An extension of the Moran process using type-specific connection graphs

TL;DR

This paper extends the Moran process by modeling mutants and residents with different perception graphs, analyzing fixation probabilities, and exploring strategic interactions and approximation methods.

Contribution

It introduces a two-graph model for the Moran process, generalizes key theorems, and studies strategic and computational aspects of fixation probabilities.

Findings

Generalization of the Isothermal Theorem for two-graph models

Identification of the clique as a beneficial graph in strategic settings

Development of a FPRAS for fixation probability in complete mutant graphs

Abstract

The Moran process, as studied by [Lieberman, E., Hauert, C. and Nowak, M. Evolutionary dynamics on graphs. Nature 433, pp. 312-316 (2005)], is a stochastic process modeling the spread of genetic mutations in populations. In this process, agents of a two-type population (i.e. mutants and residents) are associated with the vertices of a graph. Initially, only one vertex chosen uniformly at random is a mutant, with fitness $r > 0$, while all other individuals are residents, with fitness $1$. In every step, an individual is chosen with probability proportional to its fitness, and its state (mutant or resident) is passed on to a neighbor which is chosen uniformly at random. In this paper, we introduce and study a generalization of the model of Lieberman et al. by assuming that different types of individuals perceive the population through different graphs, namely $G_R(V,E_R)$ for residents and $G_M(V,E_M)$ for mutants. In this model, we study the fixation probability, i.e. the probability that eventually only mutants remain in the population, for various pairs of graphs. First, we transfer known results from the original single-graph model of Lieberman et al. to our 2-graph model. Among them, we provide a generalization of the Isothermal Theorem of Lieberman et al., that gives sufficient conditions for a pair of graphs to have the same fixation probability as a pair of cliques. Next, we give a 2-player strategic game view of the process where player payoffs correspond to fixation and/or extinction probabilities. In this setting, we attempt to identify best responses for each player and give evidence that the clique is the most beneficial graph for both players. Finally, we examine the possibility of efficient approximation of the fixation probability and provide a FPRAS for the special case where the mutant graph is complete.