Stochastic Processes and Mean Field Systems Defined by Nonlinear Markov Chains: An Illustration for a Model of Evolutionary Population Dynamics



Título del documento: Stochastic Processes and Mean Field Systems Defined by Nonlinear Markov Chains: An Illustration for a Model of Evolutionary Population Dynamics
Revista: Brazilian journal of physics
Base de datos: PERIÓDICA
Número de sistema: 000397788
ISSN: 0103-9733
Autors: 1
Institucions: 1University of Connecticut, Department of Psychology, Storrs, Connecticut. Estados Unidos de América
Any:
Període: Sep
Volum: 41
Número: 2-3
Paginació: 129-134
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico
Resumen en inglés In physics, there is a growing interest in studying stochastic processes described by evolution equations such as nonlinear master equations and nonlinear Fokker–Planck equations that define the so-called nonlinear Markov processes and are nonlinear with respect to probability densities. In this context, however, relatively little is known about nonlinear Markov processes defined by nonlinear Markov chains. In the present work, we demonstrate explicitly how the nonlinear Markov chain approach can be carried out by addressing a model for evolutionary population dynamics. In line with the nonlinear Markov chain approach, we derive a measure that tells us how attractive it is for a biological entity to evolve towards a particular biological type. Likewise, a measure for the noise level of the evolutionary process is obtained. Both measures are found to be implicitly time dependent. Finally, a simulation scheme for the many-body system corresponding to the Markov chain model is discussed
Disciplines Física y astronomía,
Matemáticas
Paraules clau: Física,
Matemáticas aplicadas,
Procesos estocásticos,
Procesos no lineales,
Procesos de Markov,
Ecuaciones de evolución,
Cadenas de Markov,
Dinámica de poblaciones
Keyword: Physics and astronomy,
Mathematics,
Physics,
Applied mathematics,
Stochastic processes,
Nonlinear processes,
Markov processes,
Evolution equations,
Markov chains,
Population dynamics
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