Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper, we present a machine-learning approach to this problem. The technique is based on a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This algorithm, which we present in detail, is at the heart of , a top-scoring agent in the recent Trading Agent Competition (TAC-01). We describe how works, the results of the competition, and controlled experiments evaluating the effectiveness of price prediction in auctions.