We propose two new signalling-game refinements that are microfounded in a model of patient Bayesian learning. Agents are born into player roles and play the signalling game against a random opponent each period. Inexperienced agents know their opponents' payoff functions but not the prevailing distribution of opponents' play. One refinement corresponds to an upper bound on the set of possible learning outcomes while the other provides a lower bound. Both refinements are closely related to divine equilibrium (Banks and Sobel, 1987).
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