk) 01:06, 19 December 2017 (CET)

Simulation proposal (hram00)

Software: NetLogo

Imagine a beach by the ocean guarded by several lifeguards. The warmer the ocean is, the more jellyfish come. If there is a lot of jellyfish in the water, people often get stung. If they get proper treatment in less then five minutes, the pain goes away quickly end they can enjoy the day on the beach. Otherwise they're mad for the rest of the day. In case someone is alergic, the situation can get critical. My simulation should serve as a support for decision how many lifeguard should be placed on the beach and how far from each other to provide the best services and ensure the highest satisfaction of people on the beach and make sure no one will die because of jellyfish sting. The Simulation will be simplified but based on reality. There are no real data about number of jellyfish in the water but there are data about number of people stung every day. Based on current experience I can make a simulation which is close to reality. So far there is one lifeguard each 200 meters, at the begining of the season when the water is cold 70 F there is 0-2 people stung in one lifeguard's area, in the hot days by the end of the season (water has about 85 F and air over 100) the numbers of people stung on each lifeguard stand go up to 100 a day and we know there are cases that it took too long to get help.

Approved. Tomáš (talk) 01:09, 19 December 2017 (CET)

Introduction

American beaches are often guarded by lifeguard to safe people’s life when drowning. But drowning is not the only problem on the beach. Very often lifeguards must deal with jellyfish. Their population increases with higher temperature. Sometimes in the hottest days of august the water needs to be closed as the number of people stung by jellyfish overcomes hundreds.

Used software: NetLogo 6.0.2 to create the simulation and get the results and Excel 2016 tu summarize the result and come to conclusion.

Problem definition

The problem that lifeguard employer needs to solve is quite simple: How many lifeguards should be on the beach and where should they be placed? When does the water become too dangerous to swim there and it needs to be closed for public? The less lifeguard he must pay for, the better for employer but he can’t afford beach patrons feeling unsafe. Concerning drowning, one lifeguard can cover 400 meters of a beach but is he able to help all people stung by jellyfish in this area in time? When you get stung by moon jellyfish and take proper care of it within 5 minutes, there is high chance the pain goes away in next half an hour. But if you don’t treat it in time, the poison gets deeper under your skin and it can be painful for days. In such case beach patrons become really unhappy and most likely don’t come for holiday to this beach again. That’s why the lifeguards try to provide the best and fastest care as possible. This simulation is supposed to help employer decide about number of lifeguard on the beach in certain conditions and about the conditions leading to closing the water.

Background information

From years of experience it’s known that in temperature under 70 °F there are no jellyfish in the ocean (or just very few). As the temperature goes higher so goes higher the number of jellyfish. In temperature over 100 °F there might be hundred people stung by jellyfish on each 200 meters of the beach whereas in 80 °F there are just units of people.

Model

Model consist of 4 turtles (patrons, lazy-patrons, jellyfishes and lifeguards) and 2 kinds of patches (beach and ocean). Simulated world is 400 patches wide and 200 patches high which represents 400 meters of a beach (1 patch = 1 meter in reality).

Patrons and lazy patrons

To make the model realistic, there must be to kinds of people. In real world (USA) about 80 – 90% of people stay all day on the beach and don’t even enter the water, 10 – 20% of people is active and play in the water most of the day. That’s why there are “patrons” in the model, who can move all over the simulated world and their movement is random until they get stung by jellyfish. Each patron has defined step-size, rescue-time (countdown to the moment, when person’s injury gets serious) and chosen-guard (the nearest lifeguard the person will seek in case of getting stung). The rest of total amount of people is called “lazy-patrons” and they are just sunbathing on the beach all day and don’t move at all (they don’t play a role in simulation, just making the model looking like reality).

Jellyfishes

Purple creatures randomly moving in the ocean. Their number depends on the temperature.

Lifeguards: there are two lifeguards with fixed y coordinate (they are still on the beach) and adjustable x coordinate. If you set x coordinate the same for both guards, it works as if there is only one guard on the beach. They do not move during the simulation.

