Agent based simulation of routine activity with social learning behavior

Abstract

Routine activity theory and social learning theory are two most important aspects of criminal psychology. Routine activity theory states that criminal acts are caused due to the presence of criminals, victims and the absence of guardians in time and place. As the number of collision of these elements in place and time increases, criminal acts will also increase even if the number of criminals or civilians remains the same within the vicinity of a city. Street robbery is a typical example of routine activity theory and the occurrence of which can be predicted using routine activity theory. Agent-based models allow simulation of diversity among individuals. Therefore agent based simulation of street robbery can be used to visualize how chronological occurrences of human activity influence the incidence of street robbery. The conceptual model identifies three classes of people-criminals, civilians and police with certain activity areas for each. Police exist only as agents of formal guardianship. Criminals with a tendency for crime will be in the search for their victims. Civilians without criminal tendency can be either victims or guardians. These agents are subjected to random walk through a street environment guided by a Q-learning module and the possible outcomes are analyzed. Through the time period some civilian agents with a tendency to commit crime and have criminal background or criminal companions may turn into criminals. This phenomenon reflects social learning theory. The results compare gang and single robberies, robberies made on kids and women, tests on criminal social learning and comparison of different guardian patrolling strategies.