Copyright © 2006 by the author(s). Published here under license by the Resilience Alliance.
Janssen, M. A., and E. Ostrom. 2006. Empirically based, agent-based models. Ecology and Society 11(2):
37. [online] URL: http://www.ecologyandsociety.org/vol11/iss2/art37/
Guest Editorial, part of a Special Feature on Empirical based agent-based modeling
Empirically Based, Agent-based models
Marco A. Janssen 1 and Elinor Ostrom 2
ABSTRACT. There is an increasing drive to combine agent-based models with empirical methods. An
overview is provided of the various empirical methods that are used for different kinds of questions. Four
categories of empirical approaches are identified in which agent-based models have been empirically tested:
case studies, stylized facts, role-playing games, and laboratory experiments. We discuss how these different
types of empirical studies can be combined. The various ways empirical techniques are used illustrate the
main challenges of contemporary social sciences: (1) how to develop models that are generalizable and
still applicable in specific cases, and (2) how to scale up the processes of interactions of a few agents to
interactions among many agents.
Key Words: Agent-based models; empirical applications; social science methods
INTRODUCTION
In recent years, agent-based modeling (ABM) has
frequently been considered a promising quantitative
methodology for social science research (see
Janssen 2002, Parker et al. 2003, Tesfatsion and
Judd 2006). Agent-based modeling
is
the
computational study of social agents as evolving
systems of autonomous interacting agents. The
technical methodology of computational models of
multiple interacting agents was initially developed
during the 1940s when John von Neumann started
to work on cellular automata (von Neumann 1966).
A cellular automaton is a set of cells, where each
cell can be in one of multiple predefined states, such
as forest or farmland. Changes in the state of a cell
occur based on the prior states of the cell’s own
history and the history of