Diffusion auction design

This paper studies an auction design problem for a seller to sell a single commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors, so that more buyers can participate in the auction and hence, the seller will be able to make a higher revenue and a higher welfare. We build a general framework for this new scenario and propose several novel diffusion auctions, which not only incentivize the buyers to report their valuations on the commodity truthfully, but also to propagate the auction information to all their neighbors. Particularly, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism on social networks can have the incentives, but it will decrease the seller's revenue or even lead to a deficit. We also show that in the social network setting all efficient mechanisms that are individually rational and incentive compatible can lead to a deficit. The goal in this article is to increase the seller's revenue by attracting more buyers, so we give up welfare maximization and propose a class of mechanisms called critical diffusion mechanisms. It is proved that both the seller's revenue and the social welfare achieved in critical diffusion mechanisms are not less than that given in the VCG mechanism before attracting new buyers. The intuition behind the proposed mechanisms is that buyers who join the mechanism earlier have higher priorities to buy the commodity. If a buyer does not win the commodity because of her propagation, then she will be compensated. The formalization of the problem has not been well-studied in the literature of mechanism design, and there are many open problems worth further investigation. The study of this problem will provide insights for the emerging market based on the participants' recommendations via their social networks. (C) 2021 Elsevier B.V. All rights reserved.