Outperforming the competition in multi-unit sealed bid auctions

ABSTRACT

In this paper, we examine the behavior of bidding agents that are in direct competition with the other participants in an auction setting. Thus the agents are not simply trying to maximize their own utility, rather they wish to maximize a weighted difference of their own gain to that of their competitors. By so doing, this work significantly extends the existing state-of-the-art results on single unit auctions, by generalizing to the multi-unit case. Specifically, our main result is the derivation of symmetric Bayes-Nash equilibria for these agents in both m^th and (m + 1)^th price sealed bid auctions. Subsequently, we use these equilibria to examine the profits of different agents and show that aiming to beat the competition is more effective than pure self interest in any competitive setting. Finally, we examine how the auctioneer's revenue is affected and find that the weight that agents place in minimizing the opponents' profit determines whether the m^th or the (m + 1)^th price auction yields a higher revenue.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Economics, Experimentation, Theory

Keywords:
bidding strategies, equilibrium analysis, game theory, revenue, simulation