This 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. 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.