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We present a model of monetary policy where the policymaker faces uncertainty about which he is learning in a Bayesian fashion. A fixed money supply rule is not optimal since the learning leads to adjustments in the monetary action. We present cases in which it is optimal to bear some cost in terms of current output performance in order to gain information that can be used in the formulation of future monetary policy: experimentation therefore pays. We also show that even passive learning without experimentation still leads to an activist monetary policy, i.e., one that is responsive to new information.