On Bayesian Epistemology of Myerson Auction - Deng, Xiaotie; Zhu, Keyu (Frontiers in Algorithmics, 2018)
Bayesian Epistemology bases its analysis of the objects under study on a prior, a probability distribution, which is in turn the subject matter in statistical learning, and that of machine learning at least implicitly. We are interested in a game setting where the agents to be learned may shift in accordance with the data collector’s strategies. We focus on this issue of learning and exploiting for Myerson auction where a seller wants to gain information on bidders’ value distributions to achieve the maximum revenue. We show that a world of the power-law distribution would enable the auctioneer to achieve both but the bidders can consistently lie about their probability distribution to improve utility under the other distributions.