MIS 643 Agent-Based Modeling and Simulation 201 6 /201 7 Fall

1. MIS 643Agent-Based Modelingand Simulation2016/2017 Fall Chapter 2 Models, Modeling Cycle and the ODD Protocol

2. Outline 1. What is Model 2. Modeling Cycle 3. ODD Protocol

3. 1. What is a Model? • A model is a purposeful simpoified representation of a real system • In science: • How thinks work • Explain patterns that are observed • Predict systems bevaior in response to some change • Social systems • Too complex or slowly changing to be experimentally studied

4. Models • Formulate a model • design its assumptions and algorithms • Different ways of simplfing real systems • Which aspect to include , which to ignore • Purpase • The questions to be answered is the filter • all aspects of the real system • irrelevant or insufficiently important • to answer the question are filtered out

5. Searching Mushrooms in a Forest • Is there a best strategy for searching mushrooms? • observation: • mushrooms in clusters • An intuitive strategy: • scanning an area in wide sweeps • upon finding a mushroom turning to smaller scale sweeps • as mushrroms in clusters

6. Searching Mushrooms in a Forest • What is large, small sweeps? and • How long to search in smaller sweeps? • Humans searching • prizes, jobs, low price goods, peace with neighbors • mushroom hunter • sensing radius is limited • must move to detect new mushrooms

7. Why develop a model for the problem • try different search strategies • not obvious with textual models • clearly formulated purpose: • what search strategy maximizes musrooms found in a given time • Ignore trees and vegitables, soil type • Include: mushrooms are distributed as clusters

8. Simplified hunter • mushroom hunter • moving point • having a sensing radius • track of • how many mushrooms found • how much time passed since last mushroom fouınd

9. Formulate a model • clusters of items (mushrooms) • If the agent (hunter) finds an item • smaller-scale movement • If a critical time passes since last item found • swithes back to more streight movement • so as to find new clusters of items

10. Why model • Here processes and behavior is simple • in general what factors are important • regarding the question addresed by the model • not possible So • formulate • implement in computers • analize • rigorously explore consequences of assuptions

11. First Formulation • First formulation of the model • Preliminary understanding about how the system works • Proceses structure • Based on • Empirical knowledge system’s behavior • Theory • Earlier models with the same purpose • Intiution or imagination

12. no idea • about how a system works • not formulate a model • e.g.: human consciousness

13. Good model • Assumptions at first experimental • Test whether they are appropriate and useful • Need a criteria – model is a good representation of the real system • Patterns and regularities • Example: Stock market model • Volatility and trends of stock prices volumes,…

14. Fisrt Versions • First version • Too simple - lack of prcecesses structure • Inconsistant -

15. 2. The Modeling Cycle • When developing a model • Series of tasks – systematically • consequences of simplfiing assumptions • Iterating through the tasks • First models are • Too simple , too complex or wrong questions

16. The Modeling Cycle • Modeling cycle:Grimm and Reilsbeck (2005) • Formulate the question • Assamble hypothesis • Choose model structure • Implement the model • Analyze the model • Communicate the model

17. Formulate the Question • Clear research question • Primary compass or filter for designing the model • clear focus • Experimental may be reformulated • E.g.: for MH Model • what strategies maximizes the rate of findng items if they are distributed in clusters

18. Assamble Hypothesis • Whether an element or prosses is an esential for addresing the modeling questions - an hypothesis • True or false • Modeling: • Build a model with working hypothsis • Test – useful and sufficient • Explanation, prediction - observed phenomena

19. Assamble Hypothesis (cont.) • Hypothesis of the conceptual model • Verbally graphically • Based on Theory and and experience • Theory provides a framework to persive a system • Experience • Knowlede who use the sysem

20. Assamble Hypothesis (cont.) • Formulate many hypothesis • What process and structures are essentiaal • Start top-down • What factors have a strong influence on the phenomena • Are these factors independent or interacting • Are they affected by ohter important factors

21. Assamble Hypothesis (cont.) • Influence diagrams, flow charts • Based on • Existing knowledge, simplifications

22. Basic Strategy • Start with as simple as possible • even you are sure that some factors are important • Gilbert: analogy null hypothesis in satatistics – agaainst my claim • Implement as soon as possible

23. Guidelines • Mere realizm is a poor guideline for modeling • must be guided by a problem or question about a real system • not by just the system itself • Constraints are esential to modeling • on information understanding time • Modeling is hardwired into our brains • we use powerful modeling heuristics to solve problems

24. Heuristics for Modeling • pleusable way or reasonalble approach that has often proved to be useful • Rephrase the problem • Draw simple diagrams • Inagine that you are indide the system • Try to idendify esential variables • identify assumptions • Use salami tactics

25. E.g.: MH Model • Esential process • swithcing between large scale movements and small scale searching • Depending on how long it has been since the hunter has found an item.

