{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/11364"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/11364","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"The Economics of Crime and Punishment: A Computational Approach","abstract":"Emergent characteristics of crime rates and law enforcement are observed empiricallyin cities. For example, spatial clustering of crimes is observed. It is difficult to explain this clustering with Becker's crime model using representative agents. We extend Becker's model using an agent-based approach to explain this phenomenon. First, we develop a grid model of a city with agents located in housing. We allow those agents to decide whether to burgle a house in their spatial location. A government agent allocates resources to fines or law enforcement. We show how these agents' interactions lead to endogenous criminals and clustering of high-crime areas. We then demonstrate that this model also produces results found in empirical literature and how this method could be used to evaluate policy in real-world cities with relaxed assumptions about human decision-making.","abstract_html":"Emergent characteristics of crime rates and law enforcement are observed empiricallyin cities. For example, spatial clustering of crimes is observed. It is difficult to explain this clustering with Becker's crime model using representative agents. We extend Becker's model using an agent-based approach to explain this phenomenon. First, we develop a grid model of a city with agents located in housing. We allow those agents to decide whether to burgle a house in their spatial location. A government agent allocates resources to fines or law enforcement. We show how these agents' interactions lead to endogenous criminals and clustering of high-crime areas. We then demonstrate that this model also produces results found in empirical literature and how this method could be used to evaluate policy in real-world cities with relaxed assumptions about human decision-making.","abstract_has_math":false,"creators":["Appert, John"],"institution":null,"degree_name":null,"degree_level":"Doctorate Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Pingle, Mark"],"committee_chairs":[],"committee_members":["Nichols, Mark","Fossen, Frank","Taylor, Michael","Sarantsev, Andrey"],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:46:19Z","subjects":["Agent-Based","Complexity","Crime","Simulation"],"languages":["en_US","English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarwolf.unr.edu/handle/11714/11364","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Pingle, Mark"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Nichols, Mark","Fossen, Frank","Taylor, Michael","Sarantsev, Andrey"]},{"key":"dc:creator","label":"Author","values":["Appert, John"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-02T18:25:58Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-02T18:25:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctorate Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agent-Based","Complexity","Crime","Simulation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarwolf.unr.edu/handle/11714/11364"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Emergent characteristics of crime rates and law enforcement are observed empiricallyin cities. For example, spatial clustering of crimes is observed. It is difficult to explain this clustering with Becker's crime model using representative agents. We extend Becker's model using an agent-based approach to explain this phenomenon. First, we develop a grid model of a city with agents located in housing. We allow those agents to decide whether to burgle a house in their spatial location. A government agent allocates resources to fines or law enforcement. We show how these agents' interactions lead to endogenous criminals and clustering of high-crime areas. We then demonstrate that this model also produces results found in empirical literature and how this method could be used to evaluate policy in real-world cities with relaxed assumptions about human decision-making."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["The Economics of Crime and Punishment: A Computational Approach"]}]}],"canonical_facts":{"dc:contributor.advisor":["Pingle, Mark"],"dc:contributor.committeemember":["Nichols, Mark","Fossen, Frank","Taylor, Michael","Sarantsev, Andrey"],"dc:creator":["Appert, John"],"dc:date.accessioned":["2025-07-02T18:25:58Z"],"dc:date.available":["2025-07-02T18:25:58Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Emergent characteristics of crime rates and law enforcement are observed empiricallyin cities. For example, spatial clustering of crimes is observed. It is difficult to explain this clustering with Becker's crime model using representative agents. We extend Becker's model using an agent-based approach to explain this phenomenon. First, we develop a grid model of a city with agents located in housing. We allow those agents to decide whether to burgle a house in their spatial location. A government agent allocates resources to fines or law enforcement. We show how these agents' interactions lead to endogenous criminals and clustering of high-crime areas. We then demonstrate that this model also produces results found in empirical literature and how this method could be used to evaluate policy in real-world cities with relaxed assumptions about human decision-making."],"dc:format":["PDF"],"dc:identifier.uri":["https://scholarwolf.unr.edu/handle/11714/11364"],"dc:language":["English"],"dc:language.iso":["en_US"],"dc:subject":["Agent-Based","Complexity","Crime","Simulation"],"dc:title":["The Economics of Crime and Punishment: A Computational Approach"],"dc:type":["Dissertation"],"thesis:degree_level":["Doctorate Degree"]},"updated_at":"2026-07-27T21:46:19Z"}