Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
Theories and Simulations of Complex Social Systems

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 52))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Andresen, M., Malleson, N.: Testing the stability of crime patterns: implications for theory and policy. J. Res. Crime Delinquency 48(1), 58–82 (2011)

    Article  Google Scholar 

  2. Bäck, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993)

    Article  Google Scholar 

  3. Beasley, D., Bull, D.R., Martin, R.R.: An overview of genetic algortihms: Part 1. Fundamentals. Univ. Comput. 15(2), 58–69 (1993)

    Google Scholar 

  4. Bhavnani, R., Miodownik, D., Nart, J.: REsCape: an agent-based framework for modeling resources, ethnicity, and conflict. J. Artif. Soc. Soc. Simul. 11(2), 7 (2008) [Online]. http://jasss.soc.surrey.ac.uk/11/2/7.html. Accessed 16 June 2011

  5. Birks, D.: Computational criminology: a multi-agent simulation of volume crime activity. Presentation to the British Society of Criminology Conference, University of Leeds, UK (2005)

    Google Scholar 

  6. Birks, D.: Synthesis over analysis: using agent-based models to examine the interactions of crime. Presentation at the Fifth National Crime Mapping Conference, London (2007)

    Google Scholar 

  7. Birks, D.J., Donkin, S., Wellsmith, M.: Synthesis over analysis: towards an ontology for volume crime simulation. In: Liu, L., Eck, J. (eds.) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, pp. 160–192. Information Science Reference, Hershey (2008)

    Chapter  Google Scholar 

  8. Birks, D., Townsley, M., Stewart, A.: Generative explanations of crime: using simulation to test criminological theory. Criminology 50(1), 221–254 (2012)

    Article  Google Scholar 

  9. Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Nat. Acad. Sci. 99, 7280–7287 (2002)

    Article  Google Scholar 

  10. Brantingham, P.J., Brantingham, P.L.: Notes on the geometry of crime. In: Brantingham, P.J., Brantingham, P.L. (eds.) Environmental Criminology (pp. 27–54). Waveland Press, Prospect Heights (1981)

    Google Scholar 

  11. Brantingham, P.L., Brantingham, P.J.: Nodes, paths and edges: considerations on the complexity of crime and the physical environment. J. Environm. Psychol. 13(1), 3–28 (1993)

    Article  Google Scholar 

  12. Brantingham, P.L., Brantingham, P.J.: Mapping crime for analytic purposes: location quotients, counts, and rates. In: Weisburd, D., McEwen, T. (eds.) Crime Mapping and Crime Prevention, Volume 8 of Crime Prevention Studies, pp. 263–288. Criminal Justice Press, Monsey (1998)

    Google Scholar 

  13. Brantingham, P.L., Brantingham, P.J.: Computer simulation as a tool for environmental criminologists. Secur. J. 17(1), 21–30 (2004)

    Article  Google Scholar 

  14. Brantingham, P.L., Glasser, U., Kinney, B., Singh, K., Vajihollahi, M.: A computational model for simulating spatial aspects of crime in urban environments. Proceedings of the 2005 IEEE International Conference on Systems Man and Cybernetics 4, 3667–3674 (2005)

    Article  Google Scholar 

  15. Brantingham, P.L., Glasser, U., Kinney, B., Singh, K., Vajihollahi, M.: Modeling urban crime patterns: viewing multi-agent systems as abstract state machines. Proceedings of the 12th International Workshop on Abstract State Machines, pp. 101–117, Paris (2005)

    Google Scholar 

  16. Brantingham, P.L., Glasser, U., Jackson, P., Kinney, B., Vajihollahi, M.: Mastermind: computational modeling and simulation of spatiotemporal aspects of crime in urban environments. In: Liu, L., Eck, J., (eds.) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, chapter 13, pp. 252–280. IGI Global, Hershey (2008)

