Abstract
The application of agent-based modelling (ABM) to simulating dynamics within geographical systems has seen a considerable increase over the last decade. ABM allows the disaggregation of systems into individual components that can potentially have their own characteristics and rule sets. This is a powerful paradigm that can be exploited through simulation to further our knowledge of the workings of geographical systems. We present in this chapter an overview of ABM; the main features of an agent-based model are given, along with a discussion of what constitutes an agent-based model. The distinction between cellular automata (CA), microsimulation (MSM) and agent-based models are discussed along with the advantages and limitations of ABM for modelling geographical systems. We conclude with a brief discussion of important areas for further research.
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Notes
- 1.
While ABM deals with individual and not aggregate behaviour, often it is neither feasible nor desirable to model complete agent heterogeneity. Instead agents are often given a representative behaviour; thus we move from average aggregate behaviour to average individual behaviour. However, greater heterogeneity can be introduced by adding ‘noise’ to such agents.
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Crooks, A.T., Heppenstall, A.J. (2012). Introduction to Agent-Based Modelling. In: Heppenstall, A., Crooks, A., See, L., Batty, M. (eds) Agent-Based Models of Geographical Systems. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8927-4_5
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