Algorithms, games, and the internet
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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
Exploring bidding strategies for market-based scheduling
Proceedings of the 4th ACM conference on Electronic commerce
Developing adaptive auction mechanisms
ACM SIGecom Exchanges
Adaptive mechanism design: a metalearning approach
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
A novel method for automatic strategy acquisition in N-player non-zero-sum games
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Settling the Complexity of Two-Player Nash Equilibrium
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Bidding algorithms for a distributed combinatorial auction
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Evolutionary stability of behavioural types in the continuous double auction
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
Analyzing and taming collective learning of a multiagent system with connected replicator dynamics
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Multi-agent Cooperative Cleaning of Expanding Domains
International Journal of Robotics Research
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Mechanism design (MD) has recently become a very popular approach in the design of distributed systems of autonomous agents. A key assumption required for the application of MD is that agents behave rationally in the mechanism or game, since this provides the predictability of agent behavior required for optimal design of the mechanism. In many cases, however, we are confronted with the intractability both of establishing rational equilibrium behavior, as well as of designing optimal mechanisms even if rational agent behavior can be assumed. In this paper, we study both sides of the problem simultaneously by designing and analyzing a 'meta-game' involving both the designer of the mechanism (game, multi-agent system) and the agents interacting in the system. We use coupled replicator dynamics to investigate equilibrium out-comes in this game. In addition, we present an algorithm for determining the expected payoffs required for our analysis, thus sidestepping the need for extensive simulations as in previous work. Our results show the validity of the algorithm, some interesting conclusions about multi-period auction design, and the general feasibility of our approach.