    Google Scholar 

  17. Chainey, S., Ratcliffe, J.: GIS and Crime Mapping, 1st edn. Wiley, Chichester (2005)

    Book  Google Scholar 

  18. Cherif, A., Yoshioka, H., Ni, W., Bose, P.: Terrorism: Mechanisms of Radicalization Processes, Control of Contagion and Counter-Terrorist Measures. Santa Fe Institute Working Paper [Online] (2009). http://tuvalu.santafe.edu/events/workshops/images/7/7e/TerrorismWorkingPaper.pdf. Accessed 16 June 2011

  19. Cilliers, P.: Complexity and Postmodernism. Routledge, Bury St Edmonds (1998)

    Google Scholar 

  20. Clarke, R.V., Cornish, D.B.: Modeling offenders’ decisions: a framework for research and policy. Crime Justice 6, 147–185 (1985)

    Article  Google Scholar 

  21. Cohen, L., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44, 588–608 (1979)

    Article  Google Scholar 

  22. Crooks, A.: Constructing and implementing an agent-based model of residential segregation through vector GIS. CASA Working Paper Series, Paper 133 (2008). http://eprints.ucl.ac.uk/15185/1/15185.pdf

  23. Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  24. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, West Sussex (2001)

    MATH  Google Scholar 

  25. Di Paolo, E.A., Noble, J., Bullock, S.: Simulation models as opaque thought experiments. Seventh International Conference on Artificial Life, pp. 497–506. MIT Press, Cambridge (2000)

    Google Scholar 

  26. Dray, A., Mazerolle, L., Perez1, P., Ritter, A.: Policing Australia’s heroin drought: using an agent-based model to simulate alternative outcomes. J. Exp. Criminol. 4, 267–287 (2008)

    Google Scholar 

  27. Eck, J., Weisburd, D.: Crime places in crime theory. In: Eck, J., Weisburd, D. (eds.) Crime and Place, pp. 1–33. Criminal Justice Press, Monsey (1995)

    Google Scholar 

  28. Eck, J.E., Liu, L.: Contrasting simulated and empirical experiments in crime prevention. J. Exp. Criminol. 4(3), 195–213 (2008)

    Article  Google Scholar 

  29. Egesdal, M., Fathauer, C., Louie, K., Neuman, J., Mohler, G., Lewis, E.: Statistical and Stochastic Modeling of Gang Rivalries in Los Angeles. SIAM Undergraduate Research Online (SIURO) 3. [Onine] (2010). http://www.siam.org/students/siuro/vol3/S01045.pdf. Accessed 16 June 2011

  30. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    Google Scholar 

  31. Efstratiadis, A., Koutsoyiannis, D.: On the practical use of multiobjective optimisation in hydrological model calibration, European Geosciences Union General Assembly 2009, Geophysical Research Abstracts, vol. 11. European Geosciences Union, Vienna (2009)

    Google Scholar 

  32. Epstein, J.M., Axtell, R.L.: Growing Artificial Societies: Social Science from the Bottom Up. MIT Press, Cambridge (1996)

    Google Scholar 

  33. Evans, A.J.: A sketchbook for ethics in agent-based modelling. Association of American Geographers (AAG) Annual Meeting, 23–26 February 2012, New York [online] (2012). http://www.geog.leeds.ac.uk/presentations/12-2/12-2.pptx

  34. Expósito, R.R., Taboada, G.L., Ramos, S., Touriño, J., Doallo, R.: Performance analysis of HPC applications in the cloud. Future Gener. Comput. Syst. 29(1), 218–229 (2013). doi:10.1016/j.future.2012.06.009

    Article  Google Scholar 

  35. Farmer, J.D., Foley, D.: The economy needs agent-based modelling. Nature 460, 685–686 (2009)

    Article  Google Scholar 

  36. Grimm, V., Railsback, S.F.: Individual-Based Modeling and Ecology. Princeton University Press, Princeton (2005)

    MATH  Google Scholar 

  37. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S., Huse, G., Huth, A., Jepsen, J.U., Jørgensen, C., Mooij, W.M., Müller, B., Pe’er, G., C., Piou, Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Rüger, N., Strand, E., Souissi, S., Stillman, R.A., Vabø, R., Visser, U., DeAngelis, D.L.: A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198, 115–126 (2006)

    Google Scholar 

  38. Groff, E.: Exploring The Geography Of Routine Activity Theory: A Spatio-Temporal Test Using Street Robbery. PhD thesis, University of Maryland (2006)

