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作者：Kothiyal, Amit; Spinu, Vitalie; Wakker, Peter P.
作者单位：Max Planck Society; University of California System; University of California Los Angeles; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
摘要：This paper provides necessary and sufficient preference conditions for average utility maximization over sequences of variable length. We obtain full generality by using a new algebraic technique that exploits the richness structure naturally provided by the variable length of the sequences. Thus we generalize many preceding results in the literature. For example, continuity in outcomes, a condition needed in other approaches, now is an option rather than a requirement. Applications to expecte...
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作者：Cownden, Daniel; Steinsaltz, David
作者单位：University of St Andrews; University of Oxford
摘要：In a novel multiplayer extension of the famous secretary problem, multiple players seek to employ secretaries from a common labour pool. Secretaries do not accept being put on hold, always accept job offers immediately, and leave the labour pool once rejected by a single player. All players have an identical preference for secretaries, and all players seek to optimize the probability of obtaining the best of all n secretaries. We find that in the Nash equilibrium, as the number, N, of players ...
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作者：Chen, Wei; Dawande, Milind; Janakiraman, Ganesh
作者单位：University of Texas System; University of Texas Dallas
摘要：We study fixed-dimensional stochastic dynamic programs in a discrete setting over a finite horizon. Under the primary assumption that the cost-to-go functions are discrete L-\tau-convex, we propose a pseudo-polynomial time approximation scheme that solves this problem to within an arbitrary prespecified additive error of epsilon > 0. The proposed approximation algorithm is a generalization of the explicit-enumeration algorithm and offers us full control in the trade-off between accuracy and ru...
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作者：Natarajan, Karthik; Shi, Dongjian; Toh, Kim-Chuan
作者单位：Singapore University of Technology & Design; National University of Singapore
摘要：In this paper, we propose a new probabilistic model for minimizing the anticipated regret in combinatorial optimization problems with distributional uncertainty in the objective coefficients. The interval uncertainty representation of data is supplemented with information on the marginal distributions. As a decision criterion, we minimize the worst-case conditional value at risk of regret. The proposed model includes the interval data minmax regret model as a special case. For the class of com...
