Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

5-2025

Abstract

This dissertation consists of three chapters that study the mechanism design problems when the designer does not completely know the economic environments. In the first chapter, we study the design of contracts when the principal has limited statistical information about the output distributions induced by the agent’s actions. In the baseline model, we consider a principal who only knows the mean of the output distribution for each action. The mean restrictions allow for a large set of profiles of output distributions, including some extreme output distributions that can be used to establish the robust optimality of increasing affine contracts. Motivated by this, we study the set of distributions that can be used to establish the robust optimality of increasing affine contracts. This facilitates the understanding for the use of increasing affine contracts in settings with more restrictions on the output distributions. Our main result shows that the optimality of increasing affine contracts persists even if the principal has access to other information about the output distributions, such as the information that the output distribution induced by each action has full support. In the second chapter, we study a robust version of monopoly pricing where the seller has limited information, knowing only the bounds on valuations and the mean of the buyer’s value distribution. The seller seeks to minimize the interim regret, defined as the loss in expected revenue due to the lack of precise knowledge about the buyer’s value distribution. The optimal pricing policy involves randomizing over a range of prices, with its support strictly bounded away from zero. Furthermore, we examine the welfare implications of this optimal pricing policy across all distributions deemed plausible by the seller, providing bounds on the buyer’s utility as a percentage of the total surplus and characterizing the frontier of the set of payoff pairs generated under all plausible distributions. The third chapter studies 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.

Degree Awarded

PhD in Economics

Discipline

Economic Theory

Supervisor(s)

LI, Jiangtao

First Page

1

Last Page

145

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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