    Google Scholar 

  39. Groff, E.: Simulation for theory testing and experimentation: An example using routine activity theory and street robbery. Journal of Quantitative Criminology, 23:75–103 (2007a)

    Google Scholar 

  40. Groff, E.: Situating simulation to model human spatio-temporal interactions: An example using crime events. Transactions in GIS, 11(4):507–530 (2007b)

    Google Scholar 

  41. Groff, E., Mazerolle, L. (2008) Simulated experiments and their potential role in criminology and criminal justice. Journal of Experimental Criminology, 4(3):187–193

    Google Scholar 

  42. Goldberg, D.: Genetic Algorithms: in Search, Optimisation and Machine Learning. Addison Wesley, Crawfordsville (1989)

    Google Scholar 

  43. Goldstein, N.C.: Brains versus brawn—comparative strategies for the calibration of a cellular automata-based urban growth model. In: Atkinson, P., Foody, G., Darby, S., Wu, F. (eds.) Geodynamics, pp. 249–272. CRC Press, Boca Raton (2004)

    Chapter  Google Scholar 

  44. Hayslett-McCall, K. L., Qiu, F., Curtin, K. M., Chastain, B., Schubert, J., Carver, V.: The simulation of the journey to residential burglary. In: Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, chapter 14. IGI Global, Hershey (2008)

    Google Scholar 

  45. Heppenstall, A.J., Evans, A.J., Birkin, M.H.: Genetic algorithm optimisation of an agent-based model for simulating a retail market. Environ. Plan. B: Plan. Design 34, 1051–1070 (2007)

    Google Scholar 

  46. Holland, J.: Adaption in Natural and Artificial Systems. MIT Press, Cambridge (1975)

    MATH  Google Scholar 

  47. Holland, J.: Genetic algorithms. Scientific American, July 1992, 66–72

    Google Scholar 

  48. Huddleston, S.H., Learmonth, G.P., Fox, J.: Changing Knives into Spoons. Proceedings of the 2008 IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, VA, USA, April 25 (2008) [Online]. http://www.sys.virginia.edu/sieds09/papers/0047_FPM2SimDM-02.pdf. Accessed 16 June 2011

  49. Johnson, S.D., Bernasco, W., Bowers, K.J., Elffers, H., Ratcliffe, J., Rengert, G.F., Townsley, M.: Near repeats: a cross national assessment of residential burglary. J. Quant. Criminol. 23(3), 201–219 (2007)

    Article  Google Scholar 

  50. Johnson, S.D., Bowers, K.J.: Permeability and burglary risk: are Cul-de-sacs safer? J. Quant. Criminol. 26(1), 89–111 (2009)

    Article  Google Scholar 

  51. Knudsen, D.C., Fotheringham, A.S.: Matrix comparison, goodness-of-fit, and spatial interaction modeling. Int. Reg. Sci. Rev. 10, 127–147 (1986)

    Article  Google Scholar 

  52. Kongmuang, C.: Modelling crime: a spatial microsimulation approach. Ph.D. thesis, School of Geography, University of Leeds, Leeds (2006)

    Google Scholar 

  53. Li, X., Yang, Q.S., Liu, X.-P.: Genetic algorithms for determining the parameters of cellular automata in urban simulation. Sci. China Ser. D: Earth Sci. 50(12), 1857–1866 (2007)

    Article  MathSciNet  Google Scholar 

  54. Liu, L., Wang, X., Eck, J., Liang, J.: Simulating crime events and crime patterns in a RA/CA models. In: Wang, F. (ed.) Geographic Information Systems and Crime Analysis, pp. 197–213. Idea Publishing, Reading (2005)

    Google Scholar 

  55. Lustick, I.S.: Defining Violence: a plausibility probe using agent-based modeling. Paper prepared for LiCEP, Princeton University, May 12–14, 2006 [Online]. http://www.prio.no/files/file48070_lustick_violdef_foroslo_v2.pdf. Accessed 16 June 2011

