Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Computationally feasible VCG mechanisms
Proceedings of the 2nd ACM conference on Electronic commerce
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Algorithms, games, and the internet
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Bargaining with limited computation: deliberation equilibrium
Artificial Intelligence
Truth revelation in approximately efficient combinatorial auctions
Journal of the ACM (JACM)
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Mechanism Design for Resource Bounded Agents
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Costly valuation computation in auctions
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Effect Lines for Specifying Animation Effects
VLHCC '04 Proceedings of the 2004 IEEE Symposium on Visual Languages - Human Centric Computing
Issues in computational Vickrey auctions
International Journal of Electronic Commerce - Special issue: Intelligent agents for electronic commerce
Selfish Grids: Game-Theoretic Modeling and NAS/PSA Benchmark Evaluation
IEEE Transactions on Parallel and Distributed Systems
Valuation uncertainty and imperfect introspection in second-price auctions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Making markets and democracy work: a story of incentives and computing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Auctions are useful mechanism for allocating items (goods, tasks, resources, etc.) in multiagent systems. The bulk of auction theory assumes that the bidders' valuations for items are given a priori. However, in many applications the bidders need to expend significant computational effort to determine their valuations. We introduce a way of measuring the negative impact of agents choosing computing strategies selfishly. Our miscomputing ratio isolates the effect of selfish computing from that of selfish bidding. We present a Bayes-Nash equilibrium analysis of a Vickrey auction where the bidders' strategies include computational actions. This equilibrium analysis allows us to predict the overhead caused by miscomputing, as measured by the miscomputing ratio. We show that in some situations, the outcome can be arbitrarily far from optimal. However, by carefully designing cost functions for agents, it is possible to provide incentives for bidders to choose computing policies that result in optimal social welfare.