Ocean

Ocean is represented by blue patches and it covers ¾ of simulated world.

Beach

Beach is represented by light yellow color and it is situated in top quarter of simulated world. Anytime a jellyfish gets to the beach it dies.

Except of the turtles and patches, there are some global variables used in the model to countdown the time (simulation represent one lifeguard working day which is 9 hours = 32400 seconds), to count all people stung by jellyfish and number of seriously injured people. It also contains several procedures to make people and jellyfish move, to make people go for help and be helped and also some reporters.

Results

I’ve simulated 4 different combinations of temperature (85°F (normal), 120°F (extreme)) and number of people on the beach (500 (week day) and 1000 (weekend)) and compared the results with one lifeguard located in the center of a beach and 2 lifeguards evenly distributed on a beach. The results are summed up in following table:

It is obvious that having two lifeguards is safer but considering the price for second lifeguard there might be days when one lifeguard is enough. Let set some limits of people stung and seriously injured that are acceptable. These limits can be absolute or relative.

Absolute limits:

• Number of people stung by jellyfish <= 100

• Number of people seriously injured <= 10

Relative limits:

• Share of people stung by jellyfish from all people on the beach < 20 %

• Share of people seriously injured from all people stung by jellyfish < 20 %

• Share of people seriously injured from all people on the beach < 1 %

When we apply these limits on our result (highlighted in the table) we come to the following conclusion.

Conclusion

In the temperature 85°F during the week days it’s enough to have one lifeguard on the beach, the number of people stung is quite low and so even the number of those who are seriously injured is alarming. During the weekend and other busy days, there should be 2 guards even if the temperature is low. In extremely hot weather it gets much worse. Even if the beach is not too busy it still needs 2 lifeguards and in really busy days it might be better to close the water and only let people stay on the beach or swim in swimming pool nearby because the danger goes to high.

Code

File:Hram00 ocean.nlogo

Hub airport (yaua00)

Software: NetLogo

As I study not in my home country I have to use plane quite often to get home and sometimes I have to go to a hub airport and change the plane there. So in my simulation I want to show the phenomenon of hub airports. Hub airports are used by one or more airlines to concentrate passenger traffic and flight operations at a given airport. They serve as transfer (or stop-over) points to get passengers to their final destination. The simulation will start with the certain amount of the airports and airplanes will appear at random locations. Airplanes will find a random airport and fly to it, leaving trails on screen to show their paths. Over time new airport and a new airplane will be built. The airports that have existed for the longest will obviously already have the greatest number of airplanes flying to them, but the goal of the simulation is to see when over some time after new airports are built, if the new airport is going to get more planes and if there is going to be new hub airport. Yaua00 (talk) 23:21, 12 December 2017 (CET)

I am afraid this does not make sense to me. First, the popularity of airports is not random and does not depend just on the time the airport exists. Second, "flying to random airports" - what would it mean? Third, in order this should make sense, the simulation would need to be much more complex.

Please, try something else. Tomáš (talk) 01:13, 19 December 2017 (CET)

Government Policies and Its Influence to Economy (xbilr00)

Software: Vensim

I would like to simulate an impact of government decision to a economy. The government will have few tools which can be fully influenced by political decisions (government spending, tax rates etc.). Based on this I will simulate an impact of these decisions to economic indicators (GDP growth, state budget incomes, inflation, state budget saldo etc.). This simulation should reflect a great complexity of each decision and its impact even to indicators which you will not imagine on the first sight.

This is very complex - the simulation has to make sense so the question is what particular literature and particular models will you base it on? Oleg.Svatos (talk) 20:46, 14 December 2017 (CET)

Ok. So what if only one variable would be a tax rate? I will define the exact rules in a special document.

