Using a multi-agent model to predict both physical and cyber criminal activity

Abstract

The paper describes a multi-agent methodology for the prediction of physical crime and cyber crime. The model uses clustering algorithms to determine the number of agents in the environment. The preference of each of these agents is determined by feature selection. The agents are allowed to interact in a synthetic environment. The results of the interactions are measured and the model is updated with new information. This modeling approach holds significant promise for the simulation of human criminal behavior.