MIS 643 Agent-Based Modeling and Simulation (ABMS) Bertan Badur badur@boun.tr Department of

1. MIS 643 Agent-Based Modeling and Simulation (ABMS) Bertan Badur badur@boun.edu.tr Department of Management Information Systems Boğaziçi University

2. Pattern-Oriented Modeling • Chapter 17-20, of Agent-Based and Individual-Based Modeling: A Practical Introduction,by S. F. Railsback and V. Grimm

3. “... mere prediction is not enough. The researcher should also concern about the generative mechanisms.” • Heine et al. (2005)

4. Outline • Chapter 17: Introduction to Part III • Chapter 18: Patterns for Model Structure • Chapter 19: Theory Development • Chapter 20: Parameterization and Calibration

5. Chapter 17: Introduction to Part III • 17.1 Towards Structurally Realistic Models • 17.2 Single and Multiple- Strong and Weak Patterns • 17.3 Overview of Part III

6. 17.1 Towards Structurally Realistic Models • Part II – engine of ABM • design, implement and test models • Part III – how real AB complex systems work • Models – simplified and purposeful representations to answer research questions and solve problems • Simplified representations: • limited and prefersbly small set of variables to represent the real system • Models – parsinonious repersentation • capture key characteristics and behaviors the real system

7. Structurally realistic • key structural elements of real systems’ internal organization • but not realistic – not every thing we know about • Internal organization: • structures and processes that in the real system • produces system’s characteristic properties and bevaiorss • Models simple – • question or problem addressed with the model as a filter • Aspects of real system: • entities, state variables, attributes, processes, spatial and temporal resolution snf extend • are included – absolutely esential for the particular problem

8. Pattern-oriented modeling • Problem with AMBs: • questions or problems addressed by the model is often inadequate to determine what must be included in the model • The problem – not enough information • structurally realistic • capture internal organization of the system • Pattern-oriented modeling (POM): • the use of patterns observed in the real system • ss the additional information to make models • structurally realistic and therefore • more general, useful, scientific and accurate

9. POM - examples • population models – strong cycles • models can produce cycles • with different assumptions about key mechanisms causing the cycles • not all are structurally realistic • produce the cyclic pattern for wrong reasons

10. explaining cyclic patterns – strong filter • filters • seperates some models as useless (not produce cyclic patterns.) • one filter is not enough to identify the internal organization of these populations • too easy to make a model produce cycles • need more filters – excluding unrealistic mechanisms

11. Basic Idea of POM • to use multiple patterns to design and analize models • each pattern as a filter some explanation • after using 3-5 patterns • to atchive structural realism • an unrealistic model may passes one pattern but not second or third • POM: multicriteria assesment of models using multiple pastterns observed in the real system • at different scales • agent or system levels

12. POM fondomental to any science • use multiple patterns to get indicators of systems about systems internal organization • trying to reproduce these patterns simultaneously • decode internal organization of real systems • POM for ABM • design model sturcutures – ch 18 • develop test theories of agent behavior – ch 19 • find good values of parameters – ch 20

13. 17.2 Single and Multiple- Strong and Weak Patterns • Pattern: anything beyond random variation • regularities, signals, • stalized facts - emprical observations – describing esential charateristics of a phenomena – call for an explanation • Example: • light emitted by an excited atom • not white – not all waveelengths but few wavelengths • need explanation – quantum theory

14. One single pattern may not be enough to decode internal organization • multiple patterns or filters are needed • Example:to identify someone at the airport • pattern –male – filters hllf of possengers • other patterns – age above 30, wearing T-shirt, carrying blue suitcase • Interesting things about these patterns: • few in number – four patterns • simple or weak – simple dercriptions or none strongly dercriptive • qualitative – not quantitative • about different things – sex, age, .. • relevant specifically to the problem

15. Small number of weak, qualitative diverse pattterns (easier to obtain) is as good as a signle storng pattern – photo • patterns characterizing the system with respect to the problem: • patterns caused by mechanisms or processes thought to be relevant to the problem to be modeled • the model should reproduce them – or no trust it

16. characterize the system • how to identify such diverse set of patterns characterizing the system to be modeled • judgement, knowledge, trail and error • some systems strong patterns exists • most systems addresses weak patterns • state variables stay in limited ranges • system responses veraities of ways

17. detecting and agreeing on what constutues a useful pattern • Subjective • Experimental trail error • preliminary understanding • blind spots, biases, inconsistancies • progress better patterns (meaningful and useful) • find mechanisms producing them: robustly ch23 • secondary predictions – validation – ch18

