腾讯会议：https://meeting.tencent.com/s/gM4XIdhQ0pGo；会议 ID：882 987 976
孔祥印博士目前就职于中国科学技术大学管理学院。孔祥印于2020年获得香港城市大学与西安交通大学管理科学博士学位。其研究兴趣主要为契约理论、供应链管理、营销与运营交叉研究，其研究成果发表在运营领域国际顶级期刊Operations Research （UTD24）, Operations Research Letters。
We consider incentive compensation where the firm has ambiguity on the effort-contingent output distribution: the parameters of the output probability distribution are in an ellipsoidal uncertainty set. The firm evaluates any contract by its worst-case performance over all possible parameters in the uncertainty set. Similarly, the incentive compatible condition for the agent must hold for all possible parameters in the uncertainty set. The firm is financially risk neutral and the agent has limited liability. We find that when the agent is financially risk neutral, the optimal robust contract is a linear contract--paying the agent a base payment and a fixed share of the output. Moreover, the linear contract is the only type of contracts that are robust to the parameter uncertainty. When there is model uncertainty over a general effort-contingent output distribution, we show that a generalized linear contract is uniquely optimal. When the agent is risk-averse and has a piecewise linear utility, the only optimal contract is a piecewise linear contract that consists of progressive fixed payments and linear rewards with progressive commission rates. We also provide the analysis for the trade-off between robustness and worst-case performance and show that our results are robust to a variety of settings, including cases with general
-norm uncertainty sets, multiple effort levels, etc. Our paper provides a new explanation for the popularity of linear contracts and piecewise linear contracts in practice and introduces a flexible modeling approach for robust contract designs with model uncertainty.