New Game-Theoretic Frameworks and Algorithms Defending Against Opportunistic Criminals

• In Event: Game Theory, Gangs and Crime

Abstract

In urban transportation networks, crime diffuses as criminals travel through the networks and look for illicit opportunities. It is important to first model this diffusion in order to recommend actions or patrol policies to control the diffusion of such crime. Previously, game theory has been used for such patrol policy recommendations, but these applications of game theory for security have not modeled the diffusion of crime that comes about due to criminals seeking opportunities; instead the focus has been on highly strategic adversaries that plan attacks in advance. To overcome this limitation of previous work, this paper provides the following key contributions. First, we provide a model of crime diffusion based on a quantal biased random movement (QBRM) of criminals opportunistically and repeatedly seeking targets. Within this model, criminals react to real-time information, rather than strategically planning their attack in advance. Second, we provide a game-theoretic approach to generate randomized patrol policies for controlling such diffusion.

Authors

• Chao Zhang, University of Southern California

• Albert X. Jiang, University of Southern California

• Martin B. Short, Georgia Tech

• P. Jeffrey Brantingham, University of California, Los Angeles

• Milind Tambe, University of Southern California