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Journal of Economic Theory
Article . 2016 . Peer-reviewed
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Graphical potential games

Authors: Yakov Babichenko; Omer Tamuz;

Graphical potential games

Abstract

We study the class of potential games that are also graphical games with respect to a given graph $G$ of connections between the players. We show that, up to strategic equivalence, this class of games can be identified with the set of Markov random fields on $G$. From this characterization, and from the Hammersley-Clifford theorem, it follows that the potentials of such games can be decomposed to local potentials. We use this decomposition to strongly bound the number of strategy changes of a single player along a better response path. This result extends to generalized graphical potential games, which are played on infinite graphs.

Accepted to the Journal of Economic Theory

Country
United States
Related Organizations
Keywords

FOS: Computer and information sciences, 330, potential games, Probability (math.PR), 004, Noncooperative games, FOS: Economics and business, Potential games, graphical games, Computer Science - Computer Science and Game Theory, Economics - Theoretical Economics, FOS: Mathematics, Theoretical Economics (econ.TH), Graphical games, Random fields, Games involving graphs, Mathematics - Probability, Computer Science and Game Theory (cs.GT)

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Average
Average
Green
bronze