Introduction to Agent-Based Modeling

Transcription

1 Date Introduction to Agent-Based Modeling Presenter: André Grow (University of Groningen/ICS) a.grow@rug.nl Presented at: Faculty of Social Sciences, Centre for Sociological Research, Family and Population

2 2 The solitary army ant is one of the least sophisticated animals imaginable * ** *** *Nigel Franks, cited in Mitchell (2009: 3) **source: ***source:

3 3 Modeling the search for food in ants Each ant follows a set of simple rules Randomly run around until you find food If you find food, bring it back to the hive and leave a trail of scent indicating that you have found food Follow any scent trail that you find * *source: adapted from Wilensky (1997a)

4 4 Outline 1. The principles of agent-based modeling 2. Classical examples 3. Agent-based modeling vs. classical tools for model building 4. Getting started 5. Summary

5 ics Date The principles of agent-based modeling

6 6 Roots of ABM in (1) Agent-based modeling and methodological individualism (MI) Social life exists because of the actors who live it Social facts should therefore be explained by reference to individuals Individuals can both create and are affected by institutions, norms, and culture Common misconceptions Atomism Utilitarism See Macy and Flache (2011) and Udehn (2001)

7 7 Roots of ABM in (2) Agent-based models (ABMs), or multi agent systems (MASs), are closest implementation of MI Typical agent characteristics Autonomous Interdependent Simple rules Adaptive and backward looking See Macy and Willer (2002), see also Smith and Conrey (2007)

8 8 When to use ABMs (Only) useful when the target phenomenon is indeed created by actions of interdependent actors When interdependence exists, ABMs potentially lead to better understanding of the mechanisms that generated the outcome Most interesting when self-organization creates phenomena with emergent properties Macro-level outcome is not included in the rules that guide micro-level units Consequences might be counterintuitive and undesirable for individuals See Macy and Flache (2011)

9 Date Classical example 1

10 10 Schelling s model of residential segregation Segregation is ubiquitous and occurs along many lines Age Education Ethnicity One important form of segregation: residential segregation by race See Schelling (1971), see also Smith and Conrey (2007)

11 11 Possible causes of segregation Organized efforts Law or regulations Economic factors Differences in rental prices constrain free moving Income often correlated with race Individual preferences?

12 12 Preferences and interdependence Individuals choose neighborhoods that satisfy their needs and preferences Individuals might have racial preferences Homophily: want to live with those who are similar in relevant demographic characteristics Heterophobia: avoid those who differ in relevant demographic characteristics Does racial segregation necessarily imply/require strong in-group love and out-group hate (net of other factors)?

13 13 The model Agents of two types (Red and Green) live on a checker-board-like world Each square represents a home that one agent can occupy Agents perceive the adjacent squares as their neighborhood (i.e. they have up to eight neighbors) Agents have preference for minimum share of neighbors that should be the same color as they are (e.g. 30 or 50%) If preferred composition is not achieved, agents move to a square that better satisfies their needs

14 14 Exemplary model runs * 10% similar wanted 30% similar wanted *source: adapted from Wilensky (1997b) 50% similar wanted 70% similar wanted

15 15 Results Segregation is obtained even in populations whose members have no preference for segregation The mechanism that connects the micro-level with the macro-level is a self-reinforcing process that derives from interdependent actions

16 Date Classical example 2

17 17 Kalick and Hamilton s model of the matching hypothesis (1) In romantic partners, there is a strong correlation in physical attractiveness (between.5 and.6) Possible explanations: Preference for similar attractiveness in partners Fear of rejection Empirical evidence: Individuals have a strong upward preference Upward preference stable even when rejection is likely See Kalick and Hamilton (1986), see also Smith and Convey (2007)

18 18 Kalick and Hamilton s model of the matching hypothesis (2) How does individuals upward bias and the population level pattern of matching go together?

19 19 The model (1) 1,000 agents, 500 male/500 female Each agent has attractiveness score between 1 and 10 In each iteration One male and one female interact Both decide whether or not to propose to the other If both propose: they are matched and removed from the pool If at least one does not propose: both are thrown back into the pool Run proceeds until all agents are matched

20 20 The model (2) Two alternative decision rules A) likelihood of proposing increases with similarity in attractiveness B) likelihood of proposing increases with attractiveness of potential partner

21 21 Results Mechanism (A) (implementing the matching hypothesis) creates attractiveness correlations of.8 to.9 Mechanism (B) (upward preference) creates attractiveness correlations of.5 to.6 Individual level upward preference seems to be compatible with population level trend of homophily

22 Date Agent-based modeling vs. classical tools

23 23 Two common competitors of ABM Equation based models Multivariate linear models See Macy and Flache (2011)

