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European journal of operational research. 2023 Oct 16;310(2):793-811. doi: 10.1016/j.ejor.2023.03.034 Q16.02024

Behavioral Analytics for Myopic Agents

近视眼代理的行为分析 翻译改进

Yonatan Mintz  1, Anil Aswani  2, Philip Kaminsky  2, Elena Flowers  3, Yoshimi Fukuoka  4

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作者单位

  • 1 Department of Industrial and Systems Engineering, University of Wisconsin - Madison, 53706.
  • 2 Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720.
  • 3 Department of Physiological Nursing, School of Nursing, University of California, San Francisco, CA 94143.
  • 4 Department of Physiological Nursing/Institute for Health and Aging, School of Nursing, University of California, San Francisco, CA 94143.
  • DOI: 10.1016/j.ejor.2023.03.034 PMID: 37554315

    摘要 Ai翻译

    Many multi-agent systems have a single coordinator providing incentives to a large number of agents. Two challenges faced by the coordinator are a finite budget from which to allocate incentives, and an initial lack of knowledge about the utility function of the agents. Here, we present a behavioral analytics approach for solving the coordinator's problem when the agents make decisions by maximizing utility functions that depend on prior system states, inputs, and other parameters that are initially unknown. Our behavioral analytics framework involves three steps: first, we develop a model that describes the decision-making process of an agent; second, we use data to estimate the model parameters for each agent and predict their future decisions; and third, we use these predictions to optimize a set of incentives that will be provided to each agent. The framework and approaches we propose in this paper can then adapt incentives as new information is collected. Furthermore, we prove that the incentives computed by this approach are asymptotically optimal with respect to a loss function that describes the coordinator's objective. We optimize incentives with a decomposition scheme, where each sub-problem solves the coordinator's problem for a single agent, and the master problem is a pure integer program. We conclude with a simulation study to evaluate the effectiveness of our approach for designing a personalized weight loss program. The results show that our approach maintains efficacy of the program while reducing its costs by up to 60%, while adaptive heuristics provide substantially less savings.

    Keywords: Control; Health care; Optimization; Statistical Inference.

    Keywords:behavioral analytics; myopic agents

    关键词:近视代理

    Copyright © European journal of operational research. 中文内容为AI机器翻译,仅供参考!

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    期刊名:European journal of operational research

    缩写:EUR J OPER RES

    ISSN:0377-2217

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    IF/分区:6.0/Q1

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