Lenient Learning in a Multiplayer Stag Hunt

This paper describes the learning dynamics of individual learners in a multiplayer Stag Hunt game, focussing primarily on the difference between lenient and non-lenient learning. We find that, as in 2-player games, leniency significantly promotes cooperative outcomes in 3-player games, as the basins of attraction of (partially) cooperative equilibria grow under this learning scheme. Moreover, we observe significant differences between purely selection-based models, as often encountered in related analytical research, and models that include mutation. Therefore, purely selection-based analysis might not always accurately predict the behavior of practical learning algorithms, which often include mutation.