2024

最新文章

• Persuading Risk-Conscious Agents: A Geometric Approach AI翻译
  作者：Anunrojwong, Jerry; Iyer, Krishnamurthy; Lingenbrink, David
  作者单位：Columbia University; University of Minnesota System; University of Minnesota Twin Cities; Cornell University
  摘要：We consider a persuasion problem between a sender and a receiver where utility may be nonlinear in the latter's belief; we call such receivers risk conscious. Such utility models arise when the receiver exhibits systematic biases away from expected utility maximization, such as uncertainty aversion (e.g., from sensitivity to the variance of the waiting time for a service). Because of this nonlinearity, the standard approach to finding the optimal persuasion mechanism using revelation principle...
• Data-Driven Surgical Tray Optimization to Improve Operating Room Efficiency AI翻译
  作者：Deshpande, Vinayak; Mundru, Nishanth; Rath, Sandeep; Knowles, Martyn; Rowe, David; Wood, Benjamin C.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Surgical procedures account for over 60% of the operating cost of a hospital. About 15% of these costs are related to surgical instruments and supplies. Hospitals spend several million dollars annually on instrument sterilization, instrument tray assembly, and instrument repurchase costs. However, in a large majority of hospitals, less than 20%-30% of reusable instruments supplied to a surgery are used on average. Prior implementations of surgical tray rationalizations have typically been expe...
• Uncertainty Quantification and Exploration for Reinforcement Learning AI翻译
  作者：Zhu, Yi; Dong, Jing; Lam, Henry
  作者单位：Northwestern University; Columbia University; Columbia University
  摘要：We investigate statistical uncertainty quantification for reinforcement learning (RL) and its implications in exploration policy. Despite ever-growing literature on RL applications, fundamental questions about inference and error quantification, such as large-sample behaviors, appear to remain quite open. In this paper, we fill in the literature gap by studying the central limit theorem behaviors of estimated Q-values and value functions under various RL settings. In particular, we explicitly ...
• Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation AI翻译
  作者：Aziz, Haris; Freeman, Rupert; Shah, Nisarg; Vaish, Rohit
  作者单位：University of New South Wales Sydney; University of Virginia; University of Toronto; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi
  摘要：We study the problem of allocating indivisible goods among agents with additive valuations. When randomization is allowed, it is possible to achieve compelling notions of fairness such as envy-freeness, which states that no agent should prefer any other agent's allocation to their own. When allocations must be deterministic, achieving exact fairness is impossible but approximate notions such as envy-freeness up to one good can be guaranteed. Our goal in this work is to achieve both simultaneou...
• Risk-Averse Regret Minimization in Multistage Stochastic Programs AI翻译
  作者：Poursoltani, Mehran; Delage, Erick; Georghiou, Angelos
  作者单位：Universite de Montreal; HEC Montreal; Universite de Montreal; HEC Montreal; University of Cyprus
  摘要：Within the context of optimization under uncertainty, a well-known alternative to minimizing expected value or the worst-case scenario consists in minimizing regret. In a multistage stochastic programming setting with a discrete probability distribution, we explore the idea of risk-averse regret minimization, where the benchmark policy can only benefit from foreseeing increment steps into the future. The increment -regret model naturally interpolates between the popular ex ante and ex post reg...
• New Algorithms for Hierarchical Optimization in Kidney Exchange Programs AI翻译
  作者：Delorme, Maxence; Garcia, Sergio; Gondzio, Jacek; Kalcsics, Jörg; Manlove, David; Pettersson, William
  作者单位：Tilburg University; University of Edinburgh; University of Glasgow
  摘要：Many kidney exchange programs (KEPs) use integer linear programming (ILP) based on a hierarchical set of objectives to determine optimal sets of transplants. We propose innovative techniques to remove barriers in existing mathematical models, vastly reducing solution times and allowing significant increases in potential KEP pool sizes. Our techniques include two methods to avoid unnecessary variables, and a diving algorithm that reduces the need to solve multiple complex ILP models while still...
• Unified Moment-Based Modeling of Integrated Stochastic Processes AI翻译
  作者：Kyriakou, Ioannis; Brignone, Riccardo; Fusai, Gianluca
  作者单位：City St Georges, University of London; University of Freiburg; University of Eastern Piedmont Amedeo Avogadro; City St Georges, University of London
  摘要：In this paper, we present a new method for simulating integrals of stochastic processes. We focus on the nontrivial case of time integrals, conditional on the state variable levels at the endpoints of a time interval through a moment-based probability distribution construction. We present different classes of models with important uses in finance, medicine, epidemiology, climatology, bioeconomics, and physics. The method is generally applicable in well-posed moment problem settings. We study i...
• Statistical Inference for Aggregation of Malmquist Productivity Indices AI翻译
  作者：Pham, Manh; Simar, Leopold; Zelenyukc, Valentin
  作者单位：University of Queensland; Universite Catholique Louvain; University of Queensland; University of Queensland
  摘要：The Malmquist productivity index (MPI) has gained popularity among studies on the dynamic change of productivity of decision-making units (DMUs). In practice, this index is frequently reported at aggregate levels (e.g., public and private firms) in the form of simple, equally weighted arithmetic or geometric means of individual MPIs. A number of studies emphasize that it is necessary to account for the relative importance of individual DMUs in the aggregations of indices in general and of the ...
• Robust Assortment Optimization Under the Markov Chain Choice Model AI翻译
  作者：Desir, Antoine; Goyal, Vineet; Jiang, Bo; Xie, Tian; Zhang, Jiawei
  作者单位：INSEAD Business School; Columbia University; Shanghai University of Finance & Economics; New York University
  摘要：Assortment optimization arises widely in many practical applications, such as retailing and online advertising. In this problem, the goal is to select a subset from a universe of substitutable products to offer customers in order to maximize the expected revenue. We study a robust assortment optimization problem under the Markov chain choice model. In this formulation, the parameters of the choice model are assumed to be uncertain, and the goal is to maximize the worst case expected revenue ov...
• Two-Stage Stochastic Matching and Pricing with Applications to Ride Hailing AI翻译
  作者：Feng, Yiding; Niazadeh, Rad; Saberi, Amin
  作者单位：Microsoft; University of Chicago; Stanford University
  摘要：Matching and pricing are two critical levers in two-sided marketplaces to connect demand and supply. The platform can produce more efficient matching and pricing decisions by batching the demand requests. We initiate the study of the two-stage stochastic matching problem, with or without pricing, to enable the platform to make improved decisions in a batch with an eye toward the imminent future demand requests. This problem is motivated in part by applications in online marketplaces, such as r...