Add to ORKGWe investigate the behavior of two learning rules, Stochastic Best Response (SBR) and Replicator Dynamics, in a model with aggregate and time-correlated shocks to payoffs. The main difference between the two behavior of the two rules is that under SBR corners are not absorbing. We study a setting where there are two actions and many states of nature and the transition between states follows a Markov chain. We find that the SBR converges to a behavior similar to probability matching. On the other hand, the Replicator Dynamics selects the optimal action only if the average payoff of both actions is different enough. JEL Classification Number: C73
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