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MIS 643 Agent-Based Modeling and Simulation 2016/2017 Fall Chapter 4: Creating Agent-Based Models

This chapter explores the process of designing, building, and examining agent-based models. It covers topics such as choosing questions, agents, behaviors, and measures, as well as the iterative refinement of the model. The Wolf Sheep Simple Model is used as an example to demonstrate these concepts.

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MIS 643 Agent-Based Modeling and Simulation 2016/2017 Fall Chapter 4: Creating Agent-Based Models

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  1. MIS 643 Agent-Based Modeling and Simulation 2016/2017 Fall Chapter 4: Creating Agent-Based Models

  2. Outline Intorduction Designing s Model Building a Model Examining a Model Predator-Prey Models: Additional Context Conclusion

  3. Introduction • This chapter is based on • IABM-WR: Chapter 4 • Creating Agent-Based Models

  4. Introduction • This chpapter: • Devising a question or area to explore • designing and building a model • refineing question • revising the model to analize results to answering the question

  5. Introduction • iterative exploration and refinment of the model and motivating question • Three sections: • 1 – Designing a model: • what elements to include • 2 – Building a model • from conceptual to a computational object • 3- Examining a model • running, creating and analizing results to provcide a useful answer to the motivating question

  6. Introduction • In this chapter • desining, building and examining • Wolf Sheep Simple Model • The basic quesiton addressed: • How do population levels of two habitat-shering animal species change over time? • Generalized: • companies competing for customers • electral parites competing for wotes • viruses evolving in a computer system • Basic components of most ABMs

  7. Outline Intorduction Designing a Model Building a Model Examining a Model Predator-Prey Models: Additional Context Conclusion

  8. Designing a Model • Choosing Questions • Choosing Agents • Choosing Agent Properties • Choosing Environmental Characteristics and Stationary Agents • Choosing Agent Behavior • Designing a Time Step • Choosing Parameters of the Model • Choosing Measures • Summary of the Wolf Sheep Simple Model Desing

  9. Designing a Model • Two major categories of modeling • phenomena-based modeling • exploratory modeling

  10. Phenomena-Based Modeling • begin with a target phenomena • characterisitc pattern – reference pattern • E.g.: • common housing segregation patterns in cities • spral shaped galaxies in space • oscilating population patterns of interraction speices • goal: to somehow capture the reference pattern

  11. Phenomena-Based Modeling • In ABM: • finding agents and rules of behavior and interrection so as to generate the observed patterns • once generated: candidate explanatory mechanism for that pattern • vary parameters to see • other patterns can be emereged • find in data or by conducting experiments • In EBM: writting equations that wiill give rise to the reference pattern

  12. Exploratory Modeling • less common in EBM then in ABM • greate agents, • define their behavior • explore the emerging patterns • may be • abstract forms; Game of Life • similaritiies • behavior of the model and • phenomena in the world • refine the model in the direction of perceived similarities to explanatory modeling of the phenomena

  13. Degree of Questions • Specific research question(s) • E.g.: • How does a colony of ants forage for food? • -How does a flock of geese fly in a V-shaped • other extream without a clear question • modeling ants or birds • gradually refine question • a specific one answered by a model

  14. Combining conceptual model and coding • top-down design • conceptual model then coding • designing agents, behavor and environment • before any line of coding • butom-up design • conceptual model and coding coevolve • start with a phenomena – with or without a formal queswtion • E.g.: how a market evolves • start with sellers and buyers then add brokers

  15. In practice • modelers use rearly use one style • switch between forms and styles • collaborating with a programmer • top-down design approach

  16. Top-Down Design Approach • phenomena specific RQ • design agents  properties, rules, • conceptual model • revise and refine until at a fine level

  17. ABM Design Principle • ABM Design Principle: • start simple and build towards the question you want to answer • Two components

  18. First Aspect • begin with simplest agents and rules to explore the system • A. Einstein (1933) • “Every thing should be made as simple a possible but not simpler”

  19. Second Aspect • keep your quesiton in mind • do not add anything in your model that does not help you in answering your question • G. Box (1979) • “All models are wrong but some models are useful” • all models are incomplete – simplify some aspects of the world, • some are useful because they are designed to answer particular questions and the simplifications in the model do not interfere with the answer

  20. ABM Design Principle Useful • 1- examine every possible agent and rules • eliminate if progress can be made without it • Starting simple and adding elements slowly – those irrelevant components never appear

  21. More understanable and easier to varify • Verification: a computational model correctly implements its conceptual model • just in time results • at every point in model development • the model give same answer about the

  22. Choosing Questions • first research question • may be too general • may change as new versions of the model are developed • for SWSM • How do population levels of two habitat-shering animal species change over time? • is is appropriate for ABM or refine it for ABM

  23. Choosing Questions • ABM: interacting, autonomous, heterogoenus entites • Some questions are more amenable to ABM some not • so try other modeling approaches • E.g.: examine population dynamics of two species, well mixed (no spatial dependence), homogenous, change depends on only levels of each • system dynamics more appropriate

  24. Choosing Questions • ABM more relevant • how animals use resources – food at specific locations to get energy • Aggregates depends on agents interractions with themselfs and their environments • start simple only prey with a simple environment • add resources, predator-prey interractions • Time dependen processes

  25. Refined Question • suitable for ABM • can we find model parameters for two species that will sustain with positive population levels in a limited area when one is a predator the other – prey – consuming resources from the environment.

