Can natural selection encode Bayesian priors?

We present an evolutionary model to explore how accurately Bayesian prior estimates can be encoded genetically and shaped by natural selection when decision-makers learn from uncertain information. The model simulates the evolution of a population of individuals that are required to estimate the probability of an event. Every individual has a prior estimate of this probability and collects noisy cues from the environment in order to update its prior belief to a Bayesian posterior estimate with the evidence gained. The prior is inherited and passed on to offspring. Fitness increases with the accuracy of the posterior estimates produced. Simulations show that prior estimates become accurate over evolutionary time. In addition to these 'Bayesian' individuals, we also introduce 'frequentist' individuals that do not use a prior and instead use frequentist inference when estimating the probability. Competition between the two shows that the former tend to have an evolutionary advantage over the latter, as predicted by the literature, and that this advantage is lowest when the information available to individuals poses the least uncertainty.