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In this paper, we describe our autonomous bidding agent, RoxyBot, who emergedvictorious in the travel division of the 2006 Trading Agent Competition in aphoto finish. At a high level, the design of many successful trading agents canbe summarized as follows: (i) price prediction: build a model of market prices;and (ii) optimization: solve for an approximately optimal set of bids, giventhis model. To predict, RoxyBot builds a stochastic model of market prices bysimulating simultaneous ascending auctions. To optimize, RoxyBot relies on thesample average approximation method, a stochastic optimization technique.

RoxyBot-06: Stochastic Prediction and Optimization in TAC Travel