Applications of Classifying Bidding Strategies for the CAT Tournament

Mark L Gruman, Manjunath Narayana

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, although no traders ever readily switch specialists. An MDP framework for determining optimal actions given an accurate distribution of bidding strategies is proposed as a motivator for future work.

Subjects: 12. Machine Learning and Discovery; 3. Automated Reasoning

Submitted: May 5, 2008


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