Incentive compatible regression learning
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Stepwise randomized combinatorial auctions achieve revenue monotonicity
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Trading in risk: using markets to improve access control
Proceedings of the 2008 workshop on New security paradigms
Computational analysis of an auction for licensed and unlicensed use of spectrum
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
The value of location in keyword auctions
Electronic Commerce Research and Applications
Autonomous Agents and Multi-Agent Systems
Incentive compatible regression learning
Journal of Computer and System Sciences
Keyword auction protocol for dynamically adjusting the number of advertisements
Web Intelligence and Agent Systems
Revenue monotonicity in deterministic, dominant-strategy combinatorial auctions
Artificial Intelligence
A public procurement combinatorial auction mechanism with quality assignment
Decision Support Systems
Fairness In Recurrent Auctions With Competing Markets And Supply Fluctuations
Computational Intelligence
Quadratic Core-Selecting Payment Rules for Combinatorial Auctions
Operations Research
Economic co-allocation and advance reservation of network and computational resources in grids
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
Stochastic game for wireless network virtualization
IEEE/ACM Transactions on Networking (TON)
| Hi-index | 0.01 |
In theory, the mathematically elegant Vickrey-Clarke-Groves process offers perfect efficiency with dominant truth-revealing strategies. However, it has many serious practical problems. This paper describes these problems and argues that research that aims to maintain the dominant truth-revealing strategies while compromising on the other practical issues is of limited practical value.