Mixed updating in structured populations

TL;DR

This paper investigates how mixed updating rules in evolutionary graph theory affect fixation probabilities and times, providing formulas and algorithms for various structures and highlighting their sensitivities.

Contribution

It introduces a study of mixed update rules combining death-Birth and Birth-death processes, offering new formulas, algorithms, and sensitivity classifications for fixation dynamics.

Findings

Fixation probabilities and times can vary non-monotonically with mixing probability.

Most unweighted undirected graphs have short fixation times.

Exact formulas are derived for cycles, stars, and complex structures.

Abstract

Evolutionary graph theory (EGT) studies the effect of population structure on evolutionary dynamics. The vertices of the graph represent the $N$ individuals. The edges denote interactions for competitive replacement. Two standard update rules are death-Birth (dB) and Birth-death (Bd). Under dB, an individual is chosen uniformly at random to die, and its neighbors compete to fill the vacancy proportional to their fitness. Under Bd, an individual is chosen for reproduction proportional to fitness, and its offspring replaces a randomly chosen neighbor. Here we study mixed updating between those two scenarios. In each time step, with probability $\delta$ the update is dB and with remaining probability it is Bd. We study fixation probabilities and times as functions of $\delta$ under neutral evolution and constant selection. Despite the fact that fixation probabilities and times can be increasing, decreasing, or non-monotonic in $\delta$, we prove nearly all unweighted undirected graphs have short fixation times and provide an efficient algorithm to estimate their fixation probabilities. Finally, we prove exact formulas for fixation probabilities on cycles, stars, and more complex structures and classify their sensitivities to $\delta$.