Operations Research

最新文章

• Conditional Distributionally Robust Functionals AI翻译
  作者：Shapiro, Alexander; Pichler, Alois
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Many decisions, in particular decisions in a managerial context, are subject to uncertainty. Risk measures cope with uncertainty by involving more than one candidate probability. The corresponding risk averse decision takes all potential candidate probabilities into account and is robust with respect to all potential probabilities. This paper considers conditional robust decision making, where decisions are subject to additional prior knowledge or information. The literature discusses various ...
• Latent Agents in Networks: Estimation and Targeting AI翻译
  作者：Ata, Baris; Belloni, Alexandre; Candogan, Ozan
  作者单位：University of Chicago; Duke University
  摘要：We consider a platform that serves (observable) agents, who belong to a larger network that also includes additional agents who are not served by the platform. We refer to the latter group of agents as latent agents. Associated with each agent are the agent's covariate and outcome. The platform has access to past covariates and outcomes of the observable agents, but no data for the latent agents is available to the platform. Crucially, the agents influence each other's outcome through a certai...
• Stochastic Approximation of Symmetric Nash Equilibria in Queueing Games AI翻译
  作者：Ravner, Liron; Snitkovsky, Ran I.
  作者单位：University of Haifa; Tel Aviv University
  摘要：We suggest a novel stochastic-approximation algorithm to compute a symmetric Nash-equilibrium strategy in a general queueing game with a finite action space. The algorithm involves a single simulation of the queueing process with dynamic updating of the strategy at regeneration times. Under mild assumptions on the utility function and on the regenerative structure of the queueing process, the algorithm converges to a symmetric equilibrium strategy almost surely. This yields a powerful tool tha...
• Dynamic Pricing with Unknown Nonparametric Demand and Limited Price Changes AI翻译
  作者：Perakis, Georgia; Singhvi, Divya
  作者单位：Massachusetts Institute of Technology (MIT); New York University
  摘要：We consider the dynamic pricing problem of a retailer who does not have any information on the underlying demand for a product. The retailer aims to maximize cumulative revenue collected over a finite time horizon by balancing two objectives: learning demand and maximizing revenue. The retailer also seeks to reduce the amount of price experimentation because of the potential costs associated with price changes. Existing literature solves this problem in the case where the unknown demand is par...
• Testing, Voluntary Social Distancing, and the Spread of an Infection AI翻译
  作者：Acemoglu, Daron; Makhdoumi, Ali; Malekian, Azarakhsh; Ozdaglar, Asuman
  作者单位：Massachusetts Institute of Technology (MIT); Duke University; University of Toronto; Massachusetts Institute of Technology (MIT)
  摘要：We study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines a social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. However, greater testing also reduces voluntary social distancing or increases social activity, exacerbating the spread of the virus. We show that the effect of testing on infections is nonmonotone. This no...
• Data Tracking Under Competition AI翻译
  作者：Bimpikis, Kostas; Morgenstern, Ilan; Saban, Daniela
  作者单位：Stanford University
  摘要：We explore the welfare implications of data-tracking technologies that enable firms to collect consumer data and use it for price discrimination. The model we develop centers around two features: competition between firms and consumers' level of sophistication. Our baseline environment features a firm that can collect information about the consumers it transacts with in a duopoly market, which it can then use in a second, monopoly market. We characterize and compare the equilibrium outcomes in...
• Assortment Optimization Under the Multi-Purchase Multinomial Logit Choice Model AI翻译
  作者：Bai, Yicheng; Feldman, Jacob; Segev, Danny; Topaloglu, Huseyin; Wagner, Laura
  作者单位：Washington University (WUSTL); Tel Aviv University; Universidade Catolica Portuguesa
  摘要：In this paper, we introduce the Multi-Purchase Multinomial Logit choice model, which extends the random utility maximization framework of the classical Multinomial Logit model to a multiple-purchase setting. In this model, customers sample random utilities for each offered product as in the Multinomial Logit model. However, rather than focusing on a single product, they concurrently sample a budget parameter M , which indicates the maximum number of products that the customer is willing to pur...
• Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation AI翻译
  作者：He, Shengyi; Jiang, Guangxin; Lam, Henry; Fu, Michael C.
  作者单位：Columbia University; Harbin Institute of Technology; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
  摘要：In solving simulation-based stochastic root-finding or optimization problems that involve rare events, such as in extreme quantile estimation, running crude Monte Carlo can be prohibitively inefficient. To address this issue, importance sampling can be employed to drive down the sampling error to a desirable level. However, selecting a good importance sampler requires knowledge of the solution to the problem at hand, which is the goal to begin with and thus forms a circular challenge. We inves...
• Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees AI翻译
  作者：Li, Yongchun; Xie, Weijun
  摘要：This paper studies a classic maximum entropy sampling problem (MESP), which aims to select the most informative principal submatrix of a prespecified size from a covariance matrix. By investigating its Lagrangian dual and primal characterization, we derive a novel convex integer program for MESP and show that its continuous relaxation yields a near-optimal solution. The results motivate us to develop a sampling algorithm and derive its approximation bound for MESP, which improves the best know...
• Is Separately Modeling Subpopulations Beneficial for Sequential Decision-Making? AI翻译
  作者：Lee, Ilbin
  作者单位：University of Alberta
  摘要：In recent applications of Markov decision processes (MDPs), it is common to estimate transition probabilities and rewards from transition data. In healthcare and some other applications, transition data are collected from a population of different entities, such as patients. Thus, one faces a modeling question of whether to estimate different models for subpopulations (e.g., divided by smoking status). For instance, there may be a subpopulation whose disease status progresses faster than other...