In this paper, we present a novel approach to computational modeling of social systems. By combining the abstract state machine (ASM) formalism with the multiagent modeling paradigm, we obtain a formal semantic framework for modeling and integration of established theories of crime analysis and prediction. We focus here on spatial and temporal aspects of crime in urban areas. Our work contributes to a new multidisciplinary research effort broadly classified as Computational Criminology.

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Pattern and routine activities theories suggest that through a combination of decisions and innate understandings of environmental cues, likely offenders are able to separate good criminal opportunities from bad risks. The nature of this process is highly structured and allows researchers to anticipate likely concentrations for a variety of regular, daily activities, including criminal offences. This chapter sets out to model and test these theoretical principles. Mastermind represents an interdisciplinary research project in computational criminology jointly managed by ICURS and the Software Technology Lab at Simon Fraser University. Using the abstract state machine (ASM) formalism in combination with a multiagent based modeling paradigm, we devise a formal framework for semantic modeling and systematic integration of the theories for crime analysis and understanding crime patterns. We focus on crime in urban areas and model spatial and temporal aspects of crime potentially involving multiple offenders and multiple targets. Mastermind is used in a hypothetical analysis of motor vehicle theft.