Work

Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions

Year: 2003

Type: article

Abstract: Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This article presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. A core component of our approach learns a model of the empirical price dynamics based on past data and uses the model to analytically calculate, to the greatest extent possible, optimal bids. We introduce 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 approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). We present experiments demonstrating the effectiveness of our boosting-based price predictor relative to several reasonable alternatives.

Source: Journal of Artificial Intelligence Research

DOI: 10.1613/jair.1200

Authors: P. Stone, R. E. Schapire, M. L. Littman, J. A. Csirik, D. McAllester

Institutions: Princeton University, The University of Texas at Austin, Rutgers, The State University of New Jersey, D. E. Shaw Research, Toyota Technological Institute at Chicago

Language: English

Topic: Auction Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W2102674295

FWCI: 11.58

Cites: 32

Cited by: 66

Related to: 10