Abstract
The aim of this research is to develop an adaptive agent based model of auction scenarios commonly used in auction theory to help understand how competitors in auctions reach equilibria strategies through the process of learning from experience. This paper describes the private value model of auctions commonly used in auction theory and experimentation and the initial reinforcement learning architecture of the adaptive agent competing in auctions against opponents following a known optimal strategy. Three sets of experiments are conducted: the first establishes the learning scheme can learn optimal behaviour in ideal conditions; the second shows that the simplest approach to dealing with situations of uncertainty does not lead to optimal behaviour; the third demonstrates that using the information assumed common to all in private value model allows the agent to learn the optimal strategy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
FreeMarkets Inc., http://www.freemarkets.com/
FreightTraders Ltd., http://www.freight-traders.com/
Chavez, A., Moukas, A., Maes, P.: Challenger: A multi-agent system for distributed resource allocation. In: Lewis Johnson, W., Hayes-Roth, B. (eds.) Proceedings of the First International Conference on Autonomous Agents (Agents 1997), pp. 323–331. ACM Press, New York (1997)
Bagnall, J., Smith, G.D.: An adaptive agent model for generator company bidding in the uk power pool. In: Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M., Ronald, E. (eds.) AE 1999. LNCS, vol. 1829, pp. 191–203. Springer, Heidelberg (2000)
Cliff, D.: Evolution of market mechanism through a continuous space of auctiontypes. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 206–209 (2002)
Cliff, D., Bruten, J.: Zero is not enough: On the lower limit of agent intelligence for continuous double auction markets (1997)
David, H.A.: Order Statistics. John Wiley and Sons, Chichester (1970)
Friedman, D., Rust, J. (eds.): The Double Auction Market. Perseus Publishing, Cambridge (1991)
Gode, D., Sunder, S.: Allocative efficiency of markets with zero intelligence traders. Journal of Political Economy 101, 119–137 (1993)
Hu, J., Wellman, M.P.: Multiagent reinforcement learning: theoretical framework and an algorithm. In: Proc. 15th International Conf. on Machine Learning, pp. 242–250. Morgan Kaufmann, San Francisco (1998)
Krishna, V.: Auction Theory. Harcourt Publishers Ltd., New York (2002)
Sawhney, M., Kaplan, S.: The-b-to-b-boom, let’s get vertical. Business 2.0 (September 1999)
Tesauro, G., Das, R.: High-performance bidding agents for the continuous double auction. In: Proceedings of the Third acm Conference on Electronic Commerce, pp. 206–209 (2001)
Vickery, W.: Counterspeculation, auctions, and competitive sealed tenders. Journal of Finance 16, 8–37 (1961)
Wellman, M.P., Hu, J.: Conjectural equilibrium in multiagent learning. Machine Learning 33(2-3), 179–200 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bagnall, A.J., Toft, I. (2004). An Agent Model for First Price and Second Price Private Value Auctions. In: Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2003. Lecture Notes in Computer Science, vol 2936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24621-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-24621-3_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21523-3
Online ISBN: 978-3-540-24621-3
eBook Packages: Springer Book Archive