Deter and protect: crime modeling with multi-agent learning

Authored by Trevor R Caskey, James S Wasek, Anna Y Franz

Date Published: 2018

DOI: 10.1007/s40747-017-0062-8

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

This paper presents a formal game-theoretic belief learning approach to model criminology's routine activity theory (RAT). RAT states that for a crime to occur a motivated offender (criminal) and a desirable target (victim) must meet in space and time without the presence of capable guardianship (law enforcement). The novelty in using belief learning to model the dynamics of RAT's offender, target, and guardian behaviors within an agent-based model is that the agents learn and adapt given observation of other agents' actions without knowledge of the payoffs that drove the other agents' choices. This is in contrast to other crime modeling research that has used reinforcement learning where the accumulated rewards gained from prior experiences are used to guide agent learning. This is an important distinction given the dynamics of RAT. It is the presence of the various agent types that provide opportunity for crime to occur, and not the potential for reward. Additionally, the belief learning approach presented fits the observed empirical data of case studies, producing statistically significant results with lower variance when compared to a reinforcement learning approach. Application of this new approach supports law enforcement in developing responses to crime problems and planning for the effects of displacement due to directed responses, thus deterring offenders and protecting the public through crime modeling with multi-agent learning.

Tags

game theory Simulation Agent-based modeling Displacement crime modeling Hot-spots Belief learning Foot patrol