On learning dynamics underlying the evolution of learning rules.
In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.
Department of Ecology and Evolution, University of Lausanne, Switzerland.
Slimane
S
Dridi
Department of Ecology and Evolution, University of Lausanne, Switzerland. Electronic address: laurent.lehmann@unil.ch.
Laurent
L
Lehmann
D001499:Bayes Theorem; D005075:Biological Evolution; D006801:Humans; D007858:Learning; D011336:Probability; D013269:Stochastic Processes
Evolutionary game theory; Fictitious play; Fluctuating environments; Producer–scrounger game; Reinforcement learning; Stochastic approximation