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作者：Paccagnan, Dario; Gairing, Martin
作者单位：Imperial College London; University of Liverpool
摘要：In this work, we address the problem of minimizing social cost in atomic congestion games. For this problem, we present lower bounds on the approximation ratio achievable in polynomial time and demonstrate that efficiently computable taxes result in polynomial time algorithms matching such bounds. Perhaps surprisingly, these results show that indirect interventions, in the form of efficiently computed taxation mechanisms, yield the same performance achievable by the best polynomial time algori...
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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 ...
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作者：Feinstein, Zachary; Rudloff, Birgit
作者单位：Stevens Institute of Technology; Vienna University of Economics & Business
摘要：Nash equilibria and Pareto optimality are two distinct concepts when dealing with multiple criteria. It is well known that the two concepts do not coincide. However, this work, we show that it is possible to characterize the set of all Nash equilibria for any noncooperative game as the Pareto-optimal solutions of a certain vector optimization problem. To accomplish this task, we increase the dimensionality of the objective function and formulate a nonconvex ordering cone under which Nash equil...
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作者：Li, Haidong; Lam, Henry; Peng, Yijie
作者单位：Peking University; Columbia University; Peking University
摘要：We consider a simulation optimization problem for context-dependent decision making. Under a Gaussian mixture model-based Bayesian framework, we develop a dynamic sampling policy to maximize the worst-case probability of correctly selecting the best design over all contexts, which utilizes both global clustering information and local performance information. In particular, we design a computationally efficient approximation method to learn these sources of information, thereby leading to an im...
