Abstract
Auction theory has proven that auction revenue is influenced by factors such as the auction format and the auction parameters. The Revenue Maximising Adaptive Auctioneer (RMAA) agent model has been developed with the aim of generating maximum auction revenue by adapting the auction format and parameters to suit the auction environment. The RMAA agent uses a learning classifier system to learn which rules are profitable in a particular bidding environment. The profitable rules are then exploited by the RMAA agent to generate maximum revenue. The RMAA agent model can effectively adapt to a real time dynamic auction environment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Gerding, E.H., Rogers, A., Dash, R.K., Jennings, N.R.: Competing Sellers in Online Markets: Reserve Prices, Shill bidding, and Auction Fees. In: 5th International Joint Conference on Autonomous agents and Multiagent Systems, New York, pp. 1208–1210 (2006)
Milgrom, P.: Putting Auction Theory to Work. Cambridge University Press, Cambridge (2004)
Klemperer, P.: Auction Theory: A Guide to the Literature, Technical report, EconWPA (1999)
Monderer, D., Tennenholtz, M.: Optimal Auctions Revisited. Faculty of Industrial Engineering Technion – Israel Institute of Technology (1998)
Sandholm, T.: Distributed Rational Decision Making in Multiagent Systems. In: Weiss, G. (ed.), ch. 5. MIT Press, Cambridge (1999)
Lucking-Reiley, D.: Using Field Experiments to Test Equivalence between Auction Formats: Magic on the Internet. The American Economic Review 89(5), 1063–1080 (1999)
Bryan, D., Lucking-Reiley, D., Prasad, N., Reeves, D.: Pennies from eBay: the Determinants of Price in Online Auctions. Econometric Society World Congress 2000 Contributed Papers 1736, Econometric Society (2000)
Onur, I., Tomak, K.: Impact of ending rules in online auctions: the case of Yahoo.com. Decis. Support Syst. 42(3), 1835–1842 (2006)
Gregg, D.G., Walczak, S.: Auction Advisor: an agent-based online-auction decision support system. Decis. Support Syst. 41(2), 449–471 (2006)
Pardoe, D., Stone, P.: Developing Adaptive Auction Mechanisms. SIGecom Exch. 5(3), 1–10 (2005)
Cliff, D.: Evolution of Market Mechanism Through a Continuous Space of Auction-Types. In: IEEE Congress on Evolutionary Computation on 2002, Washington, DC, USA, pp. 2029–2034 (2002)
Wilson, S.W.: ZCS: A Zeroth Level Classifier System. Evol. Comput. 2(1), 1–18 (1994)
Zhang, J., Zhang, N., Chung, J.: Assisting Seller Pricing Strategy Selection for Electronic Auctions. In: IEEE International Conference on E-Commerce Technology, pp. 27–33. IEEE Computer Society, Washington (2004)
Vermorel, J., Mohry, M.: Multi-armed Bandit Algorithms and Empirical Evaluation. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS, vol. 3720, pp. 437–448. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pike, J.C., Ehlers, E.M. (2008). Revenue Maximising Adaptive Auctioneer Agent. In: Bui, T.D., Ho, T.V., Ha, Q.T. (eds) Intelligent Agents and Multi-Agent Systems. PRIMA 2008. Lecture Notes in Computer Science(), vol 5357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89674-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-89674-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89673-9
Online ISBN: 978-3-540-89674-6
eBook Packages: Computer ScienceComputer Science (R0)