Learning to Believe in Sunspots

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Published in RePEc

Abstract

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 learning rule studied is of the "stochastic approximation" form studied by H. Robbins and S. 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. Copyright 1990 by The Econometric Society. (This abstract was borrowed from another version of this item.)

Keywords

STOCHASTIC
MODEL
COPYRIGHT
ADAPTIVE
EQUILIBRIA
ECONOMY
BELIEVE
SUNSPOT
LEARNING RULE
ITEM

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