Competitive analysis of incentive compatible on-line auctions
Proceedings of the 2nd ACM conference on Electronic commerce
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Online auctions with re-usable goods
Proceedings of the 6th ACM conference on Electronic commerce
Truthful and Near-Optimal Mechanism Design via Linear Programming
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Online Stochastic Combinatorial Optimization
Online Stochastic Combinatorial Optimization
Dynamic Mechanism Design for Online Commerce
Operations Research
Automated online mechanism design and prophet inequalities
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
An ironing-based approach to adaptive online mechanism design in single-valued domains
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Expressive banner ad auctions and model-based online optimization for clearing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Monotone branch-and-bound search for restricted combinatorial auctions
Proceedings of the 13th ACM Conference on Electronic Commerce
A model-based online mechanism with pre-commitment and its application to electric vehicle charging
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Journal of Artificial Intelligence Research
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We exploit methods of sample-based stochastic optimization for the purpose of strategyproof dynamic, multi-unit auctions. There are no analytic characterizations of optimal policies for this domain and thus a heuristic approach, such as that proposed here, seems necessary in practice. Following the suggestion of Parkes and Duong [17], we perform sensitivity analysis on the allocation decisions of an online algorithm for stochastic optimization, and correct the decisions to enable a strategyproof auction. In applying this approach to the allocation of non-expiring goods, the technical problem that we must address is related to achieving strategyproofness for reports of departure. This cannot be achieved through self-correction without canceling many allocation decisions, and must instead be achieved by first modifying the underlying algorithm. We introduce the NowWait method for this purpose, prove its successful interfacing with sensitivity analysis and demonstrate good empirical performance. Our method is quite general, requiring a technical property of uncertainty independence, and that values are not too positively correlated with agent patience. We also show how to incorporate "virtual valuations" in order to increase the seller's revenue.