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

4. 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

5. 17.1 Towards Structurally Realistic Models • Part II – engine of ABM • design, implement and test models • Part III – how real AMBs work • Models – simplified and purposefull representations to answer research questions and solve problems • Simplified representations: • limited and preferbaly small set of variables to represent the real system • Models – parsinonious repersentation • capture key characteristics and behaviors

6. Structurally realistic • key structural elements of real systems’ internal organization • but not realistic – every thing we know • Internal organization: • structures and processes • produces system’s characteristics and bevaiors • Models simple – • question or problem as a filter • Aspects of real system • entities, state variables, spatial zand time scales • are included – absolutely esential for the particular problem

7. 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: • take intoaccount observed patterns • to make models structurallhy realistic • more general, useful, scientific and accurate

8. POM - examples • population models – strong cycles • models can produce cycles • withifferent assukmptions and mechanizms • not all are structurally realistic • generate cycles for wrong reason

9. explaining cyclic patterns – strong filter • filters • seperates some models as useless • one filter is not enough • Basic idea of POM • multiple patterns • each pattern as a filter some explanation • to atchive structural realism • Pastterns • at different scales • agent or system levels

10. POM fondomental to any science • use multiple patterns to get indicators of systems about syswtems internal organization • POM for ABM • design model sturcutures – ch 18 • develop test theores of agent behavior – ch 19 • find values of parameters – ch 20

11. 17.2 Single and Multiple- Strong and Weak Patterns • Pattern: anything beyind random variation • regularities, signals, • stalized facts - emprical observations – derscribing esential charateristics of a phenomena • Example: • light emitted by an excited atom • not white – not all waveelengths but few wavelengths • need explanation – quantum theory • Example: • meet someone at theairport • pattern –male – filters hllf of possengers • others – age wearing T-shirt, carrying blue suitcase

12. few – • simple or weak – simple dercriptions or none strongly dercriptive • qualitative – not quantitative • about different things – sex, age, .. • relevant specifically to the problem • Small number of weak, qualitative diverse pattterns is as good as a signlestorngpattern – photo – difficult to obtain • patterns characterizing the system with respect to the problem • patterns caused by mechanizms or processes thought to be relevant to the problem to be modeled • the model should reproduce them – or no trust

13. characterize the system • how to identify such diverse set of patterns characterizsing 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

14. Subjective • Experimental trail error • preliminary understanding • blind spots, biases, inconsistancies • progress better patterns find mechanisms producing them – robustly ch23

15. Mathcing patterns: qualitative or quantitative? • until final stages of modelin • qualitative to eliminate many models mechanizms • proof traits • quantification – next finalstages

16. 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 appropriagte submodel for agents’ key behavior – theory development • hypothesising alternative submodels • rejecting – falifying – thoese not explaining observed patterns

17. 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 • calibration – nerrow valuse of parmeters for quantitrative prediction

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

19. 18.1 Introduction • First • entities, state variables • attrubutes representing the system • What processes • making the state variables change • Model’s purpose • can be too nerrow – too simple in structure • Ex: to predict growth of humman population • exponential growth, state varible – population • Models with few variables • underdetermined – results not explain patterns • too poor in structure and mechanizms • Ex: age structure – demographic processes

20. first task of POM • observed patterns – design structure of the model • sufficiently realistic and testable • but not unnecessarily complex • Learning objectives: • Four setps of POM to design model structures • structure an ABM from • a - knowledge of the system and problem • b - objerved patterns

21. 18.2 Steps in POM to Design Model Structure • 1- fromulating the model – ODD • purpose only filter – designing structure • include • entitie, state variables and processes • minimum necessary • too simple to be useful

22. 2- identify set of observed patterns • characterizing the system • relative to the problem • literature and experts • whiat is known – processes to patterns • few patterns 2-6 often 3-4 • independent ot linked? • diverse as possible • Rank – how important characterizing sytem or problem

23. 3- define criteria for pattern matching • How the model reproduce each patterns • start qualitative – visual mattern-matching • trends statistial outputs –quantitative • What outputs to be used to evaluate whether model reproduce characteristic patterns

24. 4 – review model formulation • additional thinks • new entities state variables • new proceses • new output ot observe patterns • Then procede with the model cycle • re formulate the model • implement • test

25. 18.4 Example:Managing Accountingf and Collusion • How believable existing models are? • how many patterns they were designed to expalin • How much to invest in comanies different divisions? • didision 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

26. identified six patterns or stylized facts • sse Railsback and Grimm, pp 240 • howexisting 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 toevaluate existing models • not necessarily ABMs

