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For example, a perusal of the programme for a recent Winter Simulation Conference revealed that 27 papers had the word ‘agent’ in the title or abstract (see ). Several indicators of the growing interest in agent-based modelling include the number of conferences and workshops devoted entirely to or having tracks on agent-based modelling, the growing number of peer-reviewed publications in discipline-specific academic journals across a wide range of application areas as well as in modelling and simulation journals, the growing number of openings for people specializing in agent-based modelling, and interest on the part of funding agencies in supporting programmes that require agent-based models. These applications have been made possible by advances in the development of specialized agent-based modelling software, new approaches to agent-based model development, the availability of data at increasing levels of granularity, and advancements in computer performance. Other agent-based models are large scale in nature, in which a system is modelled in great detail, meaning detailed data are used, the models have been validated, and the results are intended to inform policies and decision making. Some of these applications are small but elegant models, which include only the essential details of a system, and are aimed at developing insights into a social process or behaviour. Applications range from modelling agent behaviour in the stock market ( Arthur et al, 1997) and supply chains ( Macal, 2004a) to predicting the spread of epidemics ( Bagni et al, 2002) and the threat of bio-warfare ( Carley et al, 2006), from modelling the adaptive immune system ( Folcik et al, 2007) to understanding consumer purchasing behaviour ( North et al, 2009), from understanding the fall of ancient civilizations ( Kohler et al, 2005) to modelling the engagement of forces on the battlefield ( Moffat et al, 2006) or at sea ( Hill et al, 2006), and many others. Agent-based modelling offers a way to model social systems that are composed of agents who interact with and influence each other, learn from their experiences, and adapt their behaviours so they are better suited to their environment.Īpplications of agent-based modelling span a broad range of areas and disciplines. The emphasis on modelling the heterogeneity of agents across a population and the emergence of self-organization are two of the distinguishing features of agent-based simulation as compared to other simulation techniques such as discrete-event simulation and system dynamics. Patterns, structures, and behaviours emerge that were not explicitly programmed into the models, but arise through the agent interactions.

By modelling systems from the ‘ground up’-agent-by-agent and interaction-by-interaction-self-organization can often be observed in such models.

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By modelling agents individually, the full effects of the diversity that exists among agents in their attributes and behaviours can be observed as it gives rise to the behaviour of the system as a whole.

Agents have behaviours, often described by simple rules, and interactions with other agents, which in turn influence their behaviours. Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling complex systems composed of interacting, autonomous ‘agents’.

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