24 Date Equation based models (EBMs)

25 25 General properties of EBMs Try to model a phenomenon top-down Set of differential equations Model relations among system properties Examples Predator/prey populations Models of production flows Arms race models

26 26 Predator/prey models * *source: adapted from and Wilensky (1997c)

27 27 Pros and cons of EBMs EBMs: pros Analytical tractability Generality of results EBMs: cons Requirement of homogenous agents Non-linear processes difficult to model

28 Date Multivariate linear models (MLMs)

29 29 General properties of MLMs Estimate (causal) relations between variables/population parameters Examples Linear regression models Path modeling Structural equation modeling Assume independence of observations

30 MLMs and interdependence An example Goal: assessing the causal link between education and liberal attitudes Hypothesis: more highly educated individuals have more liberal attitudes (e.g., graduate vs. non-graduate degree) Possible reasons Education of parents Training in critical thinking... Alternative reason Individuals might influence each others attitudes Influence might depend on perceived similarity 30

31 31 A simple non-independence model (1) Agents have three individual properties Education: non-graduate degree (-1) / graduate degree (1) Attitude on political issue 1: varies between conservative (-1) and liberal (1) Attitude on political issue 2: varies between conservative (-1) and liberal (1) Agents influence each other during interactions Homophily and positive influence agents who perceive each other as similar influence each other so that their opinions become more similar Heterophobia and negative influence agents who perceive each other as dissimilar reject each others opinions so that they become more dissimilar

32 32 A simple non-independence model (2) Both education and attitudes matter for perceived dis-/similarity Similarity = Average difference over all dimensions is > 0 Dissimilarity = Average difference over all dimensions is < 0 Two conditions There are equal numbers of grad/non-grad-level individuals and... - Condition A):... their attitudes are initially distributed completely at random - Condition B):... 20% grad-level individuals have consistently maximally liberal attitudes

33 Issue 1 Issue 1 faculty of behavioral and 33 Exemplary model runs liberal grad non- grad No bias condition Grad liberal bias condition conservative conservative Issue 2 Issue 2 liberal

34 34 Results and implications Regression model overestimates the causal effect of education on political attitudes Of course, education and liberalism are correlated in our example, but ABM reveals the (potential) mechanism that underlies this correlation Proportion of grads with liberal bias Correlation between education and liberalism

35 Date Getting started

36 36 How to develop a model There is no single best way to develop a model Yet, there are some core criteria Is the phenomenon the result of actions of interdependent individuals? What rules govern individual behavior? How are agents connected? The golden rule: keep it simple, stupid!

37 37 How to analyze a model Conduct systematic experiments Explore parameter space Investigate interactions among parameters Conduct sensitivity analyses Is the outcome stable for extreme parameter values? Is the outcome robust to small changes in assumptions?

38 38 How to implement a model Two forms of simulation software Low level programming language C++ Delphi Java High level programming language/modeling software Swarm NetLogo

39 Date Summary

40 40 How to use ABM Theoretical modeling and explanation (most common) Abstract thought experiments Explore plausible mechanisms that generate observed pattern Deriving empirical predictions (less common) Common misconceptions ABM replaces empirical research Findings of ABMs can directly be translated into policy advice

41 41 Summarizing points ABM is a tool for theory development ABM enables us to investigate phenomena that emerge from actions of interdependent individuals An ABM (should) describe the simplest set of assumptions necessary to generate the phenomenon ABMs have particular advantages compared to more traditional tools that assume independence of observations

42 Date Thank you for your attention!

43 43 References and recommended readings Bonabeau, E Agent-Based Modeling: Methods and Techniques for Simulating Human Systems. PNAS, 99: Gilbert, N. and Terna, P How to Build and Use Agent-Based in Social Science. Mind & Society, 1: Kalick, S. M. and Hamilton, T. E The Matching Hypothesis Reexamined. Journal of Personality and Social Psychology, 51: Macy, M.W. and Willer, R From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annual Review of Sociology, 28: Macy, M.W. and Flache, A Social Dynamics From the Bottom Up: Agent-Based Models of Social interaction. Pp in Hedström, P. and P. Bearman (eds). The Oxford Handbook of Analytical Sociology. Oxford University Press. Michtell, M Complexity: A Guided Tour. New York: Oxford University Press Schelling, T. C Dynamic Models of Segregation Journal of Mathematical Sociology, 1: Smith, E.R. and Conrey, F.R Agent-Based Modeling: A New Approach for Theory Building in Social Psychology. Personality and Social Psychology Review, 11: Udehn, L Methodological Individualism: Background, History and Meaning. London: Routledge

44 44 Models used Wilensky, U. (1997a). NetLogo Ants model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Wilensky, U. (1997b). NetLogo Segregation model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Wilensky, U. (1997c). NetLogo Wolf Sheep Predation model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.