The document discusses the social science applications of agent-based modeling (ABM), emphasizing its potential in decision-making and social evolution. It explores how subjective beliefs impact the diffusion of knowledge and outcomes in social scenarios, particularly highlighting the challenges faced by minority groups in a majority context. The author calls for proper data collection and collaboration between domain experts and ABM practitioners to advance the field.

1. Social Science Applications of Agent Based Modelling http://www.simian.ac.uk Edmund Chattoe-Brown (ecb18@le.ac.uk) Department of Sociology, University of Leicester

2. Some orientation A middle way between implausible formality and rich confusion. How can ABM “get closer” to the best bits of social science as it is really done? What are the institutional requirements for the diffusion of ABM? Main domains: Decision making and social evolution. http://www.simian.ac.uk

3. An example Rational choice: Choose the best from a set of alternatives. (Hard to argue with in many ways.) BUT: Where does the set of alternatives come from? (Presumably social.) Models already exist where people imitate actions or observe payoffs but analytically transmission of “possibilities” is hard to handle. http://www.simian.ac.uk

4. The basic model The world has “situations” requiring actions in response. Each action for a situation has an objective payoff. Actions are things like “boil it” or “bash it”. Each agent has a subjective set of payoffs (including “nil”) for all actions/situations with associated “confidences”. Choice is still “rational” (payoff x confidence). At the start, each agent only knows a single “traditional” action (and the correct - rather low - payoff) with complete confidence for each situation. In each period, there is a very small chance that it suddenly “occurs to” an agent that they might try a new action for a situation. The confidence for this is moderate (people like their own ideas!) When agents meet, they exchange one “piece” of information chosen randomly across situations and actions. If one “knows” an action and another doesn’t then the payoff belief is inherited at reduced confidence. If both agents “know” and agree on the payoff then their confidence is increased. If they disagree then payoff is averaged but with no change to confidence. Changes to confidence via communication are capped at 0.9. When an agent tries an action they learn the “correct” payoff and thus become totally confident in it. They will not change their belief or confidence thereafter. Agents live 80 periods and are then replaced by a randomly selected copy of someone else in the population. Why? http://www.simian.ac.uk

5. What happens? All actions are rapidly identified but with rather few correct subjective beliefs on payoff. The system does not reach the optimum but converges on sub-optimal outcomes. Thinking carefully about subjective belief makes it clear why. If you believe something is no better than what you have, why would you try it? Belief trap (Mackie): Convergence before optimisation. Even with objective information feeding into the system, whether an improvement diffuses depends on what people happen to believe. Of course, all this depends on the assumptions. http://www.simian.ac.uk

6. More interesting addition Two sorts of agents with “mirror” payoffs. (What is good for “greens” is bad for “blues” and vice versa.) With random mixing, minority groups do significantly worse. Their rationality is hampered by the ubiquity of majority beliefs. Again, there are plenty of ways to “break” this finding (some descriptively plausible) but the core idea is the interesting part. Without coercion, prejudice or limitations on rationality, a minority can be harmed simply by being a minority. How to make ideas like “ideology” and “hegemony” into robust theories. http://www.simian.ac.uk

7. Back to “real” social science Lots of ways of developing this model as a model but is that the way to go? Where can we find a situation in which a minority is “surrounded” by a majority and has to make important decisions in this context? There has to be “proper” data. What about heterosexual hegemony and “Coming Out Stories?” http://www.simian.ac.uk

8. RIP examples 1: Social capital There are many theories of social capital, all pretty plausible: Network position, trust, resource control, repeated interaction … How do these theories “fit together?” Statistics just atomise them (and some don’t suit “variables” anyway.) Qualitative research shows there is rich interplay but doesn’t answer “aggregate” questions i. e. can a society exist in which social capital is high by all common measures except one? How: A development of an Iterated Prisoner’s Dilemma model with networks and “time plans”. http://www.simian.ac.uk

