Empirical game-theoretic analysis of the TAC Supply Chain game

The TAC Supply Chain Management (TAC/SCM) game presents a challenging dynamic environment for autonomous decision-making in a salient application domain. Strategic interactions complicate the analysis of games such as TAC/SCM. since the effectiveness of a given strategy depends on the strategies played by other agents on the supply chain. The TAC tournament generates results from one particular path of combinations, and success in the tournament is rightly regarded as evidence for agent quality. Such results along with post-competition controlled experiments provide useful evaluations of novel techniques employed in the game. We argue that a broader game-theoretic analysis framework can provide a firmer foundation for choice of experimental contexts. Exploiting a repository of agents from the 2005 and 2006 TAC/SCM tournaments, we demonstrate an empirical game-theoretic methodology based on extensive simulation and careful measurement. Our analysis of agents from TAC-05 reveals interesting interactions not seen in the tournament. Extending the analysis to TAC-06 enables us to measure progress from year-to-year, and generates a candidate empirical equilibrium among the best known strategies. We use this equilibrium as a stable background population for comparing relative performance of the 2006 agents, yielding insights complementing the tournament results.

[1]  W. Hamilton,et al.  The Evolution of Cooperation , 1984 .

[2]  Shou-De Lin,et al.  Designing the Market Game for a Trading Agent Competition , 2001, IEEE Internet Comput..

[3]  Michael P. Wellman,et al.  STRATEGIC INTERACTIONS IN A SUPPLY CHAIN GAME , 2005, Comput. Intell..

[4]  Joakim Eriksson,et al.  Evolution of a supply chain management game for the Trading Agent Competition , 2006, AI Commun..

[5]  Pericles A. Mitkas,et al.  A Robust Agent Design for Dynamic SCM Environments , 2006, SETN.

[6]  Norman M. Sadeh,et al.  The supply chain trading agent competition , 2005, Electron. Commer. Res. Appl..

[7]  Peter Stone,et al.  TacTex-05: A Champion Supply Chain Management Agent , 2006, AAAI.

[8]  Michael P. Wellman,et al.  Generating trading agent strategies: Analytic and empirical methods for infinite and large games , 2005 .

[9]  Michael P. Wellman,et al.  An analysis of the 2004 supply chain management trading agent competition , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[10]  Simon Parsons,et al.  A novel method for automatic strategy acquisition in N-player non-zero-sum games , 2006, AAMAS '06.

[11]  D. Friedman EVOLUTIONARY GAMES IN ECONOMICS , 1991 .

[12]  Michael P. Wellman,et al.  Empirical mechanism design: methods, with application to a supply-chain scenario , 2006, EC '06.

[13]  Michael P. Wellman,et al.  Approximate Strategic Reasoning through Hierarchical Reduction of Large Symmetric Games , 2005, AAAI.

[14]  Nicholas R. Jennings,et al.  Designing a successful trading agent for supply chain management , 2006, AAMAS '06.

[15]  G A Parker,et al.  Evolutionary Stable Strategies , 1984, Encyclopedia of Evolutionary Psychological Science.

[16]  Leigh Tesfatsion,et al.  Agent-based computational economics: modeling economies as complex adaptive systems , 2003, Inf. Sci..

[17]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[18]  P. Taylor,et al.  Evolutionarily Stable Strategies and Game Dynamics , 1978 .

[19]  Jason Miller,et al.  Controlling a supply chain agent using value-based decomposition , 2006, EC '06.

[20]  Peter Stone,et al.  Predictive Planning for Supply Chain Management , 2006, ICAPS.

[21]  John Collins,et al.  The Supply Chain Management Game for the 2007 Trading Agent Competition , 2004 .

[22]  Rajarshi Das,et al.  Choosing Samples to Compute Heuristic-Strategy Nash Equilibrium , 2003, AMEC.