Authors:
Yida Xu
and
Hamidou Tembine
Affiliation:
New York University, United Arab Emirates
Keyword(s):
LUBA, Auction, Game Theory, Imitative Learning, Reinforcement Learning.
Related
Ontology
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 limited
to 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 for
an arbitrary number of bidders and items. The equilibrium is explicitly computed in two bidder setup with
resubmission possibilities. In the general setting we propose a distributed strategic learning algorithm to
approximate equilibria. Computer simulations indicate that the error quickly decays in few number of steps
by means of speedup techniques. When the number of bidders per item follows a Poisson distribution, it is
shown that the seller can get a non-negligible revenue on several items, and hence making a partial revelation
of the true value of the items.