$K$-Branching Random Walk with Noisy Selection: Large Population Limits and Phase Transitions

Abstract: We analyze a variant of the Noisy $K$-Branching Random Walk, a population model that evolves according to the following procedure. At each time step, each individual produces a large number of offspring that inherit the fitness of their parents up to independent and identically distributed fluctuations. The next generation consists of a random sample of all the offspring so that the population size remains fixed, where the sampling is made according to a parameterized Gibbs measure of the fitness of the offspring. Our model interpolates between classical models of fitness waves and exhibits a novel phase transition in the propagation of the wave. By employing a stochastic Hopf-Cole transformation, we show that as we increase the population size, the random dynamics of the model can be described by deterministic operations acting on the limiting population densities. We then show that for fitness fluctuations with exponential tails, these operations admit a unique traveling wave solution with local stability. The traveling wave solution undergoes a phase transition when changing selection pressure, revealing a complex interaction between evolution and natural selection.