Satisficing Learning Dynamics in the Stag Hunt

Satisficing game theory is an alternative to traditional game theory that allows players´ utilities to he conditioned upon the preferences of others. This construction, however, requires that players have accurate information about each other´s preferences. We discuss a learning mechanism for satisficing players to estimate other´s preferences through repeated interactions. We apply this mechanism to the Stag Hunt game, and show that under most circumstances, players can indeed learn players´ preferences sufficiently to make correct decisions