This is pretty complex topic so it cannot be done on just spontaneous basis, you either have to have a reasonable model based on some solid literature, you would base the simulation on, or try something else.Oleg.Svatos (talk) 15:33, 15 December 2017 (CET)

Ok. My simulation would be based on neoclassical economy which is saying that lowering taxes boosts the economic growth. On the other hand it decreases state budget income but the economic growth should cause a contradictory effect. I would like to prove this theory on real numbers. I decided to focus in on Czech economy. I will use the data published by Czech Statistical Bureau about tax rates, GDP growth and state budget incomes. Of course there will be a little generalization but it should reflect the real trends. Based on this I will build my simulation. Is it ok now or should I rather find something else?

You can approch it this way, but then it is task for Monte Carlo - find the sensitivity of individual parameters on each other (based on the data and theory you specified) and then create a simulation in Excel. Agreed? Oleg.Svatos (talk) 12:21, 16 December 2017 (CET)

Ok. Is it approved from your side? I will probably visit you in January during your consultancy hours.

Since you agree, approved Oleg.Svatos (talk) 20:31, 16 December 2017 (CET)

Influenza A virus subtype H1N1 (alrw00)

Software: Netlogo

I would like to simulate the spread of the virus H1N1 via the breathed air in a small isolated population. The model should examine the emergent effects of aspects of a polluted air and human contact. The user controls the population’s tendency to practice abstinence, the amount of time a person in the population will stay healthy. Exploration of the first and second variables may illustrate how changes in human behavior in the isolated population contributes to increases in the prevalence of H1N1 as transmitted diseases, while exploration of the third and fourth may provide contemporary solutions to the problem.

Approved. Tomáš (talk) 01:14, 19 December 2017 (CET)

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- Project Name:Influenza A virus subtype H1N1

- Class: 4IT496 Simulation of Systems (WS 2017/2018)

- Author: Wahida Al-Rawahi

- Model Type: Agent-Based Simulation

- Software Used: NetLogo

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Section 1: Problem Definition

H1N1 flu is also known as swine flu. It's called swine flu because in the past, people who caught it had direct contact with pigs. That changed several years ago, when a new virus emerged that spread among people who have not been near pigs. The World Health Organization proved that H1N1 virus can spread around the same way as the seasonal flu. When people who are already infected with the virus cough or sneeze, they spray tiny bits of the virus into the air. Therefore, when a healthy person comes in contact with these bits by touching a surface of where the bits have landed, or touching something an infected person has already touched, the H1N1 virus has be transmitted easily.

1.1 Goal

To simulate the transmission and perpetuation of the H1N1 virus in a human population.

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Section 2: Method

The environment used to build the simulation is NetLogo 6.0.2. It was chosen due to the nature of the problem and the ease of visualizing how the virus is moving from one to person to another. Another reason for picking this software is to get quick results and have a flat learning curve.

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Section 3: Interface

The model is initialized with number of people, of which a certain amount are infected. These people move randomly in the model with one of three conditions:

1. Healthy but vulnerable to be infected with the H1N1 virus and they are visualized in the color green.

2. Infected and infectious are visualized in the color red.

3. Healthy and immune are visualized in the color gray.

Moreover, people may die of infection or old age. This is clearly illustrated when the population slopes below the environment’s “carrying capacity” and healthy people may produce healthy, but prone children.

3.1.1 Sliders

1. “pick-number-of-people” slider: To determine the number of initial population.

2. “Infections” slider: To control how high or low the chance of the H1N1 virus transmission will occur when an infected person and vulnerable person occupy the same area.

3. “chance-of-recovery” slider: It controls the likelihood that an infection will end in recovery or immunity.

4. “duration” slider: It determines the number of weeks before an infected person either dies or recovers.

3.1.2 Buttons

5. “setup” button: It resets the display, monitors, and plots and randomly distributes “pick-number-of-people” in the display. All but 10 of the people are set to be green vulnerable people and 10 red infected people, all of randomly distributed ages.

6. “go” button: Starts running the simulation.

3.1.3 Choosers

7. “turtle-shape” chooser: User can pick whether individuals are visualized as persons or circles. The purpose of this is to easily distinguish the colors and to see individuals who are health, immune, or infected.

