Evaluation of Market Design Agents: The Mertacor Perspective

The annual Trading Agent Competition for Market Design, cat, provides a testbed to study the mechanisms that modern stock exchanges use in their effort to attract potential traders while maximizing their profit. This paper presents an evaluation of the agents that participated in the 2008 competition. The evaluation is based on the analysis of the cat finals as well as on the results obtained from post-tournament experiments. We present Mertacor, our entrant for 2008, and compare it with the other available agents. In addition, we introduce a simple yet effective way of computing the global competitive equilibrium that Mertacor utilizes and discuss its importance for the game.

[1]  Michael P. Wellman,et al.  Flexible double auctions for electronic commerce: theory and implementation , 1998, Decis. Support Syst..

[2]  Rajarshi Das,et al.  High-performance bidding agents for the continuous double auction , 2001, EC '01.

[3]  Peter McBurney,et al.  An Analysis of Entries in the First TAC Market Design Competition , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[4]  Leigh Tesfatsion,et al.  Agent-Based Computational Economics: Growing Economies From the Bottom Up , 2002, Artificial Life.

[5]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[6]  Longbing Cao,et al.  Agent Mining: The Synergy of Agents and Data Mining , 2009, IEEE Intelligent Systems.

[7]  Elizabeth Sklar,et al.  Some preliminary results on competition between markets for automated traders , 2007 .

[8]  S. Parsons,et al.  Overview of CAT: A Market Design Competition , 2009 .

[9]  Pericles A. Mitkas,et al.  A Sequence Mining Method to Predict the Bidding Strategy of Trading Agents , 2009, ADMI.

[10]  Kalyan Chatterjee,et al.  Bargaining under Incomplete Information , 1983, Oper. Res..

[11]  Dhananjay K. Gode,et al.  Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality , 1993, Journal of Political Economy.

[12]  J. Dickhaut,et al.  Price Formation in Double Auctions , 1998 .

[13]  Peter McBurney,et al.  JCAT: a platform for the TAC market design competition , 2008, AAMAS.

[14]  Nicholas R. Jennings,et al.  IAMwildCAT: The Winning Strategy for the TAC Market Design Competition , 2008, ECAI.

[15]  Nicholas R. Jennings,et al.  Market-Based Task Allocation Mechanisms for Limited-Capacity Suppliers , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Pericles A. Mitkas.  Knowledge Discovery for Training Intelligent Agents: Methodology, Tools and Applications , 2005, AIS-ADM.

[17]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[18]  Perukrishnen Vytelingum,et al.  The structure and behaviour of the continuous double auction , 2006 .

[19]  A. Bowman,et al.  Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .

[20]  Michael Schroeder,et al.  Market-based Resource Allocation for Grid Computing: A Model and Simulation , 2003, Middleware Workshops.

[21]  Pericles A. Mitkas,et al.  Designing Pricing Mechanisms for Autonomous Agents Based on Bid-Forecasting , 2005, Electron. Mark..

[22]  V. Smith.  An Experimental Study of Competitive Market Behavior , 1962, Journal of Political Economy.

[23]  N. Nisan,et al.  Globally distributed computation over the Internet-the POPCORN project , 1998, Proceedings. 18th International Conference on Distributed Computing Systems (Cat. No.98CB36183).

[24]  Pericles A. Mitkas,et al.  Agent intelligence through data mining , 2006, Multiagent systems, artificial societies, and simulated organizations.