In recent years, the field of social simulation has been dominated by the individual, or agent-based, computational model (ABM). ABMs provide unique means to explore complex social systems by allowing researchers to construct explicit models of the individual actors and interactions that make them up - people, peer groups, companies, nations, trade, reproduction, victimization, and so on, This chapter aims to provide the reader with a primer in the social simulation method and in particular the application of ABM in the field of environmental criminology. It begins by discussing the rationale behind the ABM approach. Subsequently, drawing on two illustrative simulations, it summarizes fundamental processes involved in designing, constructing, verifying, calibrating, validating, and utilizing ABM. It concludes by discussing some of the overarching strengths and limitations of the approach, and by discussing several areas of research that might aid in furthering the use of ABM within the field of environmental criminology.
Studying Situational Effects of Setting Characteristics: Research Examples from the Study of Peers, Activities, and Neighborhoods
Frank M. Weerman, Evelien Hoeben, Wim Bernasco, Lieven J. R. Pauwels, and Gerben J.N. Bruinsma
This chapter addresses methods to study situational influences of setting characteristics on adolescent offending. In particular, it describes data collection methods (space-time budget interviews, census data, community surveys, and systematic social observations) that enable precise measurement of what respondents do, with whom they undertake these activities, and in what kind of places (both the geographical area and the function of the location) they find themselves. Such data capture presence in and exposure to different kinds of settings during particular periods in time. This chapter illustrates the usefulness of these method for criminological research by summarizing the results of three sub-studies from the Study of Peers, Activities, and Neighborhoods (SPAN) conducted in the Netherlands. It first discusses the design of the SPAN data collection and the instruments that were used in it. It then reviews each study in turn by summarizing its theoretical motivation, data structure, and analytical strategy, and by describing the main findings it has generated.