3.1.4 Monitors and Plots

The first two monitors (percentage-of-infected-individuals, percentage-of-immuned-individuals) show the percent of the population that is infected and the percent that is immune. The third monitor (number-of-years) shows the number of years that have passed. The plot shows the number of vulnerable, infected, and immune individuals. It also shows the number of individuals in the total population.

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Section 4: How the Simulation Model Works

This section describes how the simulation runs when the execution button is pressed. The factors of the above section is explained below on how each person is treated in the simulation model:

1. Population Density: It affects how often infected, immune and vulnerable individuals come into contact with each other. The size of the initial population can be changed through the “pick-number-of-people” slider.

2. Population Turnover: When some of those people die, some of them will also die infected with the H1N1 virus, as well as vulnerable or immune. Those who die are replaced with the new born who are also vulnerable to the virus. People may die from the virus, the chances of which are determined by the slider “chance-of-recovery”, or they may die of old age. In this simulation model, people can die of old age at the age of 50 years and reproduction rate is constant. If the carrying capacity has not been reached, every healthy person has a 1% chance to reproduce.

3. Immunity Degree: If the individual has been infected with the H1N1 virus and has successfully recovered from it, then he/she is immune. In this simulation model, the when the person is immune it lasts for a year.

4. Infections: This is determined by the “infections” slider, where a user can pick how high or low the virus spread.

5. Infection Duration: This can be determined by the “duration” slider by picking how long is a person infected before he/she either recovers from the virus or dies. The time length is the virus’s opportunity for transmitting to a new host.

4.1 Inputs Embedded into Source Code

1. Lifespan = 50 years

2. Carrying Capacity = 300

3. Immunity Duration = 52 weeks

4. Birth Rate = 1 in 100 of reproducing when the number of individuals is less than the carrying capacity

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Section 5: Results and Interpretations

The aspects selected on the sliders interrelate to influence the likeliness of the H1N1 virus to spread in the population. However, the aspects selected must create a balance in which a sufficient number of potential hosts remain available to the virus to access. What makes the simulation interesting is the fact that there will frequently be an explosion of infection since no individual in the population will be immune. At first, the virus is very powerful and can successfully infect everyone. However, it may not survive in the long-term. And since every person who is infected generally dies or becomes immune, as a result, the potential number of hosts is every so often limited. The exception to the above is when the “duration” slider is set so high that reproduction can provide new hosts.

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Section 6: Conclusion

It is concluded that when there are no immune individuals in the population, it can lead to approximating of the plague of a viral infection in the population, one that has devastating consequences for the humans concerned. Before long, however, the H1N1 virus becomes less common as the population dynamics change. What ultimately happens to the virus is determined by the factors controlled by the sliders (and can also be applied in real life situations).

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Section 7: Code

Rumour has it --Mashal (talk) 23:36, 15 December 2017 (CET)

Sotware: NetLogo

So, this simulation will be interesting for someone who wants to rule the world (who doesn't?). This is representation of dictatorial regime. There is one rumour, that can turn down this regime (not by itself, just people will go for revolution), so it's dangerous and government doesn't want it be heard. There are three kinds of agents there: POLICE, PEOPLE and WHISPERERS. Police try to find and catch the whisperers. Whisperers try to tell a rumour to people. The one from people group who hears the rumour becomes a whisperer. If one policeman can't find a whisperer for a long time government kills him (because he is either unprofessional or against the government). If the whole people become whisperers regime will lose. If the police will catch and kill all whisperers regime will win. I want to see how all this system depends on the number of police, whisperers and people.

I have two problems: first, spreading rumors does not necessarily mean a regime would fall. In the 80. in Czechoslovakia, no one trusted in the communist government, hovewer the regime didn't fall until multiple appropriate conditions occured. The main problem, nevertheless, is that this simulation is unverifiable. Your results will be completely artificial, you will struggle to prove they make any sense. Tomáš (talk) 01:21, 19 December 2017 (CET)

Isn't it the main problem of all social simulations? Okay, what about the model explores the stability of predator-prey ecosystems?

