Abstract
This chapter discusses the use of agent based and laboratory simulation methods for investigating preventive measures against crime. We distinguish anticipatory prevention that attempts to preclude that offenders and targets meet while no guardians are present, and mitigating prevention that when such meetings take place attempt to deflect the seriousness of crime or indeed interrupt its execution. After a brief discussion on how simulation studies can contribute to evaluation of measures, various agent based simulation studies on police strategies (anticipatory prevention) and laboratory studies on victim training (mitigating prevention) are reviewed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The classical (non-criminological) example is Schelling’s (<CitationRef CitationID="CR29" >1987</Citation Ref>) differential moving tendency simulation: white and black pawns are randomly distributed on a chessboard. Repeatedly an arbitrary pawn may select to be relocated to a new square, where white pawns prefer squares next to other white pawns just a little bit more than next to black pawns and vice versa. In no time (i.e. in not too many iterations of the simulation) under this scheme almost all pawns are segregated in black and white neighbourhoods. (Notice that this simulation can be easily done “by hand” and does not need a computer).
- 2.
For example Gerritsen’s (<CitationRef CitationID="CR16" >2011</Citation Ref>) work on ABM modelling of aggression in crowds.
- 3.
There is an interesting research tradition in bringing ordinary people in such environments and look whether they will displaying criminal behaviour, as a function of environmental queues, e.g. in tax evasion simulation (Webley, Robben, Elffers, & Hessing, <CitationRef CitationID="CR35" >1991</Citation Ref>). Van Bavel (<CitationRef CitationID="CR33" >forthcoming</Citation Ref>) is reporting on theft experiments with ordinary people in the role of offenders. The crux in that type of experiments is of course how to manipulate the motivation of the prospective offenders.
- 4.
Notice that authors cited here did not present their work in terms of anticipatory prevention, which is a term introduced in the present chapter.
References
Birks, D., & Elffers, H. (2014). Agent-based assessment of criminological theory. In G. Bruinsma & D. Weisburd (Eds.), Encyclopaedia of criminology (pp. 19–32). New York: Springer.
Birks, D., Townsley, M., & Stewart, A. (2012). Generative models of crime: Using simulation to test criminological theory. Criminology, 50(1), 221–254.
Birks, D., Townsley, M., & Stewart, A. (2014). Emergent regularities of interpersonal victimisation: An agent-based investigation. Journal of Research in Crime and Delinquency, 51(1), 119–140.
Bosse, T., Elffers, H., & Gerritsen, C. (2010). Simulating the dynamical interaction of offenders, targets and guardians. Crime Patterns and Analysis, 3(1), 51–66.
Bosse, T., & Gerritsen, C. (2010). An agent-based framework to support crime prevention. In Proceedings of the Ninth International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS‘10 (pp. 525–532). New York: ACM Press.
Bosse, T., Gerritsen, C., de Man, J., & Treur, J. (2013). Effects of virtual training on emotional response: A comparison between different emotional regulation strategies. In Proceedings of the 7th International Conference on Brain and Health Informatics, BHI‘13 (Lecture notes in artificial intelligence, pp. 21–31). Berlin: Springer.
Bosse, T., Gerritsen, C., & Klein, M. (2010). Predicting the development of juvenile delinquency by simulation. Berlin: Springer.
Brantingham, P. L., & Brantingham, P. J. (2004). Computer simulation as a tool for environmental criminologists. Security Journal, 17(1), 21–30.
Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.
de Kort, Y. A. W., IJsselsteijn, W. A., Haans, A., Lakens, D., Kainauskaite, I., & Schietecat, A. (2014). De-escalate: Defusing escalating behaviour through the use of interactive light scenarios. www.de-escalate.nl
de Man, J. (2014). Analysing emotional video using consumer EEG hardware. In Proceedings of the 16th International Conference on Human-Computer Interactions, HCI‘14 (pp. 729–738). Berlin: Springer.
Eck, J., & Liu, L. (2008). Contrasting simulated and real experiments in crime prevention. Journal of Experimental Criminology, 4, 195–213.
Epstein, J. (2006). Generative social science: Studies in agent-based computational modeling. Princeton: Princeton University Press.
Farrington, D. P., & Welsh, B. C. (2002). Effects of improved street lighting on crime: A systematic review (Home Office Research study, Vol. 251). London: Home Office.
