Permanent link to this recordhttp://hdl.handle.net/10754/550665
MetadataShow full item record
AbstractWe study an atomic signaling game under stochastic evolutionary dynamics. There are a finite number of players who repeatedly update from a finite number of available languages/signaling strategies. Players imitate the most fit agents with high probability or mutate with low probability. We analyze the long-run distribution of states and show that, for sufficiently small mutation probability, its support is limited to efficient communication systems. We find that this behavior is insensitive to the particular choice of evolutionary dynamic, a property that is due to the game having a potential structure with a potential function corresponding to average fitness. Consequently, the model supports conclusions similar to those found in the literature on language competition. That is, we show that efficient languages eventually predominate the society while reproducing the empirical phenomenon of linguistic drift. The emergence of efficiency in the atomic case can be contrasted with results for non-atomic signaling games that establish the non-negligible possibility of convergence, under replicator dynamics, to states of unbounded efficiency loss.
CitationDynamics in atomic signaling games 2015, 376:82 Journal of Theoretical Biology
JournalJournal of Theoretical Biology
- Stochastic evolutionary dynamics of bimatrix games.
- Authors: Ohtsuki H
- Issue date: 2010 May 7
- Unpredictability induced by unfocused games in evolutionary game dynamics.
- Authors: Hashimoto K
- Issue date: 2006 Aug 7
- Equilibrium selection in evolutionary games with random matching of players.
- Authors: Miekisz J
- Issue date: 2005 Jan 7
- Fixation probabilities in evolutionary games with the Moran and Fermi processes.
- Authors: Liu X, Pan Q, Kang Y, He M
- Issue date: 2015 Jan 7
- Evolutionary game dynamics in finite populations with strong selection and weak mutation.
- Authors: Fudenberg D, Nowak MA, Taylor C, Imhof LA
- Issue date: 2006 Nov