Equilibrium selection in the stag hunt game under generalized reinforcement learning

- Generalized reinforcement dynamic allows equilibrium selection in the stag hunt.
- It selects the Pareto superior equilibrium under two conditions.
- The payoff at the inferior equilibrium should be negative.
- The payoff at the superior equilibrium should be high and positive.
- Conditions for selection of the inferior equilibrium are also presented.

We apply the generalized reinforcement (GR) learning protocol to the stag hunt game. GR learning combines positive and negative reinforcement. The GR learning rule generates the GR dynamic, which governs the evolution of the mixed strategy of agents in the population. We identify conditions under which the GR dynamic converges globally to one of the two pure strategy Nash equilibria of the game.