Journal of Experimental Criminology

, Volume 4, Issue 3, pp 195–213 | Cite as

Contrasting simulated and empirical experiments in crime prevention

Article

Abstract

This paper argues that simulated experiments of crime prevention interventions are an important class of research methods that compare favorably with empirical experiments. It draws on Popper’s demarcation between science and non-science (Conjectures and refutations: the growth of scientific knowledge. Routledge, London, 1992) and Epstein’s principle of generative explanation (Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton, NJ, 2006) to show how simulated experiments can falsify theory. The paper compares simulated and empirical experiments and shows that simulations have strengths that empirical methods lack, but they also have important relative weaknesses. We identify three threats to internal validity and two forms of external validity peculiar to simulated experiments. The paper also looks at the problem of validating simulations with crime data and suggests that simulations need to mimic the error production processes involved in the creation of empirical data. It concludes by listing ways simulations can be used to improve empirical experiments and discussing the differing operating assumption of empirical and simulation experimentalists.

Keywords

Agent based modeling Computers Crime patterns Crime prevention Experimental validity Experiments Measurement error Simulation 

References

  1. Batty, M., Desyllas, J., & Duxbury, E. (2003). Safety in numbers? Modelling crowds and designing control for the Notting Hill Carnival. Urban Studies, 40(8), 1573–1590.CrossRefGoogle Scholar
  2. Beavon, D. J. K., Brantingham, P. L., & Brantingham, P. J. (1994). The influence of street networks on the patterning of property offenses. In R. V. Clarke (Ed.), Crime prevention studies, volume 2 (pp. 115–148). Monsey, New York: Criminal Justice Press.Google Scholar
  3. Benenson, I., & Torrens, P. (2004). Geosimulation: Automata-based modeling of urban phenomena. New York: Wiley.Google Scholar
  4. Birks, D. J., Donkin, S., & Wellsmith, M. (2008). Synthesis over analysis: Towards an ontology for volume crime simulation. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 160–192). Hershey, PA: IGI Global.Google Scholar
  5. Brantingham, P. L., & Brantingham, P. J. (1993). Environment, routine, and situation: toward a pattern theory of crime. In R. V. Clarke, & M. Felson (Eds.), Routine activity, and rational choice (pp. 259–294). New Brunswick, NJ: Transaction Press.Google Scholar
  6. Brantingham, J., & Tita, G. (2008). Offender mobility and crime pattern formation from first principles. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 193–208). Hershey, PA: IGI Global.Google Scholar
  7. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally.Google Scholar
  8. Casti, J. L. (1997). Would-be-worlds: How simulation is changing the frontiers of science. New York: Wiley.Google Scholar
  9. Catalano, S. M. (2006). Criminal victimization, 2005. Washington, DC: U.S. Department of Justice, Bureau of Justice Statistics (http://www.ojp.usdoj.gov/bjs/pub/pdf/cv05.pdf).
  10. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago, IL: Rand McNally.Google Scholar
  11. Dray, A., Mazerolle, L., Perez, P., & Ritter, A. (2008). Drug law enforcement in an agent-based model: simulating the disruption to street-level drug markets. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 352–271). Hershey, PA: IGI Global.Google Scholar
  12. Eck, J. E. (1995). Examining routine activity theory: A review of two books. Justice Quarterly, 12(4), 763–797.CrossRefGoogle Scholar
  13. Eck, J. E. (2002). Learning From experience in problem-oriented policing and situational prevention: The positive functions of weak evaluations and the negative functions of strong ones. In N. Tilley (Ed.), Evaluation in crime prevention. Crime prevention studies, volume 14 (pp. 93–118). Monsey, NY: Criminal Justice Press.Google Scholar
  14. Eck, J. E. (2006). When is a Bologna Sandwich better than sex? A defense of small-N case study evaluations. Journal of Experimental Criminology, 2(3), 345–362.CrossRefGoogle Scholar
  15. Eck, J. E., & Liu, L. (2008). Varieties of artificial crime analysis: Purpose, structure, and evidence in crime simulations. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 413–432). Hershey, PA: IGI Global.Google Scholar
  16. Eck, J. E., Clarke, R. V., & Guerette, R. T. (2007). Risky facilities: Crime concentration in homogeneous sets of establishments and facilities. In G. Farrell, K. J. Bowers, S. D. Johnson, & M. Townsley (Eds.), Imagination for crime prevention. Crime prevention studies, volume 19 (pp. 225–264). Monsey, NY: Criminal Justice Press.Google Scholar
  17. Ekblom, P. (1999). Can we make crime prevention adaptive by learning from other evolutionary struggles? Studies on Crime and Crime Prevention, 8(1), 27–51.Google Scholar
  18. Elffers, H., & Van Baal, P. (2008). Spatial Backcloth is not that important in simulation research: An illustration from simulating perceptual deterrence. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 19–34). Hershey, PA: IGI Global.Google Scholar
