Abstract
In the CAT Tournament, specialists facilitate transactions between buyers and sellers with the intention of maximizing profit from commission and other fees. Each specialist must find a well-balanced strategy that allows it to entice buyers and sellers to trade in its market while also retaining the buyers and sellers that are currently subscribed to it. Classification techniques can be used to determine the distribution of bidding strategies used by all traders subscribed to a particular specialist. Our experiments showed that Hidden Markov Model classification yielded the best results. The distribution of strategies, along with other competition-related factors, can be used to determine the optimal action in any given game state. Experimental data shows that the GD and ZIP bidding strategies are more volatile than the RE and ZIC strategies. An MDP framework for determining optimal actions given an accurate distribution of bidding strategies is proposed as a motivator for future work.
This is a preview of subscription content, log in via an institution to check access.
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
Gerding, E., McBurney, P., Niu, J., Parsons, S., Phelps, S.: Overview of CAT: A Market Design Competition. Technical Report ULCS-07-006. Department of Computer Science, University of Liverpool. Liverpool, UK (2007), http://www.csc.liv.ac.uk/research/techreports/tr2007/tr07006 abs.html
Cliff, D.: Minimal-intelligence agents for bargaining behaviors in market-based environments. Technical Report HP–97–91, Hewlett Packard Laboratories, Bristol, England (1997)
Gjerstad, S., Dickhaut, J.: Price Formation in Double Auctions. Games and Economic Behavior 22, 1–29 (1998)
Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: Markets as a partial substitute for individual rationality. The Journal of Political Economy 101(1), 119–137 (1993)
Roth, A.E., Erev, I.: Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior 8, 164–212 (1995)
Niu, J., Cai, K., Parsons, S., Gerding, E., McBurney, P., Moyaux, T., Phelps, S., Shield, D.: JCAT: A platform for the TAC Market Design Competition. In: Proceedings of the Seventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2008), Estoril, Portugal (May 2008)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Bilmes, J.A.: What HMMs can do. IEICE Transactions on Information and Systems E89-D(3), 869–891 (2006)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. In: Proceedings of the IEEE International Conference, vol. 77(2), pp. 257–286 (February 1989)
Ruping, S.: SVM kernels for time series analysis (Technical Report), Department of Computer Science. University of Dortmund, Dortmund, Germany (2001)
Grauman, K., Darrell, T.: The Pyramid Match kernel: discriminative classification with sets of image features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Beijing, China (October 2005)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence, Englewood Cliffs, New Jersey (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gruman, M.L., Narayana, M. (2010). Applications of Classifying Bidding Strategies for the CAT Tournament. In: Ketter, W., La Poutré, H., Sadeh, N., Shehory, O., Walsh, W. (eds) Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis. AMEC TADA 2008 2008. Lecture Notes in Business Information Processing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15237-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-15237-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15236-8
Online ISBN: 978-3-642-15237-5
eBook Packages: Computer ScienceComputer Science (R0)