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A robust optimization approach to mechanism desig

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  • Li, Jiangtao

    (Singapore Management University)

  • Wang, Kexin

    (Singapore Management University)

Abstract

We study the design of mechanisms when the mechanism designer faces local uncertainty about agents’ beliefs. Specifically, we consider a designer who does not know the exact beliefs of the agents but is confident that her estimate is within of the beliefs held by the agents (where reflects the degree of local uncertainty). Adopting the robust optimization approach, we design mechanisms that incentivize agents to truthfully report their payoff-relevant information regardless of their actual beliefs. For any fixed, we identify necessary and sufficient conditions under which requiring this sense of robustness is without loss of revenue for the designer. By analyzing the limiting case in which approaches 0, we provide two rationales for the widely studied Bayesian mechanism design framework.

Suggested Citation

  • Li, Jiangtao & Wang, Kexin, 2024. "A robust optimization approach to mechanism desig," Economics and Statistics Working Papers 8-2024, Singapore Management University, School of Economics.
    • Handle: RePEc:ris:smuesw:2024_008
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