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Some dynamics of signaling games

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Date
2014
Dewey
Probabilités et mathématiques appliquées
Sujet
costly signaling; replicator dynamics; Moran process
JEL code
C.C7.C73
Journal issue
Proceedings of the National Academy of Sciences of the United States of America
Volume
111
Publication date
2014
Article pages
10873-10880
Publisher
National Academy of sciences
DOI
http://dx.doi.org/10.1073/pnas.1400838111
URI
https://basepub.dauphine.fr/handle/123456789/15312
Collections
  • CEREMADE : Publications
Metadata
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Author
Huttegger, Simon
17255 University of California
Skyrms, Brian
17255 University of California
Tarres, Pierre
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Wagner, Elliott
status unknown
Type
Article accepté pour publication ou publié
Abstract (EN)
Information transfer is a basic feature of life that includes signaling within and between organisms. Owing to its interactive nature, signaling can be investigated by using game theory. Game theoretic models of signaling have a long tradition in biology, economics, and philosophy. For a long time the analyses of these games has mostly relied on using static equilibrium concepts such as Pareto optimal Nash equilibria or evolutionarily stable strategies. More recently signaling games of various types have been investigated with the help of game dynamics, which includes dynamical models of evolution and individual learning. A dynamical analysis leads to more nuanced conclusions as to the outcomes of signaling interactions. Here we explore different kinds of signaling games that range from interactions without conflicts of interest between the players to interactions where their interests are seriously misaligned. We consider these games within the context of evolutionary dynamics (both infinite and finite population models) and learning dynamics (reinforcement learning). Some results are specific features of a particular dynamical model, whereas others turn out to be quite robust across different models. This suggests that there are certain qualitative aspects that are common to many real-world signaling interactions.

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