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Crime relies, directly or indirectly, upon an array of factors, ranging from the levels of concentration of wealth to the physical organization of the urban center under consideration. Modeling the highly interconnected nature of this social system has recently attracted attention in computer science. As experiments in this domain cannot be performed without high risks, because they result on loss of human lives, simulation models have been chosen as supporting tools for this process. Multiagent systems (MAS) primarily study the behavior of autonomous and organized groups of software agents with the purpose of providing solutions to complex problems that could not be achieved by each individual agent alone. Multiagent-based simulation systems have been successfully adopted because the inherent characteristics of the agents (e.g., autonomy, sociability, and pro-activity) facilitate the construction of more dynamic models, thus contrasting...
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Recommended Reading and References
Benenson I, Torrens PM (2004) Geosimulation: object-based modeling of urban phenomena. Comput Environ Urban Syst 28(1/2):1–8
Bonabeau E, Dorigo M, Heraulaz G (1999) Swarm intelligence: from natural to artificial systems. Santa Fe Institute Studies in the Sciences of Complexity Series. Oxford Press
Bosse T, Gerritsen C, Treur J (2007) Cognitive and social simulation of criminal behavior: the intermittent explosive disorder case. AAMAS 58:367–374
Brantingham P, Brantingham P (1979) Environment, routine, and situation: toward a pattern theory of crime. In: Clark R, Felson M (eds) Routine activity and rational choice, vol 5. Transaction books, pp 259–294
Calvez B, Hutzler G (2005) Automatic tuning of agent-based models using genetic algorithms. Fourth international joint conference on autonomous agents & multi agent system, Netherlands
Cohen L, Felson M (1979) Social change and crime rate trends: a routine approach. Am Sociol Rev 44:588–608
Dorigo M, Stützle T (2004) Ant colony optimization. The MIT Press, Cambridge
Elffers H, Van Baal P (2008) Realistic spatial backcloth is not that important in agent based simulation research: an illustration from simulating perceptual deterrence. In: Liu L, Eck J (eds) Artificial crime analysis systems: using computer simulations and geographic information systems, pp 19–34
Ferber J (1999) Multi-agent systems: an introduction to distributed artificial intelligence. Addison-Wesley
Furtado V, Melo A, Coelho AL, Menezes R (2008) Simulating crime against properties using swarm intelligence and social networks. In: Liu L, Eck J (eds) Artificial crime analysis systems: using computer simulations and geographic information systems, pp 300–318
Gilbert N, Conte R (1995) Artificial societies: the computer simulation of social life. UCL Press, London
Glaeser E, Sacerdote B, Scheinkman J (1996) Crime and social interactions. Q J Econ 111(2):507–548, MIT Press
Groff E (2008) Characterizing the spatio-temporal aspects of routine activities and the geographic distribution of street robbery. In: Liu L, Eck J (eds) Artificial crime analysis systems: using computer simulations and geographic information systems, pp 226–251
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press
Johnson S, Bernasco W, Bowers K, Elfers H, Ratcliffe J, Rengert G, Townsley M (2007) Space-time patterns of risk: across national assessment of residential burglary victimization. J Quant Criminol 32(3):201–219
Liang J, Liu L, Eck J (2001) Simulating crimes and crime patterns using cellular automata and GIS. UCGIS student award papers 2001. Retrieved from http://www.ucgis.org/f2oppor.html
Liu L, Wang X, Eck J, Liang J (2005) Simulating crime events and crime patterns in a RA/CA Model. In: Wang F (ed) GIS and crime analysis. Idea Group, pp 197–213
Pease K (1998) Repeat victimization: taking stock. Crime detection and prevention series paper 90. Home Office, London
Reis D, Melo A, Coelho ALV, Furtado V (2006) GAPatrol: an evolutionary multiagent approach for the automatic definition of hotspots and patrol routes. In: Sichman JS, Coelho H, Oliveira S (eds) Proceedings of IBERAMIA/SBIA 2006, Lecture Notes in Artificial Intelligence (LNAI) 4140, pp 118–127
Rocher G, Sherif P (1972) A general introduction to sociology: a theoretical perspective. MacMillan Canada
Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice Hall Series in AI, New Jersey
Scott JP (2000) Social network analysis: a handbook. Sage
Snook B (2004) Individual differences in distance travelled by serial burglars. J Invest Psychol Offender Profiling 1:53–66
Sutherland E (1974) Principles of criminology, 4th edn. J. B. Lippincott, Philadelphia
Wright R, Decker S (1994) Choosing the target. Burglars on the job: street life and residential break-ins. Northeastern University Press, Boston
Xue Y, Brown D (2006) Spatial analysis with preference specification of latent decision makers for criminal event prediction. Decis Support Syst 41(3):560–573
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Furtado, V. (2014). Simulation as a Tool for Police Planning. In: Bruinsma, G., Weisburd, D. (eds) Encyclopedia of Criminology and Criminal Justice. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5690-2_680
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DOI: https://doi.org/10.1007/978-1-4614-5690-2_680
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