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

Evolutionary instability of selfish learning in repeated games