Algorithm for optimal winner determination in combinatorial auctions
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TLDR
The algorithm allows combinatorial auctions to scale up to significantly larger numbers of items and bids than prior approaches to optimal winner determination by capitalizing on the fact that the space of bids is sparsely populated in practice.About:
This article is published in Artificial Intelligence.The article was published on 2002-02-01 and is currently open access. It has received 1045 citations till now. The article focuses on the topics: Combinatorial auction & Common value auction.read more
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Journal ArticleDOI
Designing bidding strategies in sequential auctions for risk averse agents
Valentin Robu,Han La Poutré +1 more
TL;DR: In this paper, the effect of risk profiles on the bidders' strategies in sequential auctions is investigated, for both first price and second price sequential auctions, and the results show that the presence of risk aversion affects both bidder profits and auctioneer revenue for different market scenarios of increasing complexity.
Proceedings ArticleDOI
Efficient algorithms for combinatorial auctions with volume discounts arising in web service composition
B. Prashanth,Yadati Narahari +1 more
TL;DR: This paper develops efficient algorithms for the web service composition problem in the presence of composite web service offerings and volume discounts by model this problem as a combinatorial auction with volume discounts.
Book ChapterDOI
A Realistic Approach to Solve the Nash Welfare
TL;DR: After a study of different bilateral transaction types, the most efficient negotiation policy is underline in order to solve the multi-agent resource allocation problem with the Nash product and provide an adaptive, scalable and anytime algorithm.
Book
Auction protocols
TL;DR: The author’s research focused on the development of algorithms for Winner Determination Algorithms for Combinatorial Auctions, which addressed the dilemma of how to choose a winner from a large number of bids.
More than the sum of its parts : compact preference representation over combinatorial domains
TL;DR: In this paper, the authors propose a 2.225-approximation of the standard deviation of 0.75-0.225.0.0% for each node.
References
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Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book ChapterDOI
Reducibility Among Combinatorial Problems
TL;DR: The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible.
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Integer programming
TL;DR: The principles of integer programming are directed toward finding solutions to problems from the fields of economic planning, engineering design, and combinatorial optimization as mentioned in this paper, which is a standard of graduate-level courses since 1972.
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Incentives in Teams
TL;DR: This paper analyzes the problem of inducing the members of an organization to behave as if they formed a team and exhibits a particular set of compensation rules, an optimal incentive structure, that leads to team behavior.