9. RIP examples 2: Social mobility Collaboration with Nigel Gilbert (Sociology @ Surrey). Class position of children relative to parents a very large scale measure of meritocracy across time and countries. However, the social mobility literature is totally disjointed both from markets where jobs exist because companies succeed in selling “products” and qualitative research to get at “agency”: “I always wanted to be a lumberjack ”. Key mobility relevant processes are thus neglected or atomised: Self-employment, firm failure, skill acquisition. How: An agent-based labour market based on simplified consumer goods starting to look at “real” questions: Why are patterns of relative mobility so hard to change? http://www.simian.ac.uk

10. Got to have a graphic! http://www.simian.ac.uk

11. RIP example 3: Dynamic networks Social Network Analysis (and statistics generally) seem to assume that model categories reflect the underlying social reality. (Triad closure probability: Statistical models toss coins, people don’t.) In fact, we know quite a lot about how and why people meet and maintain social relations “qualitatively”. (“Friendship culls” example.) How: Use ABM to explore network dynamics outcomes of micro assumptions about interaction processes. (For example, how does the distribution of “meeting sizes” affect network structure?) http://www.simian.ac.uk

12. Real (“published”) examples Chattoe-Brown, E. (2009) ‘The Social Transmission of Choice: A Simulation with Applications to Hegemonic Discourse’, Mind and Society , 8 (2), December, pp. 193-207. Chattoe, E. (2006) ‘Using Simulation to Develop and Test Functionalist Explanations: A Case Study of Dynamic Church Membership’, British Journal of Sociology , 57 (3), September, pp. 379-397. Chattoe, Edmund and Hamill, Heather (2005) 'It's Not Who You Know - It's What You Know About People You Don't Know That Counts: Extending the Analysis of Crime Groups as Social Networks', British Journal of Criminology , 45 (6), November, pp. 860-876. Chattoe, Edmund and Gilbert, Nigel (1997) 'A Simulation of Adaptation Mechanisms in Budgetary Decision Making', in Conte, Rosaria, Hegselmann, Rainer and Terna, Pietro (eds.) Simulating Social Phenomena , Lecture Notes in Economics and Mathematical Systems 456 (Berlin: Springer-Verlag), pp. 401-418. Blood Borne Virus Simulator <http://www.bbvsim.org.uk/>. [For training.] Chattoe, Edmund, Hickman, Matthew and Vickerman, Peter (2005) Drug Futures 2025? Modelling Drug Use (London: DTI Foresight/Office of Science and Technology), <http://www.bis.gov.uk/assets/bispartners/foresight/docs/brain-science/dti-modelling.pdf>. [Replication.] http://www.simian.ac.uk

13. Suggestions If you understand it, we can model it. (If you don’t, modelling may help!) Collaborations between domain experts and ABM experts. (This can be statisticians or experimentalists just as well as ethnographers.) Welcome to Terra Nova: Still big areas where there is almost nothing in ABM. (Great for PhD students for example.) Infrastructural developments: “Local” networks and collaborations, “hitting” new journals, sharing knowledge about how to reach new kinds of audiences effectively (consulting, medicine, the police). http://www.simian.ac.uk

14. Now what? Simulation Innovation, A Node (Part of NCRM: research, training and advice): <http://www.simian.ac.uk>. NetLogo (software mainly used, free, works on Mac/PC/Unix, with a nice library of examples): <http://ccl.northwestern.edu/netlogo/>. Simulation for the Social Scientist , 2nd edition, 2005, Gilbert/Troitzsch. [Don’t get first edition, not in NL!] Agent-Based Models , 2007, Gilbert. Journal of Artificial Societies and Social Simulation (JASSS): <http://jasss.soc.surrey.ac.uk/JASSS.html>. [Free online, interdisciplinary and peer reviewed.] simsoc (email discussion group for the social simulation community): <https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=SIMSOC>. http://www.simian.ac.uk