Introduction to Agent-Based Modelling by Bruce Edmonds
Centre for Policy Modelling, Manchester Metropolitan University
For a workshop at the "Government Operational Research Service"
Many aspects of modern society are highly complex, in the sense that they are not easy to understand without taking into account the detailed interactions between the social actors that comprise it. For such cases mathematical and system dynamics models are insufficient to be useful for understanding what is going on or forecasting what might occur. Statistical models are useful when the detail of the interactions can be treated as noise, and such models can make useful projections into the future, but it is not clear when the assumptions behind such projections will fail (and hence the projections being wrong - qualitatively as well as the error rate).
Agent-based modelling (ABM) is a relatively new technique that has the potential to play a part in this. In this technique social actors (people, departments, firms, households etc.) are individually represented as separate entities within the simulation and the interactions between the actors are represented as separate interactions within the simulation. The entities in the simulation that correspond to the social actors are called "agents". Sets of "rules" determine the micro-level behaviour of each agent. Each agent may have different characteristics and, indeed, different rules. One then "runs" the simulation where (effectively in parallel) all the agents obey their rules and a complex web of interactions between them result. This "mess" of detail can then be abstracted/graphed/measured in various ways (very similar to the techniques we use to understand what is happening in society itself) to give us macro-level statistics and visualisations which can be compared with existing aggregate statistics.
Two examples of such ABM are presented: (1) a very simple one that illustrates how the detailed interactions of individuals can affect the global outcomes, and (2) a more complex descriptive model that illustrates some of the social complexity that such models can represent.
Unsurprisingly the increased expressive power of ABM comes with downsides, including: (a) such models require a lot of data in order to be able to validate them and (b) the models are so complex that it can be difficult to understand the model itself. As a result of these difficulties, ABMs should not be considered to give probabilistic forecasts, but rather possibilistic - that is, they can produce some of the possibilities that are inherent in the system, but not (reliably) the probabilities of each (nor indeed will they be able to produce all of the real possibilities). In the context of policy making this is particularly relevant to risk-analyses of policies, where one wants to know some of the possible ways a policy might go wrong. This will allow one to design and implement "early warning" systems ...