Generally, falsifiability is a critical property to call anything science. If you have no way how to prove something is false, it's magic. You are right that social science often works with softer problems, but still, it means just that you should formulate the problems more carefully.

If you don't like it, i can take my last simulation idea. I took that course last year and i had approved simulation idea, but i failed the final exam, so i didn't make this simulation. Can i take it?

Courier is coming to town

There's a small village with certain number of houses and it doesn't have its own local delivery service, thus once a week a delivery person drives up to this village from a bigger city. Each time this person has a different delivery list and his route is based around that list. If for some reason nobody was able to receive a package at the time that the delivery person was there, he has to come back to the same address until the package is delivered. The simulation in NetLogo will be creating the perfect route for the delivery person based on A* search algorithm, key parameters are the number of times, when delivery person must return to the house and the number of houses.

Approved Tomáš (talk) 01:10, 22 December 2017 (CET)

Multilingual community simulation (xlusm05)

Software: NetLogo

Linguists are concearned that half of the existing languages will be extinct by the end of the 21 century due to globalization. There are different factors that affect the language’s viability, most importantly: total number of speakers, trends for increase or decrease in the number of speakers, transmission to younger generations, its usage on the level of administration and education or only in informal domestic environment. I would like to create a simulation of a multilingual community where all the above factors are known and then apply this model on real data of a particular country. The goal is to define if some language is endangered and will soon become extinct.

Approved. Tomáš (talk) 01:22, 19 December 2017 (CET)

Simulation proposal (Ewoud Stroom)

Software : NetLogo

The catch of many commercial fish species in the North Sea has decreased over the past decades. Many fish stocks have shrunk as a result of overfishing. In addition, fewer fish are caught by catch limiting measures. The most important measure is the total allowable catch, that is used by all the countries that can fish in the North Sea. We can use data from ICES ( International Council fort he Exploration of the Sea). The TAC gives for every sort of fish the total number of fish that can be captured by all the European countries together in an fishing area. My intention is to create a simulation of a fishing area and based on historic catches of 1 kind of fish, determine how much of this fish there can be captured every day before it becomes extinct. There are different factors that influence this : the number of fishing ships in the fishing area, the total number of this sort of fish in the area, the procreation of this fish every day and the amount of fish every ship can capture every day depending on his capacity. First I would like to create a simulation where all these factors are known, and then apply this model on the real data of the North Sea. The goal is to define when a fish sort will soon become extinct.

The topic sounds good to me, I am just not sure if NetLogo is the proper platform for you. If I understand it well, Vensim should be the platform of choice. Or, please, explain, why NetLogo. Tomáš (talk) 01:25, 19 December 2017 (CET)

       I thought if I choose a hypothetical fishing area, NetLogo would give me the most visual opportunity to work with?

That's probably true, however, the visual representation isn't everything. I don't see any need for agents. Just values and probabilities. That's why NetLogo is not the best one. It is like if you would say that you prefer writing documents in Photoshop, because it makes nicer letters. Tomáš (talk) 01:37, 22 December 2017 (CET)

Okey, I understand. What do I have to do then to make it in NetLogo? Or can I just make this proposal in Vensim then?

Depends on you, for Vensim it is OK. Oleg.Svatos (talk) 00:06, 28 December 2017 (CET)

Simulation proposal (yaua00)

Software : NetLogo

Imagine a typical school classroom with the tables standing in rows and columns. And when the school year starts students have to choose with whom the will share a desk for the rest of the year. In real life there is a range of different criteria to choose a buddy, but in this simulation the criteria for the choice are similar abilities and popularity. But the more pairs are already created – the less choice the other students have, and they become less picky and accept a student they would not accept before. Over the time all the students will be paired, and I want to follow how the difference in abilities and popularity in pairs changes over time. Yaua00 (talk) 13:51, 20 December 2017 (CET)

First, I don't see the clear point of the simulation. And, it would be very difficult to verify it. Please, try something else. Tomáš (talk) 01:45, 22 December 2017 (CET)

Proposal Ahmr00

Software: Netlogo

I would like to simulate a small version of food chain theory. In the simulation there will be a predator - wolves and the prey - rabbits and they will wander randomly around the landscape. For each of them every step costs the energy, and they must eat something (in this case wolves will eat rabbits and rabbits carrots) in order to replenish their energy - when they run out of energy they die. This simulation will help to see population dynamics in such environment.

