Learning and Control: Optimal Decision-Making in a Changing Economic Environment
AbstractThis paper considers optimal decision-making in an environment with changing parameters. The decision maker's beliefs regarding these unknown, time-varying parameters are normal distributions and are updated according to Bayes rule. Optimal decisions involve a certain degree of experimentation. I approximate optimal policies and payoffs using numerical dynamic-programming methods and investigate how the incentive for experimentation varies with the extent of parameter uncertainty. In particular, I explore if this incentive is larger with time-varying parameters than with unknown but fixed parameters. A particular example of such a problem is optimal monetary policy when the slope of the short-run Phillips curve, or the interest-sensitivity of aggregate demand, are uncertain and vary over time. Preliminary findings suggest that the decision maker is willing repeatedly to undertake costly experiments. As a consequence he tolerates steady-state fluctuations. The incentive to experiment appears to be largest in situations where uncertainty is high but policy is approximately on target.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 743.
Date of creation: 01 Mar 1999
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