Evolutionary instability of selfish learning in repeated games

TL;DR

This paper investigates the stability of selfish learning in repeated games, showing it is generally unstable and that evolution favors social preferences, supported by simulations, analysis, and experimental data.

Contribution

It demonstrates the evolutionary instability of selfish learning in repeated games and highlights the emergence of social preferences through analytical and experimental methods.

Findings

Selfish learning is unstable in most classical repeated games.

Evolution favors social (other-regarding) preferences over selfish learning.

Experimental data shows selfish learning cannot explain human behavior in fairness trade-offs.

Abstract

Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one's own success. However, when two such "selfish" learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner's dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.