Social sciences often aspire to move beyond exploring individual behaviours and seek to understand how the interaction between individuals leads to large-scale outcomes. In this process, an understanding of complex systems requires more than understanding its parts. Agent-based modelling (ABM) is a bottom-up modelling approach in which macro-level system behaviour is modelled through the behaviours of micro-level autonomous, interacting agents. ABM can generate deep quantitative and qualitative insights into complex socio-economic, natural and man-made systems through simulating the interactions of processes, diversity and behaviours on different scales. Social interventions are generally designed to influence micro-level behaviour (e.g. the behaviour of an individual or household). Similarly, evaluations are generally interested in explaining micro-level behavioural changes. ABM, in contrast, allows macro-level mechanisms to be represented, making it well-suited to evaluate complex programmes and policies, for example, the evaluation of multi-intervention outreach programmes.

Recent ABM applications have been made possible by advances in the development of specialised agent-based modelling software, more granular and larger data sets and advancements in computer performance (Macal and North 2010). However, ABM is a complex and resource-intensive method: the implementation process requires expert modellers, the results can be difficult to understand and communicate, and the application of ABM can be costly.

What is involved?

The underlying premise of ABM is its ‘complex system-thinking’. That is, the complex world is comprised of numerous interrelated individuals, whose interactions create higher-level features. In ABM, such emergent phenomena are generated from the bottom up (Bonabeau 2002) by seeking to identify the underlying rules that govern the behaviours of whole systems. ABM presumes that simple rules behind individual actions can lead to coherent group behaviour, and that even a small change in these rules can radically change group behaviour.

A typical ABM has three main elements:

  1. Agents: Agents are autonomous. They are ‘active, initiating their actions to achieve their internal goals, rather than merely passive, reactively responding to other agents and the environment’ (Macal and North 2010, p. 153).
  2. The relationships between agents: ABM is concerned with two main questions: who interacts with whom (as agents only connect to a subset of agents – termed neighbours), and how these neighbours are connected (the topology of connectedness).
  3. Environments: Information about the environment in which agents interact may be needed beyond mere spatial location: while interacting with their environment, agents are constrained in their actions by the infrastructure, resources, capacities or links that the environment can provide.

These elements need to be identified, modelled and programmed to create an ABM.

Download an ABM case study here
Download a longer briefing on ABM here

Useful resources

Axelrod and Tesfatsion’s On-Line Guide for Newcomers to ABM provides a good introduction to ABM.

An introductory tutorial into the background and application of ABM:

Macal, C. and North, M. (2014) Introductory tutorial: Agent-based modeling and simulation. In Proceedings of the Winter Simulation Conference 2014, pp. 6–20. IEEE.

A widely read book that provides a simple overview of the methodology, including how to construct simple ABM:

Gilbert, N. and Troitzsch, K. (2005) Simulation for the Social Scientist, McGraw-Hill.