Tutorial on agent-based modelling and simulation

see http://www.wintersim.org/pastprog.htm). This article provides a brief introduction to ABMS. We illustrate the main concepts of agent-based modelling *Correspondence: CM Macal, Center for Complex Adaptive Agent Systems Simulation, Decision & Information Sciences Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439-4867, USA. E-mail: [email protected] Journal of Simulation (2010) 4, 151–162 r 2010 Operational Research Society Ltd. All rights reserved. 1747-7778/10 www.palgrave-journals.com/jos/ (Section 2), discuss some recent applications across a variety of disciplines (Section 3), and identify methods and toolkits for developing agent models (Section 4). 2. Agent-based modelling 2.1. Agent-based modelling and complexity ABMS can be traced to investigations into complex systems (Weisbuch, 1991), complex adaptive systems (Kauffman, 1993; Holland, 1995), and artificial life (Langton, 1989), known as ALife (see Macal (2009) for a review of the influences of investigations into artificial life on the development of agent-based modelling and the article by Heath and Hill in this issue for a review of other early influences). Complex systems consist of interacting, autonomous components; complex adaptive systems have the additional capability for agents to adapt at the individual or population levels. These collective investigations into complex systems sought to identify universal principles of such systems, such as the basis for self-organization, emergent phenomenon, and the origins of adaptation in nature. ABMS began largely as the set of ideas, techniques, and tools for implementing computational models of complex adaptive systems. Many of the early agent-based models were developed using the Swarm modelling software designed by Langton and others to model ALife (Minar et al, 1996). Initially, agent behaviours were modelled using exceedingly simple rules that still led to exceedingly complex emergent behaviours. In the past 10 years or so, available agent-based modelling software tools and development environments have expanded considerably in both numbers and capabilities. Following the conventional definition of simulation, we use the term ABMS in this article to refer to both agent-based simulation, in which a dynamic and timedependent process is modelled, and more general kinds of agent-based modelling that includes models designed to do optimization (see, eg, Olariu and Zomaya, 2006) or search (see, eg, Hill et al, 2006). For example, particle swarm optimization and ant optimization algorithms are both inspired by agent-based modelling approaches and are used to achieve an end (optimal) state rather than to investigate a dynamic process, as in a simulation. 2.2. Structure of an agent-based model A typical agent-based model has three elements: 1. A set of agents, their attributes and behaviours. 2. A set of agent relationships and methods of interaction: An underlying topology of connectedness defines how and with whom agents interact. 3. The agents’ environment: Agents interact with their environment in addition to other agents. A model developer must identify, model, and program these elements to create an agent-based model. The structure of a typical agent-based model is shown in Figure 1. Each of the components in Figure 1 is discussed in this section. A computational engine for simulating agent behaviours and agent interactions is then needed to make the model run. An agent-based modelling toolkit, programming language or other implementation provides this capability. To run an agent-based model is to have agents repeatedly execute their behaviours and interactions. This process often does, but is not necessarily modelled to, operate over a timeline, as in time-stepped, activity-based, or discrete-event simulation