Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars

  • Nick Malleson
  • Linda See
  • Andrew Evans
  • Alison Heppenstall
Part of the Intelligent Systems Reference Library book series (ISRL, volume 52)


Agent-based methods are one approach for modelling complex social systems but one issue with these models is the large number of parameters that require estimation. This chapter examines the effect of using a genetic algorithm (GA) for the parameter estimation of an agent-based model (ABM) of burglary. One of the main issues encountered in the implementation was the computation time required to run the algorithm. Nevertheless a set of preliminary results were obtained, which indicated that visibility is the most important parameter in the decision of whether to burgle a house while accessibility was the least important. Such tools may eventually provide the means to gain a greater understanding of the factors that determine criminological behaviour.


Genetic Algorithm Collective Efficacy Output Area Crime Data Routine Activity Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Nick Malleson
    • 1
  • Linda See
    • 2
    • 3
  • Andrew Evans
    • 1
  • Alison Heppenstall
    • 1
  1. 1.School of GeographyUniversity of LeedsLeedsUK
  2. 2.International Institute of Applied Systems AnalysisLaxenburgAustria
  3. 3.Centre for Applied Spatial AnalysisUniversity College London (UCL)LondonUK

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