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作者：Kim, Youngsoo; Kwon, H. Dharma
作者单位：University of Alabama System; University of Alabama Tuscaloosa; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In the game of investment in the common good, the free rider problem can delay the stakeholders' actions in the form of a mixed strategy equilibrium. However, it has been recently shown that the commonly known form of mixed strategy equilibria of the stochastic war of attrition is destabilized by even the slightest degree of asymmetry between the players. Such extreme instability is contrary to the widely accepted notion that a mixed strategy equilibrium is the hallmark of the war of attrition...
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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...
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作者：Nannicini, Giacomo
作者单位：International Business Machines (IBM); IBM USA
摘要：We propose quantum subroutines for the simplex method that avoid classical computation of the basis inverse. We show how to quantize all steps of the simplex algorithm, including checking optimality, unboundedness, and identifying a pivot (i.e., pricing the columns and performing the ratio test) according to Dantzig's rule or the steepest edge rule. The quantized subroutines obtain a polynomial speedup in the dimension of the problem but have worse dependence on other numerical parameters. For...
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作者：Wang, Jie; Gao, Rui; Zha, Hongyuan
作者单位：The Chinese University of Hong Kong, Shenzhen; University of Texas System; University of Texas Austin; The Chinese University of Hong Kong, Shenzhen; Shenzhen Institute of Artificial Intelligence & Robotics for Society
摘要：In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy using logged trajectory data generated from different behavior policy, without execution of the target policy. Reinforcement learning in high-stake environments, such as healthcare and education, is often limited to off-policy settings due to safety or ethical concerns or inability of exploration. Hence, it is imperative to quantify the uncertainty of the off-policy estima...
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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...
