A Tractable and Expressive Class of Marginal Contribution Nets and Its Applications

Coalitional games raise a number of important questions from the point of view of computer science, key among them being how to represent such games compactly, and how to efficiently compute solution concepts assuming such representations. Marginal contribution nets (MC‐nets), introduced by Ieong an...

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Published in:Mathematical logic quarterly Vol. 55; no. 4; pp. 362 - 376
Main Authors: Elkind, Edith, Goldberg, Leslie Ann, Goldberg, Paul W., Wooldridge, Michael
Format: Journal Article
Language:English
Published: Berlin WILEY-VCH Verlag 01-08-2009
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Abstract Coalitional games raise a number of important questions from the point of view of computer science, key among them being how to represent such games compactly, and how to efficiently compute solution concepts assuming such representations. Marginal contribution nets (MC‐nets), introduced by Ieong and Shoham, are one of the simplest and most influential representation schemes for coalitional games. MC‐nets are a rulebased formalism, in which rules take the form pattern → value, where “pattern ” is a Boolean condition over agents, and “value ” is a numeric value. Ieong and Shoham showed that, for a class of what we will call “basic” MC‐nets, where patterns are constrained to be a conjunction of literals, marginal contribution nets permit the easy computation of solution concepts such as the Shapley value. However, there are very natural classes of coalitional games that require an exponential number of such basic MC‐net rules. We present read‐once MC‐nets, a new class of MC‐nets that is provably more compact than basic MC‐nets, while retaining the attractive computational properties of basic MC‐nets. We show how the techniques we develop for read‐once MC‐nets can be applied to other domains, in particular, computing solution concepts in network flow games on series‐parallel networks (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
AbstractList Coalitional games raise a number of important questions from the point of view of computer science, key among them being how to represent such games compactly, and how to efficiently compute solution concepts assuming such representations. Marginal contribution nets (MC‐nets), introduced by Ieong and Shoham, are one of the simplest and most influential representation schemes for coalitional games. MC‐nets are a rulebased formalism, in which rules take the form pattern → value, where “pattern ” is a Boolean condition over agents, and “value ” is a numeric value. Ieong and Shoham showed that, for a class of what we will call “basic” MC‐nets, where patterns are constrained to be a conjunction of literals, marginal contribution nets permit the easy computation of solution concepts such as the Shapley value. However, there are very natural classes of coalitional games that require an exponential number of such basic MC‐net rules. We present read‐once MC‐nets, a new class of MC‐nets that is provably more compact than basic MC‐nets, while retaining the attractive computational properties of basic MC‐nets. We show how the techniques we develop for read‐once MC‐nets can be applied to other domains, in particular, computing solution concepts in network flow games on series‐parallel networks (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Coalitional games raise a number of important questions from the point of view of computer science, key among them being how to represent such games compactly, and how to efficiently compute solution concepts assuming such representations. Marginal contribution nets (MC‐nets), introduced by Ieong and Shoham, are one of the simplest and most influential representation schemes for coalitional games. MC‐nets are a rulebased formalism, in which rules take the form pattern → value , where “ pattern ” is a Boolean condition over agents, and “ value ” is a numeric value. Ieong and Shoham showed that, for a class of what we will call “basic” MC‐nets, where patterns are constrained to be a conjunction of literals, marginal contribution nets permit the easy computation of solution concepts such as the Shapley value. However, there are very natural classes of coalitional games that require an exponential number of such basic MC‐net rules. We present read‐once MC‐ nets , a new class of MC‐nets that is provably more compact than basic MC‐nets, while retaining the attractive computational properties of basic MC‐nets. We show how the techniques we develop for read‐once MC‐nets can be applied to other domains, in particular, computing solution concepts in network flow games on series‐parallel networks (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Author Elkind, Edith
Wooldridge, Michael
Goldberg, Paul W.
Goldberg, Leslie Ann
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Snippet Coalitional games raise a number of important questions from the point of view of computer science, key among them being how to represent such games compactly,...
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SubjectTerms Coalitional games
marginal contribution networks
read-once formulas
Shapley value
Title A Tractable and Expressive Class of Marginal Contribution Nets and Its Applications
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