Controlling a Stochastic Process with Unknown Parameters
研究在无限时域、贴现条件下，如何控制一个参数未知的随机过程。代理人通过分布表达对未知参数的信念，信念序列收敛到极限分布，但极限分布不一定集中在真实参数上。文章探讨了完全学习与不完全学习的最优性，并给出特例和生成不完全学习最优例子的方法。
The problem of controlling a stochastic process, with unknown parameters over an infinite horizon, with discounting is considered. Agents express beliefs about unknown parameters in terms of distributions. Under general conditions, the sequence of beliefs converges to a limit distribution. The limit distribution may or may not be concentrated at the true parameter value. In some cases, complete learning is optimal; in others, the optimal strategy does not imply complete learning. The paper concludes with examination of some special cases and a discussion of a procedure for generating examples in which incomplete learning is optimal. Copyright 1988 by The Econometric Society.