Convergence of generalized urn models to non-equilibrium attractors

Autor: Mathieu Faure, Sebastian J. Schreiber
Přispěvatelé: Groupement de Recherche en Économie Quantitative d'Aix-Marseille (GREQAM), École Centrale de Marseille (ECM)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), École des hautes études en sciences sociales (EHESS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Zdroj: Stochastic Processes and their Applications
Stochastic Processes and their Applications, Elsevier, 2015, 125 (8), pp.3053--3074
Stochastic Processes and their Applications, 2015, 125 (8), pp.3053--3074. ⟨10.1016/j.spa.2015.02.011⟩
ISSN: 0304-4149
1879-209X
Popis: Generalized Polya urn models have been used to model the establishment dynamics of a small founding population consisting of k different genotypes or strategies. As population sizes get large, these population processes are well-approximated by a mean limit ordinary differential equation whose state space is the k simplex. We prove that if this mean limit ODE has an attractor at which the temporal averages of the population growth rate is positive, then there is a positive probability of the population not going extinct (i.e. growing without bound) and its distribution converging to the attractor. Conversely, when the temporal averages of the population growth rate is negative along this attractor, the population distribution does not converge to the attractor. For the stochastic analog of the replicator equations which can exhibit non-equilibrium dynamics, we show that verifying the conditions for convergence and non-convergence reduces to a simple algebraic problem. We also apply these results to selection-mutation dynamics to illustrate convergence to periodic solutions of these population genetics models with positive probability.
29 pages, 2 figures
Databáze: OpenAIRE