Algorithm for optimal winner determination in combinatorial auctions
TL;DR: 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.
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01 Jan 2007TL;DR: A new era of theoretical computer science addresses fundamental problems about auctions, networks, and human behavior in a bid to solve the challenges of 21st Century finance.
Abstract: A new era of theoretical computer science addresses fundamental problems about auctions, networks, and human behavior.
1,994 citations
Book•
09 Dec 2005
TL;DR: It's important for you to start having that hobby that will lead you to join in better concept of life and reading will be a positive activity to do every time.
Abstract: combinatorial auctions What to say and what to do when mostly your friends love reading? Are you the one that don't have such hobby? So, it's important for you to start having that hobby. You know, reading is not the force. We're sure that reading will lead you to join in better concept of life. Reading will be a positive activity to do every time. And do you know our friends become fans of combinatorial auctions as the best book to read? Yeah, it's neither an obligation nor order. It is the referred book that will not make you feel disappointed.
1,139 citations
TL;DR: The state of knowledge about the design of combinatorial auctions is surveyed and some new insights are presented.
Abstract: Many auctions involve the sale of a variety of distinct assets. Examples are airport time slots, delivery routes, network routing, and furniture. Because of complementarities or substitution effects between the different assets, bidders have preferences not just for particular items but for sets of items. For this reason, economic efficiency is enhanced if bidders are allowed to bid on bundles or combinations of different assets. This paper surveys the state of knowledge about the design of combinatorial auctions and presents some new insights. Periodic updates of portions of this survey will be posted to this journal's Online Supplements web page at http://joc.pubs.informs.org/OnlineSupplements.html.
1,096 citations
Cites background from "Algorithm for optimal winner determ..."
...(1999), and Sandholm (1999)....
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...(1999) as well as Sandholm (1999) and Andersson et. al....
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...The penultimate section is devoted to incentive issues in the design of combinatorial auctions....
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...Notice that this is large enough to handle the test problems considered in Sandholm (1999) and Fujishima et. al....
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...(Auctions; Combinatorial Optimization ) ∗Zentrum Mathematik, TU München, D-80290 München, Germany. e-mail: devries@ma.tum.de †Department of Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, Evanston IL 60208. e-mail: r-vohra@nwu.edu Contents...
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21 Aug 2006
TL;DR: An introduction to market-based multirobot coordination is provided, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges are discussed.
Abstract: Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to market-based multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges
896 citations
Cites background from "Algorithm for optimal winner determ..."
...cases, due to the sparseness of the bids, the auction clearing step can be done optimally and quickly [36], although the resulting allocation is still likely to be suboptimal given that not all task bundles are considered....
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TL;DR: This paper addresses task allocation to coordinate a fleet of autonomous vehicles by presenting two decentralized algorithms: the consensus-based auction algorithm (CBAA) and its generalization to the multi-assignment problem, i.e., theensus-based bundle algorithm ( CBBA).
Abstract: This paper addresses task allocation to coordinate a fleet of autonomous vehicles by presenting two decentralized algorithms: the consensus-based auction algorithm (CBAA) and its generalization to the multi-assignment problem, i.e., the consensus-based bundle algorithm (CBBA). These algorithms utilize a market-based decision strategy as the mechanism for decentralized task selection and use a consensus routine based on local communication as the conflict resolution mechanism to achieve agreement on the winning bid values. Under reasonable assumptions on the scoring scheme, both of the proposed algorithms are proven to guarantee convergence to a conflict-free assignment, and it is shown that the converged solutions exhibit provable worst-case performance. It is also demonstrated that CBAA and CBBA produce conflict-free feasible solutions that are robust to both inconsistencies in the situational awareness across the fleet and variations in the communication network topology. Numerical experiments confirm superior convergence properties and performance when compared with existing auction-based task-allocation algorithms.
761 citations
Cites methods from "Algorithm for optimal winner determ..."
...The winner determination has been shown to be NP-complete [46], and only heuristic methods [47]–[49] are available....
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References
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Book•
01 Jan 1990TL;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.
Abstract: From the Publisher:
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. Like the first edition,this text can also be used for self-study by technical professionals since it discusses engineering issues in algorithm design as well as the mathematical aspects.
In its new edition,Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity,and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage.
As in the classic first edition,this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further,the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds.
Each chapter presents an algorithm,a design technique,an application area,or a related topic. The chapters are not dependent on one another,so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally,the new edition offers a 25% increase over the first edition in the number of problems,giving the book 155 problems and over 900 exercises thatreinforcethe concepts the students are learning.
21,651 citations
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.
Abstract: Throughout the 1960s I worked on combinatorial optimization problems including logic circuit design with Paul Roth and assembly line balancing and the traveling salesman problem with Mike Held. These experiences made me aware that seemingly simple discrete optimization problems could hold the seeds of combinatorial explosions. 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. Jack Edmonds’ papers and a few key discussions with him drew my attention to the crucial distinction between polynomial-time and superpolynomial-time solvability. I was also influenced by Jack’s emphasis on min-max theorems as a tool for fast verification of optimal solutions, which foreshadowed Steve Cook’s definition of the complexity class NP. Another influence was George Dantzig’s suggestion that integer programming could serve as a universal format for combinatorial optimization problems.
8,644 citations
7,666 citations
"Algorithm for optimal winner determ..." refers methods in this paper
...With a fully expressive bidding language, bidding truthfully can be made incentive compatible (a dominant strategy) by using the Vickrey-Clarke-Groves mechanism [58, 7, 17]....
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Book•
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01 Jan 1972
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.
Abstract: The principles of integer programming are directed toward finding solutions to problems from the fields of economic planning, engineering design, and combinatorial optimization. This highly respected and much-cited text, a standard of graduate-level courses since 1972, presents a comprehensive treatment of the first two decades of research on integer programming.
4,336 citations
"Algorithm for optimal winner determ..." refers background or methods in this paper
...This linear program at each search node can be solved in polynomial time in the size of the input (which itself is (nm)) using interior point methods, or fast on average using the simplex method [11, 60]....
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...There are several modern books on this approach [11, 60], and good overviews of applying this approach to combinatorial auctions have been written very recently [13, 37]....
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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.
Abstract: This paper analyzes the problem of inducing the members of an organization to behave as if they formed a team. Considered is a conglomerate-type organization consisting of a set of semi-autonomous subunits that are coordinated by the organization's head. The head's incentive problem is to choose a set of employee compensation rules that will induce his subunit managers to communicate accurate information and take optimal decisions. The main result exhibits a particular set of compensation rules, an optimal incentive structure, that leads to team behavior. Particular attention is directed to the informational aspects of the problem. An extended example of a resource allocation model is discussed and the optimal incentive structure is interpreted in terms of prices charged by the head for resources allocated to the subunits.
3,347 citations
"Algorithm for optimal winner determ..." refers methods in this paper
...With a fully expressive bidding language, bidding truthfully can be made incentive compatible (a dominant strategy) by using the Vickrey–Clarke–Groves mechanism [7,17, 58]....
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