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. 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’.

Anylogic tutorials