Research

Job Market Paper

Accuracy Discrimination in Personalized Product Recommendation

Abstract: I examine how a platform designs personalized product recommendations with discriminatory accuracy based on consumers’ preference intensities to maximize the probability of trade. The platform can increase recommendation accuracy for a consumer at the cost of the consumer’s privacy and higher endogenous prices set by third-party firms. When consumers’ types are observable, the platform’s optimal mechanism is perfectly misaligned with consumer preferences: it offers accurate recommendations only to “picky” consumers who benefit the least, thereby minimizing ex post consumer surplus. In contrast, when the platform must screen consumers, it behaves as if maximizing ex post consumer surplus by allowing consumers to self-select their most preferred accuracy levels. The results highlight the importance of requiring platforms to obtain consumers’ consent before providing personalized services, even when the platform’s interests are partly aligned with those of consumers.

Working Paper

Bargaining with Uncertainty about Beliefs in a Lemon Market

Earlier Publications

Bayesian truncation errors in equations of state of nuclear matter with chiral nucleon-nucleon potentials, joint with Jinniu Hu and Ying Zhang, Physics Letters B 798 (2019): 134982.

How New Rural Cooperative Medical System Affect Labor Supply of Middle-Aged and Elderly Population in Rural Areas: Based on Analysis of Dynamic Stochastic Model, joint with Zhaona, Finance and Economics(in Chinese)