Incentive-Compatible Assortment Optimization for Sponsored Products
研究了在线市场中赞助产品拍卖的设计问题，考虑消费者选择替代效应和卖家信息不对称，发现最优机制可能需要在顶部和底部扭曲有效分配，且卖家数量多时激励相容的社会成本高。
Online marketplaces, such as Amazon, Alibaba, Google Shopping, and JD.com, allow sellers to promote their products by charging them for the right to be displayed on top of organic search results. In this paper, we study the problem of designing auctions for sponsored products and highlight some new challenges emerging from the interplay of two unique features: substitution effects and information asymmetry. The presence of substitution effects, which we capture by assuming that consumers choose sellers according to a multinomial logit model, implies that the probability a seller is chosen depends on the assortment of sellers displayed alongside. Additionally, sellers may hold private information about how their own products match consumers’ interests, which the platform can elicit to make better assortment decisions. We first show that the first-best allocation, that is, the welfare-maximizing assortment in the absence of private information, cannot be implemented truthfully in general. Thus motivated, we initiate the study of incentive-compatible assortment optimization by characterizing prior-independent and prior-dependent mechanisms and quantifying the worst-case social cost of implementing truthful assortment mechanisms. An important finding is that the worst-case social cost of implementing truthful mechanisms can be high when the number of sellers is large. Structurally, we show that optimal mechanisms may need to downward distort the efficient allocation both at the top and the bottom. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4603 .