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          【明理講堂2025年第14期】6-10美國(guó)喬治華盛頓大學(xué)(George Washington University)何龍副教授: Proactive Policing: Resource Allocation for Crime Prevention with Deterrence Effect

          時(shí)間:June 10, 2025, 15:00-17:00       

          地點(diǎn):中關(guān)村主樓216

          報(bào)告人:何龍

          報(bào)告人國(guó)籍:中國(guó)

          報(bào)告人政治面貌:群眾

          報(bào)告人職稱(chēng):Associate Professor

          報(bào)告人工作單位:George Washington University

          報(bào)告人簡(jiǎn)介:

          Long He is an associate professor of decision sciences at the School of Business, George Washington University. Prior to joining GW, Long was an associate professor in the Department of Analytics & Operations at NUS Business School, National University of Singapore. He received his Ph.D. in Operations Research from the University of California, Berkeley, and his B.Eng. in Logistics Management and Engineering from HKUST. His current research involves using data-driven approaches to address problems in smart city operations (e.g., vehicle sharing, last-mile delivery) and supply chain management. This line of research has been recognized with the M&SOM Journal Best Paper Award, Transportation Science & Logistics (TSL) Best Paper Award, and ENRE Best Publication Award in Energy from INFORMS.

          報(bào)告內(nèi)容簡(jiǎn)介:

          This paper addresses police resource allocation across multiple locations, aiming to minimize the overall cost of potential crimes. Unlike previous literature focused on reactive police tasks, we propose a proactive approach that emphasizes crime prevention through deterrence. To account for the deterrence effect of police resources on crime, we employ the multinomial logit model to calibrate the distribution of crime locations. Our model sheds light on two facets of the deterrence effect in proactive policing—crime control diffusion and crime displacement—relevant to modern crime patterns from both criminology and economics perspectives. We also investigate the structural properties of our problem and its relation to mixture-of-logits assortment optimization. Additionally, we provide reformulations for mixed-integer linear/conic programs that can be solved directly using conventional optimization software. Finally, we showcase the efficacy of our model through a data-driven case study on the allocation of surveillance cameras in New York City..

          (承辦:管理工程系、科研與學(xué)術(shù)交流中心)

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