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International Journal of Game Theory
Article . 1999 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
zbMATH Open
Article
Data sources: zbMATH Open
DBLP
Article . 2017
Data sources: DBLP
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Approximate common knowledge revisited

Authors: Stephen Morris;

Approximate common knowledge revisited

Abstract

The paper analyzes, in the context of two person games, alternative notions of approximate common knowledge. One individual `\(p\)-believes' the event \(E\) if he assigns it a probability at least \(p\). \(E\) is `common \(p\)-belief' if both individuals \(p\)-believe \(E\), both \(p\)-believe that both \(p\)-believe \(E\) etc. \(E\) is `iterated \(p\)-belief' for individual 1 if 1 \(p\)-believes \(E\),1 \(p\)-believes that 2 \(p\)-believes \(E\), 1 \(p\)-believes that 2 \(p\)-believes that 1 \(p\)-believes \(E\) etc. \(E\) is iterated \(p\)-belief if \(E\) is iterated \(p\)-belief for both individuals. Another definition of approximate common knowledge explored is that of `weak common \(p\)-belief'. \(E\) is weak common \(p\)-belief if it is common \(p\)-belief either given individuals' actual information or if individuals ignore some of their information. The paper shows that the nature of approximate common knowledge is sensitive to the exact definition of common knowledge in such a framework where `knowledge' is replaced by lief with high probability. When \(p =1\), all three concepts are equivalent to each other as well as to the standard definitions of common knowledge, however, it is shown that when \(p< 1\) they are not equivalent but common \(p\)-belief implies iterated \(p\)-belief and weak common \(p\)-belief. Moreover, it is shown that the conjecture that for any \(p< 1\), there should exist some \(q\) sufficiently close to 1 such that if \(E\) is iterated \(q\)-belief then it must be comeon \(p\)-belief' is false. The paper also provides results relating each of the alternative definitions of approximate common knowledge to a class of applications. It is known that common \(p\)-belief is the natural notion of approximate common knowledge when studying the robustness of equilibria to a lack of common knowledge of payoffs. It is shown in this paper that iterated \(p\)-belief is the relevant notion for the study of best response dynamics in incomplete information games while weak common \(p\)-belief is the relevant notion for aggreeing to disagree/no trade type results. A striking feature of common \(p\)-belief is that improved information may reduce the degree of common \(p\)-belief. More information is not necessarily better for achieving coordination in strategic environments because common \(p\)-belief is what matters in achieving coordination in strategic environments.

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Keywords

Common knowledge · agreeing to disagree, common knowledge, Games in extensive form, agreeing to desagree

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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!
12
Average
Top 10%
Average
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