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Crime Modeling with Multi-Agent Lear...

Caskey, Trevor Ryan.

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Crime Modeling with Multi-Agent Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Crime Modeling with Multi-Agent Learning./
Author:	Caskey, Trevor Ryan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	96 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10682408
ISBN:	9780355564167

Crime Modeling with Multi-Agent Learning.
Caskey, Trevor Ryan.

Crime Modeling with Multi-Agent Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 96 p.

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2018.

This dissertation 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.

ISBN: 9780355564167Subjects--Topical Terms:

586835
Engineering.

Crime Modeling with Multi-Agent Learning.
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Advisers: James S. Wasek; Anna Y. Franz.
502 $a Thesis (Ph.D.)--The George Washington University, 2018.
520 $a This dissertation 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.
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