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Communication and Common Interest

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
Communication and Common Interest
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003282
Pubmed ID
Authors

Peter Godfrey-Smith, Manolo Martínez

Abstract

Explaining the maintenance of communicative behavior in the face of incentives to deceive, conceal information, or exaggerate is an important problem in behavioral biology. When the interests of agents diverge, some form of signal cost is often seen as essential to maintaining honesty. Here, novel computational methods are used to investigate the role of common interest between the sender and receiver of messages in maintaining cost-free informative signaling in a signaling game. Two measures of common interest are defined. These quantify the divergence between sender and receiver in their preference orderings over acts the receiver might perform in each state of the world. Sampling from a large space of signaling games finds that informative signaling is possible at equilibrium with zero common interest in both senses. Games of this kind are rare, however, and the proportion of games that include at least one equilibrium in which informative signals are used increases monotonically with common interest. Common interest as a predictor of informative signaling also interacts with the extent to which agents' preferences vary with the state of the world. Our findings provide a quantitative description of the relation between common interest and informative signaling, employing exact measures of common interest, information use, and contingency of payoff under environmental variation that may be applied to a wide range of models and empirical systems.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 2 4%
United Kingdom 1 2%
Germany 1 2%
Belgium 1 2%
New Zealand 1 2%
Unknown 42 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 23%
Researcher 8 17%
Student > Master 7 15%
Professor 5 10%
Student > Bachelor 2 4%
Other 8 17%
Unknown 7 15%
Readers by discipline Count As %
Philosophy 5 10%
Psychology 5 10%
Computer Science 5 10%
Agricultural and Biological Sciences 5 10%
Social Sciences 4 8%
Other 15 31%
Unknown 9 19%