Authors:Yida Xu andHamidou Tembine

Affiliation:New York University, United Arab Emirates

Keyword(s):LUBA, Auction, Game Theory, Imitative Learning, Reinforcement Learning.

RelatedOntology Subjects/Areas/Topics:Agents ;Artificial Intelligence ;Artificial Intelligence and Decision Support Systems ;Computational Intelligence ;Distributed and Mobile Software Systems ;Economic Agent Models ;Enterprise Information Systems ;Evolutionary Computing ;Knowledge Discovery and Information Retrieval ;Knowledge Engineering and Ontology Development ;Knowledge-Based Systems ;Machine Learning ;Multi-Agent Systems ;Soft Computing ;Software Engineering ;Symbolic Systems

Abstract:The recent online platforms propose multiple items for bidding. The state of the art, however, is limitedto the analysis of one item auction. In this paper we study multi-item lowest unique bid auctions (LUBA)in discrete bid spaces under budget constraints. We show the existence of mixed Bayes-Nash equilibria foran arbitrary number of bidders and items. The equilibrium is explicitly computed in two bidder setup withresubmission possibilities. In the general setting we propose a distributed strategic learning algorithm toapproximate equilibria. Computer simulations indicate that the error quickly decays in few number of stepsby means of speedup techniques. When the number of bidders per item follows a Poisson distribution, it isshown that the seller can get a non-negligible revenue on several items, and hence making a partial revelationof the true value of the items.