18. Mathcing patterns: qualitative or quantitative? • POM – testing whether a model reproduce specific patterns • need to define crieria when a pattern is matched • mumerical patterns and statistical test? • until final stages of modeling • qualitative to eliminate many models mechanisms • proof traits • quantification – next final stages • Platt (1964) many issues in science 8including physics and chemistry) - qualitative

19. 17.3 Overview of Part III • Ch 18 • How to design model structure • patterns observed in the real system emerges from execution of the model • Ch 19 • how to find most appropriate submodel for agents’ key behavior – theory development • hypothesizing (tring) alternative submodels • rejecting – falsifying – those not explaining observed patterns

20. Ch20 • calibrate models to quantitatively match observations and data • parameters in ABMs could not be determined directly from data or literature • “guestimate” – for qualitative predictions, theory development or understanding • for quantitative modeling – guesstimated values – uncertainity often too high • calibration – nerrow valuse of parmeters for quantitrative prediction

21. Chapter 18: Patterns for Model Structure • 18.1 Introduction • 18.2 Steps in POM to Design Model Structure • 18.4 Example:Managing Accounting and Collusion • 18.5 Summary and Conclusions

22. 18.1 Introduction • First • entities, state variables, attrubutes representing the system • Then what processes • making the state variables change • Model’s purpose as a guide – what entities and variables • can be too nerrow – too simple in model structure to be tested • Ex: a model to predict growth of human population • exponential growth - state varible: population – one parameter • how good is the model – is it structurally realistic? • Models with too few entities and state variables • underdetermined – • results not explain patterns • too poor in structure and mechanisms

23. first task of POM • what to do to make the model richer • but not too rich and complex? • Ex: age structure – • A model capable of reproducing historical patterns in not only population growth bu also age structure in different regions • use - demographic processes • First task of POM: • using observed patterns – design structure of the model • sufficiently realistic and testable • but not unnecessarily complex

24. Goal of Part III • understanding overall strategy oıf POM • not a simple recipe • strategy of doing science with ABMs • know the system to model

25. Learning Objective • Four setps of POM to design model structures • structure an ABM from • a - knowledge of the system and problem that the model addresses • b - observed patterns that characterize the system’s processes relevant to the problem

26. 18.2 Steps in POM to Design Model Structure • POM – model structure: • What entities • kind of agents and other things – need to be • what state variables – need to have • to give the model enough realism and complexity • to be tesetable and useful • avoiding unnecessarily complexity

27. 18.2 Steps in POM to Design Model Structure • 1- formulating the model – ODD • purpose only filter – designing structure • include • entitie, state variables and processes • minimum necessary • formulation too simple to be useful

28. 2- identify set of observed patterns • characterizing the system relative to the problem • literature and experts • what is known – processes give rise to patterns • few patterns 2-6 often 3-4 • independent or linked? • diversity of patterns – responce to different king of changes • Rank – how important they are in characterizing the sytem and problem

29. 3- define criteria for pattern matching • How to decide the model reproduce each patterns • start qualitative – visual pattern-matching • trends statistial outputs • then - quantitative • What outputs to be used to evaluate whether model reproduce characteristic patterns

30. 4 – review model formulation • additional thinks • new entities state variables • new proceses • new output - observe patterns • Then procede with the model cycle • reformulate the model • implement • test • next step of POM • testing ABM and its individual adaptive theory

31. 18.4 Example:Managing Accountingf and Collusion • How believable existing models are? • how many patterns they were designed to expalin • Decision by managers of large busineese: • How much to invest in company’s different divisions? • division managers – overstate expected returns • Groves (1973) a mechanism • forcing reporting honest predictions • undermined by collusions by division managers • some emprical, game theoretic or simulation models • Heine et al. 2005

32. identified six patterns or stylized facts • sse Railsback and Grimm, pp 240 • how existing models reproduce their patterns • 1th game thoery – address pat 1 but contradict • 2ed game theory – adressed 2,6 reproduce thrm • 1th simulation – reproduce 1, contadict 6 • 2ed simulation - reproduce 2,6 and convicingly 6 • POM to evaluate existing models • not necessarily ABMs

33. 18.5 Summary and Conclusions • General idea of POM • multiple observed patterns – indicators of systems’ internal organization • reproducing such patterns with ABMs – • decode internal organization of real systems • The point of POM – • think consciously in terms of • patterns, regularities or stylised facts • select model structure accordingly • identify patterns • data and expert knowledge • strong or weak, multiple

34. not a technique but a strategy • POM is not a technique • programming or statistical test • but a general strategy or attitude • avoid two extreames • simplistic models – poor model structures or mechnaisms • likely to reproduce very few patterns but for wrong reasons • too complex models • designed with not characteristic patterns or model purpase • as a guide • what can be left out or can be included

35. Chapter 19: Theory Development • 19.1 Introduction • 19.2 Theory Development and Strong Inference in the Virtual Labaratory • 19.3 Exaples of Theory Development for ABMs

36. 19.1 Introduction • From model structure to processes • how to model them • Designing model structure and formulate schedule • overview part of ODD • which processes but not how they work • Modeling cycle to go • simple often wrong processes • Ex: random decisions • After the first implementation • unsimplified , realistic representations • of key processes • How?