27. 18.5 Summary and Conclusions • multiple observed patterns – indicators of • systems’ internal organization • reproducing such patterns with ABMs – • decode internal organization of systems • The point of POM – • think consciously in terms of • patterns, rewgularities or stylised facts • select model structure accordingly • identify patterns • data and expert knowledge • strong or weak multiple

28. 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 mechnaizms • 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

29. Chapter 19: Theory Development • 19.1 Introduction • 19.3 Exaples of Theory Development for ABMs

30. 19.1 Introduction • From model structure to processes • how to model them • Designing model structure and formulate schedule • overview pat of ODD • which processes but not how they work • Modeling cycle to go • simple wrong processes • Ex: random decisions • After the first implementation • unsijplified , realistic representations

31. 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 • models of key agent behavior • simple then real behavior • complex enough to produce usefulsystem behavior

32. test theories with POM • most important, unique, academically fertile • Learning objectives • developing hypothesis alternative hypothesis – conducting controled experiments and refined hypothesis • theory development - POM

33. 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 syetem to find new patterns • falsfy some hypothesis • constructing alternative theories hypothsisi scientific method • strong inference by Platt 1964

34. 1 – devicing alternative hypothesis • 2- device experiments • whose outcomes may exclude some hypothesis • 3- carrying out these experiments • 4- Recycling the procedure • suhypothesis or sequential hypothsis

35. Adapted to Theoryu Development inABM • subcycle of modeling • 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 swhether match or not

36. others may develop behavioral models • cognitive psychology • behaviora economics ecology • consumer behavior • too simple to produce realistic behavior in ABM • start with existing theories • not to explaing all behavior but • patterns of interest

37. a theory – not all behavior • for patterns to explain • null theories • agents behavior random or do exactly the same thing • test the ABM • how sensitiv eto key behavior • somethimes robust to behavior • if key patterns emerge with null theory • indication that this is not a key • event a simple trait produces patterns • if not worth developing a refined model • Anaysis of submodels with other tools

38. 19.3 Exaples of Theory Development for ABMs • Fish Schooling and Bird Flocking • Trader Intelligence in Double-Auction Markets

39. Fish Schooling and Bird Flocking • fondsmental 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

40. 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

41. 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 • converve equilibrium price

42. Code Sunder (1993) – start with a null theory • zero intelligence – buyging selling pricese random • trading rule • matching highest buying offer with lowest selling price

43. Chapter 20: Parametrization and Calibration • 20.1 Introduction and Objectives • 20.2 Parametrization of ABMs is Different • 20.3 Parametrizing Submodels • 20.4 Calibration Consepts and Strategies • 20.5 Example: Calibration of the Woodhopoe Model • 20.6 Summary and Conclusions

44. 20.1 Introduction and Objectives • parameters – constants in equations and algorithms processes in ABMs • butterfly hilltaping – q: probability of moving uphill • Parametrization: selecting values for model’s parameters • in POM – models relsted to real systems • ABM organization and dynamics of complex systems • quantitative results meter • relative importance of processes

45. Calibration: finding good values especially few important parameters • what parameter values causes model to observed patterns in the real system • calibrating a model against data • fiffting a model to data • model results matches observed data • calibration v.s. theory development • quantitative v.s. quantitative • agent behavior v.s. full model behavior (in general) • strructural uncertainity v.s parameter uncertainity • good model design or agent traits

46. Three purposes • 1- force the model to match emprical observations as much as possible • more accurate • 2- estimate the value of parameters we cannot evaluate directlly • “inverse modeling” – in some fields • adjust until the model matches some observations • 3- test a models structural realizm

47. Learningobjectives • understanding several objectives of calibratiıon • How calibration of ABMs different • fondomental calibration concepts andstrategies • develop experience

48. 20.2 Parametrization of ABMs is Different • In many fiesld modeling • selecting one or few equatiosn with few parameters • evaluating them calbirating agaits emprisl data • more parameters more uncertainty • degree of fredom • Traditional • information in paramters • no mechnisms or processes • For ABMs • fine tuning small fraction of parameters

49. 20.3 Parametrizing Submodels • ABMs relable credible • develop, parametrize and test each submodel • How to chose parameters of submodels • before inverse calibration • Select a submodel from a process already bubliched calbrated analysed • time and space scaling is important • parameters may be available • literature, own data or educated guesswork • Design models – parameters measurable real quangtities • gueswork or caliblration

50. parameters procuces reaonable results whole range of situations • Fill models to estimate parameters • calsical model fitting , caliblration estimation • For initial simple models or • theoretical models – designed to explaore possible explanations • guestimated values – How? • define upper and lower values • beyound which the submodel produce nonsense