Learning by doing and the value of optimal experimentation

Volker Wieland

No 96-5, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Research on learning-by-doing has typically been restricted to cases where estimation and control can be treated separately. Recent work has provided convergence results for more general learning problems where experimentation is an important aspect of optimal control. However the associated optimal policy cannot be derived analytically because Bayesian learning introduces a nonlinearity in the dynamic programming problem. This paper characterizes the optimal policy numerically and shows that it incorporates a substantial degree of experimentation. Dynamic simulations indicate that optimal experimentation dramatically improves the speed of learning, while separating control and estimation frequently induces a long-lasting bias in the control and target variables.

Keywords: Employees; Training of (search for similar items in EconPapers)
Date: 1996
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.federalreserve.gov/pubs/feds/1996/199605/199605abs.html (text/html)
http://www.federalreserve.gov/pubs/feds/1996/199605/199605pap.pdf (application/pdf)

Related works:
Journal Article: Learning by doing and the value of optimal experimentation (2000)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:96-5

Access Statistics for this paper

More papers in Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().