37. key processes - agent behavior - ABM • less likely to find in literature • minor processes • found in literature • What is the most importnat agent behavior • How to find a submodel for that • Theory development • theory for agent behavior • tested and useful – how the system works • models of key agent behavior • simpler than real behavior • complex enough to produce useful system behavior

38. Theory for complex systems • as models of the individual characteristics • that give rise to system level dynamics • in physics, engineering – seful and general for soling problems • How to test agent behavior to be accepted as theory – POM • traits - ABM reproduces set of patterns characterizing system dynamics • most important, unique, academically fertile –AB modeling

39. Learning Objectives • developing hypothesis alternative hypothesis – conducting controled experiments and refined hypothesis • theory development - POM

40. 19.2 Theory Development and Strong Inference in the Virtual Labaratory • ABM – virtual labaratory • test alternative traits for key behavior • plug in ABM and see whether reproduce observed patterns • cycle of • testing and refining traits • studying the system to find new patterns • falsfy some hypothesis • constructing alternative theories hypothsis scientific method • strong inference by Platt 1964

41. Steps of strong inference • 1 – devicing alternative hypothesis • 2- device experiments • whose outcomes may exclude some hypothesis • 3- carrying out these experiments • 4- Recycling the procedure • subhypothesis or sequential hypothsis

42. Adapted to Theoryu Development inABM • The approach for theory for agent behavior • subcycle of modeling cycle – Platt’s steps • 1- idengtify alternative hypothesis for behavior • 2- imlement these alternatives • test software • 3- test and contrast alternatives • how well reproduce observed patterns • falsify alternatives not reprocucing patterns • 4- repeat the cycle • traits experiments find new patterns test whether match or not

43. Where to get alternative theories to test and contrast? • others may develop behavioral models • empirical data or existing theory • sufficient observations in real world • statistical or stochastic traits? • decision theory for the agnets or decision models? • many fields have subdisiplines for behavior: • cognitive psychology, behavioral economics ecology, consumer behavior

44. clasical theory about behavior ex: • individuals maximize utility, firms maximize profit, animals maximize growth • too simple to produce realistic behavior in ABMs • important to understand existing theory • start with existing theories • not to explaing all behavior but • patterns of interest

45. a theory – not all behavior • for patterns to explain • null theories – not specific mechanisms or assuptions • agents behavior random or do exactly the same thing • Why with null theories? • test rest of ABM before focusing on behavioral traits • how sensitive to key behavior • somethimes robust to behavior • if key patterns emerge with null theory • indication that this is not a key process - even a simple trait produces patterns • if not worth developing a refined model • Analysis of submodels with other tools

46. 19.3 Exaples of Theory Development for ABMs • Fish Schooling and Bird Flocking • Trader Intelligence in Double-Auction Markets

47. Fish Schooling and Bird Flocking • fundamental assumptions • individual animals adapt their movement, direction, and speed to match those of their neighbors • move toward their neighbors while maintaining a minimum seperation distance and avoiding collisions • but which neighbors? • Hunt and Wissel (1992) two assumptions – potential theories • 1- nearest other fish • 2- average direction and location of several neighbors

48. observed patterns • qualitative and simple – visually seem like fish schools • quantitative: how compact and coordinatred fish schools are • mean distance between a fish and its nearest neighbors • mean angle between a fish and average dircetion • root mean square distance between each fish and its school’s centroid • the theory – adapt to average of several neighbors • Ballerini et al. 2008 – extension • observations – ABM model • 6-7 nearest neighbors vs response to birds within a fixed distance (falsified)

49. Trader Intelligence in Double-Auction Markets • What assuptions about trader intelligence • observed patterns of trader behavior • How people make trading decisions • to produce realistic dynamics • Humman subject experiments representing buyers and sellers • Resuts • converce equilibrium price

50. Code Sunder (1993) – start with a null theory • zero intelligence – buyging selling pricese random • trading rule • matching highest buying offer with lowest selling price • result: vild flactuations in the markets exchange price • Next: adding smallest bid of intelligence • exclude those that loses money • result surprising – the simulated market converged to the equilibrium like a real market