Oslo Summer School in Comparative Social Science Studies 2018
Introduction to Agent-based Modeling and Computing
Professor Robert Axtell, Department of Computational and Data Sciences, College of Science, Department of Economics, College of Humanities and Social Sciences Center for Social Complexity, Krasnow Institute for Advanced Study, George Mason University, USA
Course dates: 23 - 27 July 2018
Main disciplines: Economics, Sociology, Data Science
Political Science, Research Methodology
Course Credits: 10 pts (ECTS)
Limitation: 25 participants
Course objectives / learning outcome
This course is designed to introduce social scientists (economists, political scientists, sociologists, anthropologists, finance students, linguists, etc.) to the emerging paradigm of agent-based modeling (ABM), in which each individual in a social system is represented as a distinct software object.
These objects, called agents, have internal data (states) and methods (rules of behavior) and interact directly with one another according to behavioral rules. From specific initial conditions such models move forward in time through agent interactions and social structures emerge, from market-clearing prices to behavioral norms and social conventions. This ‘bottom-up’ perspective on social phenomena contrasts with so-called ‘representative agent’ formulations common in economics and are a principled way to study collective behavior in sociology, for instance. Agents in ABMs can be arbitrarily heterogeneous in contrast to game theory where typically only a few agent ‘types’ are permitted. Agents typically have heterogeneous information and are boundedly rational. Since agent interactions generally give rise to social networks there is a definite sense in which ABM generalizes social network theory, permitting the study of truly dynamic networks.
Furthermore, while it is common for ABMs to yield steady-states at the aggregate, social level, these are often accompanied by perpetual flux and novelty at the agent level, meaning Nash equilibria and other ‘solution concepts’ from conventional game theory are of limited relevance. We will illustrate all of the above through a series of progressively more complex models, some of which are grounded in micro-data.
Students will learn how to manipulate existing ABMs as well as how to program such models from scratch. While some familiarity with computer programming will be helpful for this course, a user-friendly software package, NetLogo, will be used extensively and students new to programming usually find it easy to learn.
LECTURE OUTLINE (draft)
Session 1: Agent-based modeling (ABM) in the social sciences
Session 2: Software for agent-based modeling
Session 3: A simple agent-based model: zero-intelligence traders
Session 4: Applications of ABMs in economics, finance, game theory, and related disciplines
Session 5: Agents create social networks and applications of ABMs in sociology
Session 6: Agent heterogeneity, big data in the social sciences, and synthetic populations
Session 7: Specifying agent behavior: incorporating laboratory data into ABMs
Session 8: Large-scale models (millions of agents or more)
Session 9: Calibration, estimation, verification, and validation of ABMs
Session 10: Advanced topics, based on student interest: agent activation regimes, applications in anthropology/archaeology, relation of ABMs to cellular automata and interacting particle systems, macroeconomic modeling using ABMs, applications of ABMs in politics, computational statistics and ABMs, commercial applications of ABMS, or complex systems and ABMs
Professor Robert Axtell earned an interdisciplinary Ph.D. degree at Carnegie Mellon University, where he studied computing, social science, and public policy. His teaching and research involves computational and mathematical modeling of social and economic processes. Specifically, he works at the intersection of multi-agent systems computer science and the social sciences, building so-called agent-based models for a variety of market and non-market phenomena.
His research has been published in the leading scientific journals, including Science and the Proceedings of the National Academy of Sciences, USA, and reprised in Nature, and has appeared in top disciplinary journals (e.g., American Economic Review, Computational and Mathematical Organization Theory, Economic Journal), in general interest journals (e.g., PLOS One) and in specialty journals (e.g., Journal of Regulatory Economics, Technology Forecasting and Social Change.)