Learning, evolution and population dynamics

TL;DR

This paper investigates how individual learning and population dynamics influence equilibrium outcomes in a symmetric complementarity game where players adapt strategies over repeated encounters with different opponents.

Contribution

It introduces a systematic framework to compare strategy and learning schemes and analyzes how population-level effects determine equilibrium selection in a symmetric game.

Findings

Simpler strategies tend to reach favorable equilibria faster.

Bolder populations can gain advantages despite lower individual performance.

Parameters like mutation rate affect the balance between learning and evolution.

Abstract

We study a complementarity game as a systematic tool for the investigation of the interplay between individual optimization and population effects and for the comparison of different strategy and learning schemes. The game randomly pairs players from opposite populations. The game is symmetric at the individual level, but has many equilibria that are more or less favorable to the members of the two populations. Which of these equilibria then is attained is decided by the dynamics at the population level. Players play repeatedly, but in each round with a new opponent. They can learn from their previous encounters and translate this into their actions in the present round on the basis of strategic schemes. The schemes can be quite simple, or very elaborate. We can then break the symmetry in the game and give the members of the two populations access to different strategy spaces. Typically, simpler strategy types have an advantage because they tend to go more quickly towards a favorable equilibrium which, once reached, the other population is forced to accept. Also, populations with bolder individuals that may not fare so well at the level of individual performance may obtain an advantage towards ones with more timid players. By checking the effects of parameters such as the generation length or the mutation rate, we are able to compare the relative contributions of individual learning and evolutionary adaptations.