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Distributionally Robust Principal-Agent Problems and Optimality of Contracts

We propose a distributionally robust principal agent formulation, which generalizes some common variants of worst-case and Bayesian principal agent problems. We construct a theoretical framework to certify whether any surjective contract family is optimal, and bound its sub-optimality. We then apply the framework to study the optimality of affine contracts. We show with geometric intuition that these simple contract families are optimal when the surplus function is convex and there exists a technology type that is simultaneously least productive and least efficient. We also provide succinct expressions to quantify the optimality gap of any surplus function, based on its concave biconjugate. This new framework complements the current literature in two ways: invention of a new toolset; understanding affine contracts' performance in a larger landscape. Our results also shed light on the technical roots of this question: why are there more positive results in the recent literature that show simple contracts' optimality in robust settings rather than stochastic settings? This phenomenon is related to two technical facts: the sum of quasi-concave functions is not quasi-concave, and the maximization and expectation operators do not commute.

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