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作者：Yu, Yimin; Kong, Xiangyin
作者单位：City University of Hong Kong; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：We consider incentive compensation where the firm has ambiguity on the effort-contingent output distribution: The parameters of the output probability distribution are in an ellipsoidal uncertainty set. The firm evaluates any contract by its worst-case performance over all possible parameters in the uncertainty set. Similarly, the incentive compatible condition for the agent must hold for all possible parameters in the uncertainty set. The firm is financially risk neutral and the agent has lim...
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作者：Chen, Ye; Ryzhov, Ilya O.
作者单位：Virginia Commonwealth University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
摘要：Approximate Bayesian inference is a powerful methodology for constructing computationally efficient statistical mechanisms for sequential learning from incomplete or censored information. Approximate Bayesian learning models have proven successful in a variety of operations research and business problems; however, prior work in this area has been primarily computational, and the consistency of approximate Bayesian estimators has been a largely open problem. We develop a new consistency theory ...
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作者：Manshadi, Vahideh; Misra, Sidhant; Rodilitz, Scott
作者单位：Yale University; United States Department of Energy (DOE); Los Alamos National Laboratory
摘要：Motivated by viral marketing on social networks, we study the diffusion process of a new product on a network where each agent is connected to a random subset of others. The number of contacts (i.e., degree) varies across agents, and the firm knows the degree of each agent. Further, the firm can seed a fraction of the population and incurs a fixed cost per contact. Under any bounded degree distribution and for any target adoption proportion, we compute both the cost and the time it takes to re...
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作者：Han, Weidong; Powell, Warren B.
作者单位：Princeton University
摘要：We consider an optimal learning problem where we are trying to learn a function that is nonlinear in unknown parameters in an online setting. We formulate the problem as a dynamic program, provide the optimality condition using Bellman's equation, and propose a multiperiod lookahead policy to overcome the nonconcavity in the value of information. We adopt a sampled belief model, which we refer to as a discrete prior. For an infinite-horizon problem with discounted cumulative rewards, we prove ...
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作者：Misic, Velibor V.
作者单位：University of California System; University of California Los Angeles
摘要：Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are controllable and are also decision variables. In this paper, we study the problem of tree ensemble optimi...
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作者：Roughgarden, Tim; Talgam-Cohen, Inbal; Yan, Qiqi
作者单位：Columbia University; Technion Israel Institute of Technology
摘要：Most results in revenue-maximizing mechanism design hinge on getting the price right-selling goods to bidders at prices low enough to encourage a sale but high enough to garner nontrivial revenue. This approach is difficult to implement when the seller has little or no a priori information about bidder valuations or when the setting is sufficiently complex, such as matching markets with heterogeneous goods. In this paper, we apply a robust approach to designing auctions for revenue. Instead of...
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作者：Gallego, Guillermo; Li, Michael Z. F.; Liu, Yan
作者单位：Hong Kong University of Science & Technology; Nanyang Technological University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：We consider a finite-horizon, finite-capacity dynamic pricing model where consumers may purchase multiple units of the same product. We present three models that differ in their complexity and revenue potential. The dynamic nonlinear pricing (DNP) model allows the seller to dynamically selecting a price for each bundle size. The dynamic linear pricing model restricts the seller to dynamically select a unit price for all bundle sizes. There can be a significant revenue gap between the two model...
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作者：Atamturk, Alper; Gomez, Andres
作者单位：University of California System; University of California Berkeley; University of Southern California
摘要：We describe strong convex valid inequalities for conic quadratic mixed 0-1 optimization. These inequalities can be utilized for solving numerous practical nonlinear discrete optimization problems from value-at-risk minimization to queueing system design, from robust interdiction to assortment optimization through appropriate conic quadratic mixed 0-1 relaxations. The inequalities exploit the submodularity of the binary restrictions and are based on the polymatroid inequalities over binaries fo...
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作者：Bansal, Saurabh; Gutierrez, Genaro J.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Texas System; University of Texas Austin
摘要：In this paper, we develop a new characterization of multiple-point forecasts provided by experts and use it in an optimization framework to deduce actionable signals, including the mean, standard deviation, or a combination of the two for underlying probability distributions. This framework consists of three steps: (1) calibrate experts' point forecasts using historical data to determine which quantile they provide, on average, when asked for forecasts, (2) quantify the precision in the expert...
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作者：Anstreicher, Kurt M.
作者单位：University of Iowa
摘要：We consider a new approach for the maximum-entropy sampling problem (MESP) that is based on bounds obtained by maximizing a function of the form ldet M(x) over linear constraints, where M(x) is linear in the n-vector x. These bounds can be computed very efficiently and are superior to all previously known bounds for MESP on most benchmark test problems. A branch-and-bound algorithm using the new bounds solves challenging instances of MESP to optimality for the first time.
