Agent-based modeling in the social and behavioral sciences.

Scholars in recent years applying the sciences of complexity to social and behavioral phenomena have suffered from two distinct problems. One group of studies focused on the production of revealing metaphors at the cost of analytical rigor. Another set of studies developed mathematical models and techniques that remained remote to even sophisticated students of the sciences of complexity. During the 1990s, however, a growing number of social scientists interested in complex phenomena, and dissatisfied with traditional research methodologies, sought new approaches for exploring the complexities of social dynamics. One of the developments emerging from this period was the use of agent-based modeling (ABM) and simulation to examine how social phenomena are created, maintained and even dissolved. These models, although diverse in their applications and approaches, generally attempt to create “microworlds” or “would-be worlds” in a computer with the goal of determining how the interactions and varied behaviors of individual agents produce structure and pattern (Casti, 1997). These models can be seen as a middle ground between the metaphor of many complex systems studies and the remote mathematics of many studies in the 1980s. ABM is essentially the application of autonomous agents programmed to behave in different ways when interacting with adjacent agents or different aspects of their environment on a dimensional grid. An agent, say “red”, may be programmed to exhibit one behavior, when, for example in contact with “blue” and “green”, and another when in contact with another “red” and “yellow”. The important point is that