Using tabu best-response search to find pure strategy nash equilibria in normal form games
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Intelligent Robotics and Autonomous Agents)
Factoring games to isolate strategic interactions
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Empirical game-theoretic analysis of the TAC Supply Chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Stochastic search methods for nash equilibrium approximation in simulation-based games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Searching for approximate equilibria in empirical games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Characterizing effective auction mechanisms: insights from the 2007 TAC market design competition
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Methods for empirical game-theoretic analysis
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Approximate strategic reasoning through hierarchical reduction of large symmetric games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
The supply chain trading agent competition
Electronic Commerce Research and Applications
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
Strategic analysis with simulation-based games
Winter Simulation Conference
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Experimental analysis of agent strategies in multiagent systems presents a tradeoff between granularity and statistical confidence. Collecting a large amount of data about each strategy profile improves confidence, but restricts the range of strategies and profiles that can be explored. We propose a flexible approach, where multiple game-theoretic formulations can be constructed to model the same underlying scenario (observation dataset). The prospect of incorrectly selecting an empirical model is termed generalization risk, and the generalization risk framework we describe provides a general criterion for empirical modeling choices, such as adoption of factored strategies or other structured representations of a game model. We propose a principled method of managing generalization risk to derive the optimal game-theoretic model for the observed data in a restricted class of models. Application to a large dataset generated from a trading agent scenario validates the method.