On the stochastic evolution of finite populations

Abstract

This work is a systematic study of discrete Markov chains that are used to describe the evolution of a two-types population. Motivated by results valid for the well-known Moran (M) and Wright-Fisher (WF) processes, we define a general class of Markov chains models which we term the Kimura class. It comprises the majority of the models used in population genetics, and we show that many well-known results valid for M and WF processes are still valid in this class. In all Kimura processes, a mutant gene will either fixate or become extinct, and we present a necessary and sufficient condition for such processes to have the probability of fixation strictly increasing in the initial frequency of mutants. This condition implies that there are WF processes with decreasing fixation probability-in contradistinction to M processes which always have strictly increasing fixation probability. As a by-product, we show that an increasing fixation probability defines uniquely an M or WF process which realises it, and that any fixation probability with no state having trivial fixation can be realised by at least some WF process. These results are extended to a subclass of processes that are suitable for describing time-inhomogeneous dynamics. We also discuss the traditional identification of frequency dependent fitnesses and pay-offs, extensively used in evolutionary game theory, the role of weak selection when the population is finite, and the relations between jumps in evolutionary processes and frequency dependent fitnesses.

Fitness potentials and qualitative properties of the Wright-Fisher dynamics

“…If there are no-mutations, the Markov chain in reducible, the leading eigenvalue is doubly-degenerated and the quasi-stationary distribution is given by the eigenvector associated to the sub-leading eigenvalue. The fixation probability is a leading eigenvector of the backward evolution [5]. On the other hand, if mutations are introduced, even small ones, the associated Markov chain is irreducible, the leading eigenvalue is simple, the fixation probability vector is not defined and the stationary distribution is the unique (up to normalization) leading eigenvector.…”

Computation and Simulation of Evolutionary Game Dynamics in Finite Populations

“…Continuous-time processes require different simulation techniques (e.g. based on the Gillespie algorithm 18 ) that are beyond our scope, see 19 for a systematic comparison of these processes.…”

Moran process and Wright-Fisher process favor low variability

“…The Moran process applies to populations with asynchronous reproduction and overlapping generations. It has been extensively studied recently, see for example [3,30,16,18,5]. In each step, one individual is chosen for reproduction with probability proportional to its fitness.…”