  56. Malleson, N.: An agent-based model of burglary in Leeds. Master’s thesis, University of Leeds, School of Computing, Leeds [Online] (2006). http://www.geog.leeds.ac.uk/fileadmin/downloads/school/people/postgrads/n.malleson/mscproj.pdf. Accessed 16 June 2011

  57. Malleson, N.: Agent-based modelling of burglary. Ph.D. thesis, School of Geography, University of Leeds (2010)

    Google Scholar 

  58. Malleson, N., Evans, A.J., Jenkins, T.: An agent-based model of burglary. Environ. Plan. B: Plan. Des. 36, 1103–1123 (2009)

    Article  Google Scholar 

  59. Malleson, N., Heppenstall, A.J., Evans, A.J., See, L.M.: Evaluating an agent-based model of burglary. Working paper 10/1, School of Geography, University of Leeds, UK. January 2010 [Online]. http://www.geog.leeds.ac.uk/fileadmin/downloads/school/research/wpapers/10_1.pdf. Accessed 16 June 2011

  60. Malleson, N., Heppenstall, A.J., See, L.M.: Crime reduction through simulation: an agent-based model of burglary. Comput. Environ. Urban Syst. 34, 236–250 (2010b)

    Article  Google Scholar 

  61. Malleson, N., See, L.M., Evans, A.J., Heppenstall, A.J.: Implementing comprehensive offender behaviour in a realistic agent-based model of burglary. Simul.: Trans. Soc. Model. Simul. Int. 88(1), 50–71 (2010)

    Google Scholar 

  62. Malleson, N., Evans, A.J., Heppenstall, A.J., See, L.M.: Crime from the ground-up: agent-based models of burglary. Geographical Compass (Submitted)

    Google Scholar 

  63. Malleson, N., Evans, A., Heppenstall, A., See, L..: The Leeds Burglary Simulator. Informatica e diritto special issue: Law and Computational Social Science 1 211–222 (2013)

    Google Scholar 

  64. Maros, I., Mitra, G.: Simplex algorithms. In: Beasley, J.E. (ed.) Advances in Linear and Integer Programming, pp. 1–46. Oxford Science, Oxford (1996)

    Google Scholar 

  65. Melo, A., Belchior, M., Furtado, V.: Analyzing police patrol routes by simulating the physical reorganization of agents. In: Sichman, J.S., Antunes, L. (eds.) MABS, Lecture Notes in Computer Science, vol. 3891, pp. 99–114. Springer, Heidelberg (2005)

    Google Scholar 

  66. Michalewicz, M.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)

    Google Scholar 

  67. Michalewicz, Z., Janikow, C.: Genetic algorithms for numerical. Optim. Stat. Comput. 1(2), 75–91 (1991)

    Article  Google Scholar 

  68. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    Google Scholar 

  69. Moss, S., Edmonds, B.: Towards good social science. J. Artif. Soc. Soc. Simul. 8(4), 13 (2005)

    Google Scholar 

  70. Omer, I.: How ethnicity influences residential distributions: an agent-based simulation. Environ. Plan. B: Plan. Des. 32(5), 657–672 (2005)

    Article  Google Scholar 

  71. Rengert, G.F., Wasilchick, J.: Suburban burglary: a time and a place for everything. Charles Thomas, Springfield (1985)

    Google Scholar 

  72. Schmidt, B.: The Modelling of Human Behaviour. SCS Publications, Erlangen (2000)

    Google Scholar 

  73. Schmidt, B.: How to give agents a personality. Proceedings of the 3rd Workshop on Agent- Based Simulation, April 7–9, Passau, Germany (2002)

    Google Scholar 

  74. Schelling, T.C.: Dynamic models of segregation. J. Math. Sociol. 1, 143–186 (1971)

    Article  Google Scholar 

  75. Shan, J., Alkheder, S., Wang, J.: Genetic algorithms for the calibration of cellular automata urban growth modeling. Photogram. Eng. Remote Sens. 74(10), 1267–1277 (2008)

