Incentive compatible open constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Computational-Mechanism Design: A Call to Arms
IEEE Intelligent Systems
Optimal decision-making with minimal waste: strategyproof redistribution of VCG payments
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Achieving budget-balance with Vickrey-based payment schemes in exchanges
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A budget-balanced, incentive-compatible scheme for social choice
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
| Hi-index | 0.00 |
In group decision-making problems that involve selfinterested agents with private information, reaching socially optimal outcomes requires aligning the goals of individuals with the welfare of the entire group. The well-known VCG mechanism achieves this by requiring specific payments from agents to a central coordinator. However, when the goal of coordination is to allow the group to jointly realize the greatest possible welfare, these payments amount to an unwanted cost of implementation, or waste. While it has often been stated that the payments VCG prescribes are necessary in order to implement the socially optimal outcome in dominant strategies without running a deficit, this is in fact not generally true. (Cavallo 2006) specified the mechanism that requires the minimal payments among all mechanisms that are socially optimal, never run a deficit, and are ex post individual rational with an anonymity property. The mechanism achieves significant savings over VCG in a broad range of practically relevant domains, including allocation problems, by using information about the structure of valuations in the domain. This paper gives a high-level overview of that result, and discusses some potential applications to AI.