Learning to Believe in Sunspots
展示了一种适应性学习规则，使经济可能收敛到太阳黑子均衡，即使代理人最初不相信不同太阳黑子状态的结果有显著差异。
An adaptive learning rule is exhibited for the Azariadis (1981) overlapping generations model of a monetary economy with multiple equilibria, under which the economy may converge to a stationary sunspot equilibrium, even if agents do not initially believe that outcomes are significantly different in different "sunspot" states. The type of learning rule studied is of the "stochastic approximation" form studied by Robbins and Monro (1951); methods for analyzing the convergence of this form of algorithm are presented that may be of use in many other contexts as well. Conditions are given under which convergence to a sunspot equilibrium occurs with probability one.