Abstract
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.
Research results reported in this work are partially supported by the National Natural Science Foundation of China (Grant Nos. 61632017, 61173011).
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Deng, X., Zhu, K. (2018). On Bayesian Epistemology of Myerson Auction. In: Chen, J., Lu, P. (eds) Frontiers in Algorithmics. FAW 2018. Lecture Notes in Computer Science(), vol 10823. Springer, Cham. https://doi.org/10.1007/978-3-319-78455-7_14
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DOI: https://doi.org/10.1007/978-3-319-78455-7_14
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