How Learning Can Affect the Course of Evolution in Dynamic Environments

The Baldwin effect is known as interactions between learning and evolution, which suggests that individual lifetime learning can influence the course of evolution without the Lamarckian mechanism. Our concern is to consider the Baldwin effect in dynamic environments, especially when there is no explicit optimal solution through generations and it depends only on interactions among agents. We adopt the iterated Prisoner’s Dilemma as a dynamic environment and introduce phenotypic plasticity to strategies by using a meta-learning rule termed “Meta-Pavlov”. In this simulation, the Baldwin effect was observed as follows: First, strategies with enough plasticity spread, which caused a shift from defective population to cooperative. Second, these strategies were replaced by the strategy [x00x], which has a modest amount of plasticity.