Bidding Agents for Online Auctions with Hidden Bids

by Albert Xin Jiang

There is much active research into the design of automated bidding agents, particularly for environments that involve multiple auctions. These settings are complex partly because an agent's optimal strategy depends on information about other bidder's preferences. When bidders' valuation distributions are not known ex ante, machine learning techniques can be used to approximate them from historical data. It is a characteristic feature of auctions, however, that information about some bidders' valuations is systematically concealed. This occurs in the sense that some bidders may fail to bid at all because the asking price exceeds their valuations, and also in the sense that a high bidder may not be compelled to reveal her valuation. Ignoring these "hidden bids" can introduce bias into the estimation of valuation distributions. To overcome this problem, we proposed an EM-base algorithm. We validate the algorithm experimentally using agents that react to their environments both decision-theoretically and game-theoretically, using both synthetic and real-world (eBay) datasets. We show that our approach estimates bidders' valuation distributions and the distribution over the true number of bidders significantly more accurately than more straightforward density estimation techniques. Bidding agents using the estimated distributions from our EM approach were able to outperform bidding agents using the straightforward estimates, in both decision-theoretic and game-theoretic settings.

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