Co-evolutionary Auction Mechanism Design: A Preliminary Report
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
A stochastic programming approach to scheduling in TAC SCM
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
An Algorithm for Automatically Designing Deterministic Mechanisms without Payments
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
TacTex-03: a supply chain management agent
ACM SIGecom Exchanges
Evolution of market mechanism through a continuous space of auction-types
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Active learning with statistical models
Journal of Artificial Intelligence Research
Learning payoff functions in infinite games
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The supply chain trading agent competition
Electronic Commerce Research and Applications
An evolutionary game-theoretic comparison of two double-auction market designs
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
A Technique for Large Automated Mechanism Design Problems
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Empirical game-theoretic analysis of the TAC Supply Chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Strategic betting for competitive agents
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
Selecting strategies using empirical game models: an experimental analysis of meta-strategies
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
Thesis summary: empirical game-theoretic methods for strategy design and analysis in complex games
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Quantifying the strategyproofness of mechanisms via metrics on payoff distributions
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Strategy and mechanism lessons from the first ad auctions trading agent competition
Proceedings of the 11th ACM conference on Electronic commerce
Probabilistic analysis of simulation-based games
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Auctions, evolution, and multi-agent learning
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Strategic analysis with simulation-based games
Winter Simulation Conference
Constrained automated mechanism design for infinite games of incomplete information
Autonomous Agents and Multi-Agent Systems
| Hi-index | 0.00 |
Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings. We illustrate our approach with a design task from a supply-chain trading competition. Designers adopted several rule changes in order to deter particular procurement behavior, but the measures proved insufficient. Our empirical mechanism analysis models the relation between a key design parameter and outcomes, confirming the observed behavior and indicating that no reasonable parameter settings would have been likely to achieve the desired effect. More generally, we show that under certain conditions, the estimator of optimal mechanism parameter setting based on empirical data is consistent.