Computationally feasible VCG mechanisms
Proceedings of the 2nd ACM conference on Electronic commerce
Truth revelation in approximately efficient combinatorial auctions
Journal of the ACM (JACM)
Mechanism Design for Resource Bounded Agents
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Iterative combinatorial auctions: achieving economic and computational efficiency
Iterative combinatorial auctions: achieving economic and computational efficiency
Towards a Characterization of Truthful Combinatorial Auctions
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Costly valuation computation in auctions
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Auction design with costly preference elicitation
Annals of Mathematics and Artificial Intelligence
Mechanism design and deliberative agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Issues in computational Vickrey auctions
International Journal of Electronic Commerce - Special issue: Intelligent agents for electronic commerce
Using performance profile trees to improve deliberation control
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Valuation uncertainty and imperfect introspection in second-price auctions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient metadeliberation auctions
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
On revenue maximization for agents with costly information acquisition: extended abstract
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
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Most research on auctions assumes that potential bidders have private information about their willingness to pay for the item being auctioned, and that they use this information strategically when formulating their bids. In reality, bidders often have to go through a costly information-gathering process in order to learn their valuation for the item being auctioned. Recent attempts at modelling this phenomena has brought to light complex strategic behavior arising from information-gathering, and has shown that traditional approaches to auction and mechanism design are not able to overcome it. In this paper, we show that if the auction designer has some information about the agents' information-gathering processes, then it is possible to create an auction where, in equilibrium, agents have incentive to only gather information on their own valuation problems and to reveal the results truthfully to the auctioneer. Additionally, simulation results show that, from a system-level perspective, the overall cost of information acquisition is substantially lower in this new auction when it is compared to a classic auction mechanism.