    Article  Google Scholar 

  76. Shaw, C., McKay, H.: Juvenile Delinquency and Urban Areas. University of Chicago Press, Chicago (1942)

    Google Scholar 

  77. Stonedahl, F.J.: Genetic algorithms for the exploration of parameter spaces of agent-based models. Unpublished Ph.D. thesis, Northwestern University, Evanston (2011). http://forrest.stonedahl.com/thesis/forrest_stonedahl_thesis.pdf

  78. Tesfatsion, L., Judd, K.L.: Handbook of Computational Economics: Agent-Based Computational Economics. North Holland, Amsterdam (2006)

    Google Scholar 

  79. Urban, C.: PECS: a reference model for the simulation of multi-agent systems. In: Suleiman, R., Troitzsch, K.G., Gilbert, N. (eds.) Tools and Techniques for Social Science Simulation, Chapter 6, pp. 83–114. Physica, Heidelberg (2000)

    Google Scholar 

  80. van Baal, P.: Computer Simulations of Criminal Deterrence: From Public Policy to Local Interaction to Individual Behaviour. Boom Juridische Uitgevers, Den Haag, The Netherlands (2004)

    Google Scholar 

  81. Vickers, D., Rees, P.: Creating the UK national statistics 2001 output area classification. J. Royal Stat. Soc. Ser. A 170(2), 379–403 (2007)

    Article  MathSciNet  Google Scholar 

  82. Vrugt, J.A., Gupta, H.V., Bastidas, L.A., Bouten, W., Sorooshian, S.: Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour. Res. 39(8), 1214 (2003). doi:10.1029/2002WR001746

    Google Scholar 

  83. Wang, X., Liu, L., Eck, J.: Crime simulation using gis and artificial intelligent agents. In: Liu, L., Eck, J. (eds.) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, chapter 11. Information Science Reference, Hershey (2008)

    Google Scholar 

  84. Weisburd, D., Bushway, S., Lum, C., Ang, S.-M.: Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. Criminology 42(1), 283–321 (2004)

    Article  Google Scholar 

  85. Weisburd, D., Bruinsma, G.J.N., Bernasco, W.: Units of analysis in geographic criminology: historical development, critical issues, and open questions. In: Weisburd, D., Bernasco, W., Bruinsma, G.J.N. (eds.) Putting Crime in Its Place. Units of Analysis in Geographic Criminology (pp. 3–31). Springer, Heidelberg (2009)

    Google Scholar 

  86. Wiese, T.: Global Optimization Algorithms—Theory and Applications, 2nd edn. University of Kassel, Distributed Systems Group (2009). http://www.it-weise.de

  87. Winoto, P.: A simulation of the market for offenses in multiagent systems: is zero crime rates attainable? In: Sichman, J.S., Bousquet, F., Davidsson, P., (eds.) MABS, Lecture Notes in Computer Science, vol. 2581, pp. 181–193. Springer, Heidelberg (2003)

    Google Scholar 

  88. Xiao, W.: A unified conceptual framework for geographical optimization using evolutionary algorithms. Ann. Assoc. Am. Geogr. 98(4), 795–817 (2008)

    Article  Google Scholar 

  89. Yapo, P.O., Gupta, H.V., Sorooshian, S.: Multi-objective global optimization for hydrological models. J. Hydrol. 204, 83–97 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Geography, University of Leeds, Leeds, UK

    Nick Malleson, Andrew Evans & Alison Heppenstall

  2. International Institute of Applied Systems Analysis, Laxenburg, Austria

    Linda See

  3. Centre for Applied Spatial Analysis, University College London (UCL), London, UK

    Linda See

Authors
  1. Nick Malleson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linda See
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alison Heppenstall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nick Malleson .

Editor information

Editors and Affiliations

  1. The IRMACS Centre, The MoCSSy Program, Simon Fraser University, Burnaby, Canada

    Vahid Dabbaghian

  2. Department of Computer Science, Troy University, Troy, Alabama, USA

    Vijay Kumar Mago

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Malleson, N., See, L., Evans, A., Heppenstall, A. (2014). Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars. In: Dabbaghian, V., Mago, V. (eds) Theories and Simulations of Complex Social Systems. Intelligent Systems Reference Library, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39149-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39149-1_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39148-4

  • Online ISBN: 978-3-642-39149-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation