Linear Learning in Changing Environments

30 Pages Posted: 29 Oct 2004

See all articles by Tilman Klumpp

Tilman Klumpp

University of Alberta, Department of Economics

Date Written: March 2005

Abstract

The decision maker receives signals imperfectly correlated with an unobservable state variable and must take actions whose payoffs depend on the state. The state randomly changes over time. In this environment, we examine the performance of simple linear updating rules relative to Bayesian learning. We show that a range of parameters exists for which linear learning results in exactly the same decisions as Bayesian learning, although not in the same beliefs. Outside this parameter range, we use simulations to demonstrate that the consumption level attainable under the optimal linear rule is virtually indistinguishable from the one attainable under Bayes' rule, although the respective decisions will not always be identical. These results suggest that simple rules of thumb can have an advantage over Bayesian updating when more complex calculations are more costly to perform than less complex ones. We demonstrate the implications of such an advantage in an evolutionary model where agents learn to learn.

Keywords: Learning, changing environments, bounded rationality, exponential smoothing

JEL Classification: D81, D83

Suggested Citation

Klumpp, Tilman, Linear Learning in Changing Environments (March 2005). Available at SSRN: https://ssrn.com/abstract=611023 or http://dx.doi.org/10.2139/ssrn.611023

Tilman Klumpp (Contact Author)

University of Alberta, Department of Economics ( email )

Edmonton, Alberta T6G 2R3
Canada