26. Choose scale, state variable, processes, parameters • Variables derscribing environment • Not all charcteristics • Relevant wtih the question • Examples • Position (location)Age, gender, education, income, state of • mind ,…

27. Choose scale, state variable, processes, parameters • Example • Parameter being constant • Exchange rate between dolar and euro • Constrant for travelers, not for traders

28. Choose scale, state variable, processes, parameters • Scale • Time and spatial • Grain: smalest slica of time or space • Extent: total time or area covered by the model • The gain or time spen: step over which we ignore variation in variables

29. Choose scale, state variable, processes, parameters • Choose scales, entities, state variables processes and parameters • Transfering hypothesis into equations rules • Describing dynamics of entities

30. Choose scale, state variable, processes, parameters • Variables – derscribing state of thr system • The essential process – cause change of these variables • In ABM • interacting individuals • agent-agent, agent-environment • Variables – individual • parameters

31. E.g.: HM Model • Space items are in and hunter moves • Objects - agents • one hunter and items to be searched • hunter • state variables • time • how many items found • time last found • bevaior: search strategy

32. Implementation • Mathematics or computer programs • To translate verbal conceptual model into annimated objects • Implemented model has its own dynamics and life

33. Implementation • Assumption may be wong or incomplete but impolementation is right • Allows to explore the consequences of assumption • Start with the simplezt - null model • Set parameters , initial values of variables

34. Analysis • Analysing the model and learing with the aid of the model • Most time consuming and demanding part • Not just implementing agents and run the model • What agents behavior can explain important characteristics of real systems • When to stop iterations of the model cycle?

35. E.g.: HM Model • Try different search algorithms • with different parameters • to see which search algorithm – strategy is the best

36. Communication of the model • Communicate model and results to • Scientific community • Managers • Observations, experiments, findings and insights are only when • Others repreduce the finings independently and get the same insights

37. Example of a Model • Consumer behavior model: • How friends influence consumer choices of indivduals • Buy according to their preferences • what one buys influeces her friends decisions • interraction

38. Example of a Model • verbal • mathematical • theoretical model • Emprical : statistical equations • estimated from real data based on questioners • simulation models of customer behavior • ABMS – interractions, learning, formation of networks

39. Theoretical Models • Analytical models • Restrictive assumptions • Rationality of agent • Representative agents • Equilibrium • Contradicts with observations • Labaratory experiments about humman subjects

40. Theoretical Models • as precision get higher explanatory power lower • closed form solutions • Relaxation of assumptions • geting a closed form solution is impossible

41. Emprical Models • Historically mathematical differential equations • Or emprical models represente by algberic or difference equations whose parameters are to be estimated

42. Simulation Models • Simulation • ABMS: • Represent indiduals as autonomous units, their interractions with each other and environment • Chracteristics – variables • and behavior • Variables – state of the whole system

43. How ABM differs • Units agents differ in terms of resourses, size history • Adaptive behavior: adjust themselfs looking current state which may hold information about past as well. other agent environment or by forming expectations about future states • Emergence: ABM across-level models

44. Skills • A new language for thiking about or derscribing models • Software • Strategy for model development and analysis

45. 3. Summery and Conclustions • ABM relatively new • way of looking old as well as new problems • complex (adaptive) systems • improve understanding • What is modeling • What ABM brings • Model development cycle

46. Ant • An ant forgang food • Model: • an abstracted describtion of a process, object event

47. Ants • manipulability • textual – hard to manipulatfe • E.g.: what if all ants have the same behavior • A computational model • takes inputs, manipulates by algorithms and produces outputs • Model implementation • from textual to computer code

48. Ants • an ant – agent • properties • behavior

49. Creating the Ant Foraging Model

50. The ODD Protocol • Originaly for decribing ABMs or IBMs • Useful for formulating ABMs as well. • What kind of things should be in ABM? • What bahavior agents should have? • What outputs are needed_ • A way of think and describe about Agent Based Modeling • communicate models to others • ease implementation