In this paper, we describe our autonomous bidding agent, RoxyBot, who emerged victorious in the travel division of the 2006 Trading Agent Competition in a photo finish. At a high level, the design of many successful trading agents can be summarized as follows: (i) price prediction: build a model of market prices; and (ii) optimization: solve for an approximately optimal set of bids, given this model. To predict, RoxyBot builds a stochastic model of market prices by simulating simultaneous ascending auctions. To optimize, RoxyBot relies on the sample average approximation method, a stochastic optimization technique.
Cite
@article{arxiv.1401.3829,
title = {RoxyBot-06: Stochastic Prediction and Optimization in TAC Travel},
author = {Amy Greenwald and Seong Jae Lee and Victor Naroditskiy},
journal= {arXiv preprint arXiv:1401.3829},
year = {2014}
}