Simulating how animals learn: a new modelling framework applied to the process of optimal foraging

TL;DR

This paper introduces a novel Bayesian MCMC-based simulation framework to model animal learning, demonstrating how animals adapt their foraging strategies in uncertain environments, aligning with optimal foraging theories.

Contribution

The paper presents a new computational framework using Bayesian MCMC sampling to simulate animal learning and decision-making in complex, changing environments.

Findings

Simulated animals learned strategies that maximize foraging success.

Behavioral plasticity improves foraging efficiency in unpredictable environments.

Animals tend to prioritize concentrated resources, consistent with optimal foraging theory.

Abstract

Animal learning has interested ecologists and psychologists for over a century. Mathematical models that explain how animals store and recall information have gained attention recently. Central to this work is statistical decision theory (SDT), which relates information uptake in animals to Bayesian inference. SDT effectively explains many learning tasks in animals, but extending this theory to predict how animals will learn in changing environments still poses a challenge for ecologists. We addressed this shortcoming with a novel implementation of Bayesian Markov Chain Monte Carlo (MCMC) sampling to simulate how animals sample environmental information and learn as a result. We applied our framework to an individual-based model simulating complex foraging tasks encountered by wild animals. Simulated ``animals" learned behavioral strategies that optimized foraging returns simply by following the principles of an MCMC sampler. In these simulations, behavioral plasticity was most conducive to efficient foraging in unpredictable and uncertain environments. Our model suggests that animals prioritize highly concentrated resources even when these resources are less available overall, in line with existing knowledge on optimal foraging and ideal free distribution theory. Our innovative computational modelling framework can be applied more widely to simulate the learning of many other tasks in animals and humans.