On Bayesian Epistemology of Myerson Auction

• Published in: Frontiers in Algorithmics Vol. 10823; pp. 183 - 196
• Main Authors: Deng, Xiaotie, Zhu, Keyu
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Bayesian Epistemology; Bidding game Nash equilibrium; Myerson auction; Probability distribution cheating; Statistical learning
• ISBN: 3319784544; 9783319784540
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-78455-7_14

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.