Publication Type
Working Paper
Version
publishedVersion
Publication Date
8-2024
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 ϵ of the beliefs held by the agents (where ϵ 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 ϵ, 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 ϵ approaches 0, we provide two rationales for the widely studied Bayesian mechanism design framework.
Keywords
mechanism design, local uncertainty, interim belief, robust optimization, duality approach
Discipline
Economic Theory
Research Areas
Economic Theory
First Page
1
Last Page
37
Citation
LI, Jiangtao and WANG, Kexin.
A robust optimization approach to mechanism design. (2024). 1-37.
Available at: https://ink.library.smu.edu.sg/soe_research/2765
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.