Abstract:
We consider the robust contract design problem when the principal only has limited information about the actions the agent can take. The principal evaluates a contract according to its worst-case performance caused by the uncertain action space. Carroll (AER 2015) showed that a linear contract is optimal among deterministic contracts. Recently, Kambhampati (JET 2023) showed that the principal’s payoff can be strictly increased via randomization over linear contracts. In this paper, we characterize the optimal randomized contract, which remains linear and admits a closed form of its cumulative distribution function. The advantage of randomized contracts over deterministic contracts can be arbitrarily large even when the principal knows only one non-trivial action of the agent. Furthermore, our result generalizes to the model of contracting with teams, by Dai and Toikka (Econometrica 2022).
About the speaker:
Zhihao Tang is an associate professor at ITCS, Shanghai University of Finance and Economics. He obtained his PhD degree in 2019 under the supervision of Dr. Hubert Chan at the University of Hong Kong. Before that, he received his BSc in Mathematics and BA in Economics from Peking University in 2014. He is broadly interested in theoretical computer science, particularly in algorithms under uncertainty. More specifically, he works on online algorithms and algorithmic game theory.