Abstract
Macroeconomics has just passed through a period in which it was assumed that everyone knew everything. Now hopefully we are moving into a period where those assumptions will be replaced with the more realistic ones that different actors have different information and learn in different ways. One approach to implementing these kinds of assumptions is available from control theory.
This paper discusses the learning procedures that are used in a variety of control theory methods. These methods begin with deterministic control with and without state variable and parameter updating. They also included two kinds of stochastic control: passive and active. With passive learning, stochastic control variables are chosen while considering the uncertainty in parameter estimates, but no attention is paid to the potential impact of today’s control variables on future learning. By contrast, active learning control seeks a balance between reaching today’s goals and gaining information that makes it easier to reach tomorrow’s goals.
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Kendrick, D. (1994). Computational Approaches to Learning with Control Theory. In: Belsley, D.A. (eds) Computational Techniques for Econometrics and Economic Analysis. Advances in Computational Economics, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8372-5_5
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DOI: https://doi.org/10.1007/978-94-015-8372-5_5
Publisher Name: Springer, Dordrecht
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