  19. Epstein, J. M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.Google Scholar
  20. Furtado, V., Melo, A., Coelho, A., Menezes, R., & Belch, M. (2008). Simulating crime against properties using swarm intelligence and social networks. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 300–318). Hershey, PA: IGI Global.Google Scholar
  21. George, A. L., & Bennett, A. (2005). Case studies and theory development in the social sciences. Cambridge, MA: MIT Press.Google Scholar
  22. Gilbert, N. (2008). Agent Based models. quantitative applications in the social sciences. No. 153. Thousand Oaks, CA: Sage.Google Scholar
  23. Groff, E. R. (2008). Characterizing the Spatio-Temporal aspects of routine activities and the geographic distribution of street robbery. In L. Liu, & J. E. Eck (Eds.), Artificial Crime Analysis Systems (pp. 226–251). Hershey, PA: IGI Global.Google Scholar
  24. Hedström, P. (2005). Dissecting the social: On the principles of analytical sociology. New York: Cambridge University Press.Google Scholar
  25. Liu, L., & Eck, J. E. (2008). Artificial crime analysis systems. Hershey, PA: IGI Global.CrossRefGoogle Scholar
  26. Liu, L., Wang, X., Eck, J., & Liang, J. (2005). Simulating crime events and crime patterns in a RA/CA model. In F. Wang (Ed.), Geographic information systems and crime analysis (pp. 198–213). Hershey, PA: IGI Global.Google Scholar
  27. Lum, C., & Yang, S.-M. (2005). Why do evaluation researchers in crime and justice choose non-experimental methods? Journal of Experimental Criminology, 1(2), 191–213.CrossRefGoogle Scholar
  28. Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ: Princeton University Press.Google Scholar
  29. National Research Council. (2005). Improving evaluation of anticrime programs. Washington, DC: National Academies Press.Google Scholar
  30. Popper, K. (2002). Conjectures and refutations: The growth of scientific knowledge. London: Routledge.Google Scholar
  31. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimentation and quasi-experimental designs for general causal inference. New York: Houghton Mifflin.Google Scholar
  32. Sherman, L. W., & Rogan, D. P. (1995). Effects of gun seizures on gun violence: ‘Hot Spots’ patrol in Kansas City. Justice Quarterly, 12(4), 673–693.CrossRefGoogle Scholar
  33. Sherman, L. W., & Weisburd, D. A. (1995). General deterrent effects of police patrol in crime “Hot Spots”: A randomized, controlled trial. Justice Quarterly, 12(4), 625–648.CrossRefGoogle Scholar
  34. Spelman, W., & Brown, D. K. (1981). Calling the Police: A replication of the citizen reporting component of the Kansas City response time analysis. Washington, DC: Police Executive Research Forum.Google Scholar
  35. Szakas, J., Trefftz, C., Ramirez, J. R., & Jefferis, E. (2008). Development of an intelligent patrol routing system using GIS and computer simulations. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 339–351). Hershey, PA: IGI Global.Google Scholar
  36. Townsley, M., & Johnson, S. D. (2008). The need for systematic replication and tests of validity in simulation. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 1–19). Hershey, PA: IGI Global.Google Scholar
  37. van Baal, P. (2004). Computer simulations of criminal deterrence. Hoofddorp, Netherlands: Boom Juridische Uitgevers.Google Scholar
  38. van Dijk, J. J. M. (2001). Attitudes of victims and repeat victims toward the police: Results of the international crime victims survey. In G. Farrell, & K. Pease (Eds.), Repeat victimization. Crime prevention studies, volume 12 (pp. 27–52). Monsey, NY: Criminal Justice Press.Google Scholar
  39. Wang, X. (2005). Spatial Adaptive Crime Event Simulation using the RA/CA/ABM Computational Laboratory. Unpublished Doctoral Dissertation, Department of Geography, University of Cincinnati. Cincinnati, Ohio.Google Scholar
  40. Wang, X., Liu, L., & Eck, J. E. (2008). Crime simulation using GIS and artificial intelligent agents. In L. Liu, & J. E. Eck (Eds.), Artificial crime analysis systems (pp. 209–225). Hershey, PA: IGI Global.Google Scholar
  41. Weisburd, D., & Eck, J. E. (2004). What can police do to reduce crime, disorder and fear? The Annals of the American Academy of Political and Social Science, 593, 42–65.CrossRefGoogle Scholar
  42. Weisburd, D., & Piquero, A. R. (2008). How well do criminologists explain crime? In M. Tonry (Ed.), Crime and justice: An annual review of research. Volume 37. Chicago, IL: University of Chicago Press.Google Scholar
  43. White, G. F. (1990). Neighborhood permeability and burglary rates. Justice Quarterly, 7(1), 57–67.CrossRefGoogle Scholar
  44. Zhang, L., Messner, S. F., & Liu, J. (2007). An exploration of the determinants of reporting crime to the police in the City of Tianjin, China. Criminology, 45(4), 959–984.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Division of Criminal JusticeUniversity of CincinnatiCincinnatiUSA
  2. 2.Department of GeographyUniversity of CincinnatiCincinnatiUSA

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