Designing a successful trading agent for supply chain management
AAMAS '06 Proceedings of the fifth 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
Performance evaluation methods for the trading agent competition
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
TacTex-05: an adaptive agent for TAC SCM
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
A predictive empirical model for pricing and resource allocation decisions
Proceedings of the ninth international conference on Electronic commerce
Flexible decision control in an autonomous trading agent
Electronic Commerce Research and Applications
Visualization and analysis methods for comparing agent behavior in TAC SCM
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the 11th International Conference on Electronic Commerce
Detecting and forecasting economic regimes in multi-agent automated exchanges
Decision Support Systems
Strategy and mechanism lessons from the first ad auctions trading agent competition
Proceedings of the 11th ACM conference on Electronic commerce
What the 2007 TAC Market Design Game tells us about effective auction mechanisms
Autonomous Agents and Multi-Agent Systems
Agent-based competitive simulation: exploring future retail energy markets
Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes
Information Systems Research
Agent-assisted supply chain management: Analysis and lessons learned
Decision Support Systems
| Hi-index | 0.00 |
Market simulations, like their real-world counterparts, are typically domains of high complexity, high variability, and incomplete information. The performance of autonomous agents in these markets depends both upon the strategies of their opponents and on various market conditions, such as supply and demand. Because the space for possible strategies and market conditions is very large, empirical analysis in these domains becomes exceedingly difficult. Researchers who wish to evaluate their agents must run many test games across multiple opponent sets and market conditions to verify that agent performance has actually improved. Our approach is to improve the statistical power of market simulation experiments by controlling their complexity, thereby creating an environment more conducive to structured agent testing and analysis. We develop a tool that controls variability across games in one such market environment, the Trading Agent Competition for Supply Chain Management (TAC SCM), and demonstrate how it provides an efficient, systematic method for TAC SCM researchers to analyze agent performance.