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Robust auction design under multiple priors by linear and integer programming
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DOI：10.1007/s10479-017-2416-4.png)
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Abstract
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It is commonly assumed in the optimal auction design literature that valuations of buyers are independently drawn from a unique distribution. In this paper we study auctions under ambiguity, that is, in an environment where valuation distribution is uncertain itself, and present a linear programming approach to robust auction design problem with a discrete type space. We develop an algorithm that gives the optimal solution to the problem under certain assumptions when the seller is ambiguity averse with a finite prior set and the buyers are ambiguity neutral with a prior . We also consider the case where all parties, the buyers and the seller, are ambiguity averse, and formulate this problem as a mixed integer programming problem. Then, we propose a hybrid algorithm that enables to compute an optimal solution for the problem in reduced time.
Keywords:
Optimal auction design
Robustness
Multiple priors
Ambiguity
Linear programming
Mixed-integer programming
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