What is the diffence between this and Predator and Prey? Tomáš (talk) 02:14, 26 December 2017 (CET)

I can make this simulation more complex by adding an extra animals and that's will make a chain theory more complicated, or If it not sufficient enough I can change the whole topic to completely new one.

Please, do. And in order to speed it up, let me know on email as soon as you update the topic. Tomáš (talk) 16:01, 3 January 2018 (CET)

Proposal mamo00

Public Park.

Software: NetLogo

There is a little forest inside our city, which we want to open to public as Park. There are pair of sick deers among all of our deers which are behave aggressive. This disease can taint through air when deers are close to each other. There are several hunters work for us in this forest. We have three types of turtles namely deers, sick deers - namely xdeers and hunters in this model. Deers and xdeers eats grass in the field to gain energy, because of their disease xdeers get less energy than normal ones and sick deers can transmit their disease to another deers. Deers can reproduce but, xdeers because of disease can't reproduce. The main duty of hunters to find out xdeers and remove them from forest, so healthy deers can live there and reproduce and we can open this forest as Park for public.

This model will help us to observe population of healthy and sick deers in the forest and how many hunters we need for each scenario.

I don¨t see any point in this simulation. Please, try to find something real. Tomáš (talk) 02:15, 26 December 2017 (CET)

I changed proposal and make it more realistic. I also can add predators to this model, but it will change all concept of public Park.

I still don't see how you can verify the model. The situation seems to be totally artificial, it means unverifiable. In fact, it is just an adjusted version of predator-prey problem. Please, try something else and in order to speed it up, let me know on email. Tomáš (talk) 16:08, 3 January 2018 (CET)

Proposal Vladislav Zotov(zotv01)

I propose a simulation of a hospital department(ophthalmologist, as i have to wear glasses and spent some time there) using Anylogic software.

My goal is to analyze work of said department and determine if all the processes of taking care of patients are optimal. I will simulate physical placement of rooms, how people move there, time needed to complete the examination and so on to get clear representation of business state.

The result of the simulation will be a set of recommendations, e.g. if ophthalmology will need to expand, and raise prices or something else to optimize patients examination and bring business to the full potential.

What is the source of data you will use? Tomáš (talk) 02:17, 26 December 2017 (CET)

As of physical layout I don't think I can find any documents to back this up. But I've been there many times so average time for examinations and movements - from personal experience, pretty much. Other data that can be useful i will retrieve from their website. Zotv01

Approved. Tomáš (talk) 16:10, 3 January 2018 (CET)

Simulation proposal (yaua00)

I want to propose a simulation of a food chain. In this simulation I want to have the populations of plants, deer and wolves. Deers and wolves will move randomly over the surface and try to get food. Dpending on the given condition the population of deers and wolfs increases or decreases. The plants are all over the area are renewed. Moreover, I want to add a human in my simulation and the influence of man will included in these relationships in two ways - hunting and agriculture. I want to set conditions like- energy which animals gain from food, how long does the plant grow, the amount of deers and wolfs, how much energy does the person gain from the meat, reproduction speed, and the amount of hunters/agriculturers. The aim is to show if the ecosystem is stable or unstable with human intervention. Yaua00 (talk) 14:13, 24 December 2017 (CET)

What is your simulation good for? The parameters will be completely arbitrary... Tomáš (talk) 02:19, 26 December 2017 (CET)

Firstly I want to find out with which parameters the system will be in balance and then add a person there to see how it influences ecosystem. In such a way I can see how many people I can add to such an ecosystem before it stops existing. If the population starts to decrease it means that this area should be protected and hunting in this area should be forbidden. Yaua00 (talk) 15:43, 26 December 2017 (CET)