  26. Example • some times reference pattern is observed or • from the rules examine generated patterns • One reference pattern: wolf-moose species, Isle Royale, Michigan • Figure 4.2, 1959 – 2009 • no population going extinct, but oscilating • a phenomena-based modeling • RQ refined: can we find oscilating patterns of two species without going to extinct in a limited area

  27. Choosing Agents • after research question • what are the agents? • compoents of the model: automomous, heterogenous, having states, properties and behavior and bearing on the quesiton • Avoid agent overload – any component could be an agent • more relevant to RQ

  28. Choosing Agents • Granularity of agents: every entity is composed of smaller levels of entities • what is the rigth level of entiy to choose • proto-agents: in future versions of the model candidates to be agents • not individual characteristics but global • E.g.: a humman hunter – affects the two species. • E.g.: WSSM-wolf ,sheep, grass • added: hunter or soil

  29. Choosing Agent Properties • properties distinguish agents from others • E.g.: in WSSM • wolves and sheep: location, heading and energy • notion of vitality, abstracting messy details of methabolism, caloric storage or starvation • condencing all to a single measure • other properties may be added but not related to the RQ

  30. Choosing Environmental Characteristics and Stationary Agents • Environment: mobile agent live and interact with • E.g.: in WSSM • grass sheep consume • not every blade of grass • patches – grass amount • granularity shold be choosen properly • Not the deal with boundary conditions • the world will wrap verrically and horizontally – torus

  31. Choosing Agent Behavior • behaviors of agents determine how they interact with each other and environment • E.g.: in WSSM, wolves and sheep • common behavior - turn, move forward, reproduce die • differs – sheep consume grass, wolves consume sheep • may have many other common or differing behaviors – but not relevant to the RQ

  32. Choosing a Time Step • designing the time step • all behavior of agents • in what order in a time step • in ABM time discrete • each step is divided into serialized, ordered phases • determining the order may be tricky

  33. Choosing a Time Step • In WSSM wolves and sheep have the behavior:move, die,eat, reproduce • grass growhs • order for animals arbitary – any order • movement – truning and stepping forward • costs energy • death – if no energy • eating – to store energy, if something in their local envirnoment to eat • reproduce – if hes enough energy, which consumes energy • The grass agents may grow • before next time step

  34. Choosing Parameters of the Model • agent behavior with specified rules • parameters control the model • In the WSSM • initial number of sheep and wolves • how much energy it costs an egent to move • energy gained from eating • from grass or sheep • how much energy is needed to reproduce • grass regrowth rate • not included in WSSM

  35. Choosing Measures • What measures to be collected from the model • what measures – RQ • most relevant to the RQ • In WSSM – most relevant measures: • population levels of sheep and wolves • relevant to RQ: what parameters sets enable two populationns oscilating together • other may not be so relevant – energy • Some may be added for debuging the model

  36. Summary of the Wolf Sheep Simple Model Design • Driving Question: Under what conditions the two species sustain oscilating positive levels in a limited geographical area when one is a predator of the other and the other consumes limited but regenerating resource from the environment • Agent Types: sheep, wolves, grass • Agent Properties: location, heading, energy (sheep,wolves), grass amount (grass)

  37. Summary of the Wolf Sheep Simple Model Design • Agent Behavior: Move, die, reproduce (sheep and wolves), eat sheep (wolves), eat grass (sheep), regrow (grass) • Parameters: number of sheep, number of wolve, move cost, energy gain from grass, energy gain from sheep, grass regrowth rate • Time Step: 1-sheep and wolves move 2-sheep and wolves die 3-sheep and wolve eat 4-sheep and wolve reproduce 5-grass regrows • Measures: sheep and wolve population over time

  38. Top-Down Design Approach • Take notes and dates of versions • Answer the following quesitons: • What part of the phenomena would you like to build a model of? • What are the principle types of agents involed in the phenomenon? • In what kind of environment do these agents operwate? Are there environmental agents? • What properties do these agents have? (describe by agent type) • What actrions or behavior can these agents take? (describe by agent type) • How do these agents interact with the environemt and with each other? • What do you hope to observe from the model?

  39. A more buttom-up Approach to Modeling • Start with an ecological problem • add sheep – spread around • then they move, eat, reproduce, die • they eat grass • grass should regrow • something should eat sheep – wolves • come to the research question

  40. Outline Intorduction Designing a Model Building a Model Conclusion

  41. Building a Model • First Version • Second Version • Third Version • Forth Version • Fift Version

  42. Building a Model • Fife iterations • Many times in ABM, the end result is not what modeler expects • error in coding • core propertiy of compled systems – emergent behavior • Building models gradually • observe unusual behavior • determine its causes

  43. First Version • Simplest model – behavor • one species • ignore the environment • Some sheep wondering around on a lanscape breed [sheep a-sheep] • a breed: class of turtles • plural form: sheep • singular form: a-sheep • all mobile agents are turtles

  44. Update World in NetLogo • origin: center • nax x cor 17 • nax y cor 17 • wraping on • size of pathces 12

  45. creating sheep to setup clear-all create-sheep 100 [ ;;initialize sheep ;; randomly spread around the world ;; set their color to white ;; give shape “sheep” ] reset-ticks end

  46. setup breed [sheep a-sheep] to setup clear-all ask patches [ set pcolor green ] create-sheep 100 [ setxy random-xcor random-ycor set color white set shape "sheep" ] reset-ticks end

  47. iterations • sheep behavior – moving around • break into two • turning and moving forward

  48. iterations: go, wiggle and move at each iteration each sheep first turn a little bit in each direction then step forward wiggle: sheep procedure, the sheep changes its heading turn right then left, so the average is straight ahead move: sheep procedure take a step forward

  49. go, wiggle and move to go ask sheep [ wiggle move ] tick end to wiggle rt random 90 lt random 90 end to move fd 1 ;; take a step forward end

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