Gilbert defines Agent-based modelling (ABM) as "a computational method that enables a researcher to create, analyse, and experiment with models composed of agents that interact within an environment” (Gilbert 2007). The ABM approach facilitates investigation of social dynamics, where a collection of agents is acting autonomously in various (social) contexts. The massive parallel and local interactions of agents can give rise to path dependencies, dynamic returns and their interaction. In such an environment, global phenomena as the development and diffusion of technologies, the emergence of networks, herd-behaviour etc. which may caused a transformation of the observed system can be modelled. The approach focuses on depicting the agents, their relationships and the processes governing their transformation, i.e. the social behaviour of the agents is at forefront of consideration.
The application of ABM offers two major advantages (Gilbert and Troitzsch 1999):
- capability to show how collective phenomena come about and how the interaction of the autonomous and heterogeneous agents leads to their genesis. ABM supports isolation of critical behaviour in order to identify agents that more than others drive the collective result of a (dynamic) system. Such simulations also endeavour to single out points of time, where the system exhibits qualitative rather than sheer quantitative change.
- possibility to use agent-based models as computational laboratories to explore various institutional arrangements, potential paths of development so as to assist and guide e.g. firms, policy makers etc. in their particular decision contexts.
Gilbert, N. and Troitzsch, K. (1999) Simulation for the Social Scientist, Berkshire, UK: Open University Press.
Gilbert, N. (2007) Agent-Based Models, London: Sage.