Sure, I understand. However, this is in fact just another version of predator - prey model. By the way, I think third or fourth just in this semester. It was already elaborated many times. For me, it could have sense in two cases: 1) if you make a considerable improvement of the model. The considerable improvement does not mean just renaming of the agents and adding one or couple more, it means a fundamental change of the concept that moves it forward. And it is not that simple to find, to be honest. Or, 2) another acceptable case is if you use the model to demonstrate a real situation. In that case, you, however, need to use real parameters, numbers, etc. Please, try something else and let me know by email to speed thing up. Tomáš (talk) 16:23, 3 January 2018 (CET)

Simulation proposal (yaua00)

I want to simulate a model that will show the transmission and perpetuation of a virus in a human population. The model will be initialized with N people, of which N are infected. People will move in one of the states: healthy but susceptible to infection, sick and infectious, and healthy and immune. People may die of infection or old age. I want to follow the changes in the population if the following factors are used: the chances for recover from the virus, degree of immunity, infectiousness, duration of infectiousness and immune time. Information about most of the factors is available on the internet. Yaua00 (talk) 14:19, 12 January 2018 (CET)

1. Introduction

This simulation shows the spread of the virus Chickenpox via person-to-person transmission in the isolated population. It analyzes effect of vaccination on the dynamics of an infection with a person-to-person transmission and development of the immunity to the disease.

- Simulation Name: Virus Chickenpox

- Author: Yauheniya Andreyuk

2. Problem definition

Chickenpox, also known as varicella, is a highly contagious disease caused by the initial infection with varicella zoster virus (VZV). Chickenpox is an airborne disease which spreads easily through the coughs and sneezes of an infected person. The condition usually resolves by itself within a couple of weeks. The rash may, however, last for up to one month. After a chickenpox infection, the virus remains dormant in the body's nerve tissues. The immune system keeps the virus at bay, but later in life, usually in an adult, it can be reactivated and cause a different form of the viral infection called shingles.

3. Method

NetLogo 6.2. was used for this simulation. It was chosen due to the ease of visualization and quick results on a learning curve.

4. Model

4.1. People

The model contains only one turtle, which is humans. People in the simulation are divided into three groups – healthy, sick or immune (not at the beginning of the simulation, but later on). If a human is sick, he may infect other people he comes in contact with. When the person reaches a definite age he dies and is no longer relevant to the model. After the person was sick, but recovered, he also gains the immunity. People are set to move randomly.

4.2. Other sliders

Infectiousness - capability of causing infection;

Vaccination-rate – percentage of vaccinated people;

Immune-time – amount of time for which the person gains immunity;

Duration – how long the person will be sick before he recovers or dies;

Chance-recover - probability that the person will recover or gain the immunity.

Results

I want to follow how the results will vary with the vaccination-rate changing.

Case 1.

Vaccination-rate = 0.

In this case we can see that population gets sick and recovers in a natural way. Without the vaccination up to 98% of the population gets sick, but recovers afterwards, but then the circle happens once again. And not surprisingly this cycles influence a lot the amount of people dying.

Case 2.

Vaccination-rate < 10.

In this case the amount of immune people reaches up to 82% and the amount of people leaving in the area is quite stable. The amount of infected people doesn't reach even more then 12%.

Case 3.

Vaccination-rate = 50.

In this case we can see that the amount of immune people is way bigger then infected individuals (as monitors show - 0.57 infected and 98.86 immune).

Conclusion

It is clear from the results that the vaccination rate influences a lot the amount of immune people and the amount of sick/dying people. So it is concluded that more people care about the vaccination - more immune people are there in society - less people spread the virus and therefore less people die. For me, the vaccination rate was the most important parameter in this simulation, but changing the other parameters at the same time may give different results. The simulation has already a lot of parameters defined, but of course there is a space for improvements, for example - not always the symptoms are obvious at the very beginning, the extension to this simulation could be spreading disease without people even knowing it and capturing the amount of people dying when it is already too late to do something.

Code

File:Semestralni priklad.nlogo