Felson, M. (2006). Crime and nature. Thousand Oaks, CA: Sage.
Gerritsen, C. (2011). Using ambient intelligence to control aggression in crowds. Proceedings of the Fifth International Workshop on Human Aspects in Ambient Intelligence (pp. 53–56).
Groff, E. (2007). Simulation for theory testing and experimentation: An example using routine activity theory and street robbery. Journal of Quantitative Criminology, 23(2), 75–103.
Groff, E. R. (2008). Characterizing the spatio-temporal aspects of routine activities and the geographic distribution of street robbery. In L. Liu & J. Eck (Eds.), Artificial crime analysis systems: Using computer simulations and geographic information systems (pp. 226–251). Hershey, PA: Idea Group.
Groff, E. R., & Birks, D. (2008). Simulating crime prevention strategies: A look at the possibilities. Policing, 1, 1–10.
Johnson, S. (2008). Repeat burglary victimisation: A tale of two theories. Journal of Experimental Criminology, 4, 215–240.
Lindegaard, M. R., Bernasco, W., Jacques, S., & Zevenbergen, B. (2013). Posterior gains and immediate pains: Offender emotions before, during and after robberies. In J. L. Van Gelder, H. Elffers, D. Reynald, & D. Nagin (Eds.), Affect and cognition in criminal decision making: Between rational choices and lapses of self-control (pp. 58–76). New York: Routledge.
Liu, L., Wang, X., Eck, J., & Liang, J. (2005). Simulating crime events and crime patterns in a RA/CA model. Reading, PA: Idea Publishing.
Malleson, N., Evans, A., & Jenkins, T. (2009). An agent-based model of burglary. Environment and Planning B: Planning and Design, 36, 1103–1123.
Marchione, E., Johnson, S. D., & Wilson, A. (2014). Modelling maritime piracy: A spatial approach. Journal of Artificial Societies and Social Simulation, 17(2), 9. http://jasss.soc.surrey.ac.uk/17/2/9.htnl.
Melo, A., Belchior, M., & Furtado, V. (2006). Analyzing police patrol routes by simulating the physical reorganization of agents. In J. S. Sichman & L. Antunes (Eds.), Proceedings of the 6th International Workshop on Multi-Agent-Based Simulation (pp. 99–114). Berlin: Springer.
Reynald, D. M. (2011). Guarding against crime: Measuring guardianship within routine activity theory. Farnham, UK: Ashgate.
Schelling, T. (1987). Micro motives and macro behaviour. New York: W.W. Norton & Co.
Townsley, M. K., & Birks, D. J. (2008). Building better crime simulations: Systematic replication and the introduction of incremental complexity. Journal of Experimental Criminology, 4(3), 309–333.
Townsley, M., & Johnson, S. (2008). The need for systematic replication and tests of validity in simulation. In L. Liu & J. Eck (Eds.), Artificial crime analysis systems: Using computer simulations and geographic information systems (pp. 1–18). Hershey, PA: Information Science Reference.
van Baal, P. (2004). Computer simulations of criminal deterrence: From public policy to local interaction to individual behavior. Den Haag, The Netherlands: BJU Boom Juridische uitgevers.
van Bavel, M. (forthcoming). Do offenders heed guardians?
van Bavel, M., & Elffers, H. (2013). Experiments in guardianship research. In B. C. Welsh, A. A. Braga, & G. J. N. Bruinsma (Eds.), Experimental criminology: Prospects for advancing science and public policy (pp. 90–107). New York: Cambridge University Press.
Webley, P., Robben, H. S. J., Elffers, H., & Hessing, D. J. (1991). Tax evasion: An experimental approach. Cambridge: Cambridge University Press.
Yang, R., Ford, B., Tambe, M., & Lemieux, A. (2014). Adaptive resource allocation for wildlife protection against illegal poachers. In Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS (pp. 453–460). New York: ACM.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Gerritsen, C., Elffers, H. (2017). Investigating Prevention by Simulation Methods. In: LeClerc, B., Savona, E. (eds) Crime Prevention in the 21st Century. Springer, Cham. https://doi.org/10.1007/978-3-319-27793-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-27793-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27791-2
Online ISBN: 978-3-319-27793-6
eBook Packages: Law and CriminologyLaw and Criminology (R0)