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A solution Method for a Class of Learning by Doing Models

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  • Francisco Alvarez Gonzalez
  • Emilio Cerda Tena

    () (Departamento de Analisis Economico, Universidad Complutense Madrid)

Abstract

A phenomenon widely observed in industries which are in an early stage is that they reduce their costs as a result of accumulating experience, that is, they reduce their costs with their output. This is known in the economic literature as the learning by doing effect, and it was studied for the first time by Arrow in 1962. There are a lot of references about the relationship between the structure of the industry and the learning by doing effect. In general, the results available in the literature give properties of the optimal policy, but they do not find the optimal policy in a closed-form. In this paper we present a solution method that obtains the closed-form optimal policy for a class of learning by doing models. We study a model with a single agent, monopolist, without possible competition. The demand function is linear. The problem is deterministic, dynamic, with a finite time horizon and it is formulated in discrete time.

Suggested Citation

  • Francisco Alvarez Gonzalez & Emilio Cerda Tena, "undated". "A solution Method for a Class of Learning by Doing Models," Computing in Economics and Finance 1996 _002, Society for Computational Economics.
  • Handle: RePEc:sce:scecf6:_002
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    File URL: http://www.unige.ch/ce/ce96/ps/alvarez.eps
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    References listed on IDEAS

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    1. Dasgupta, Partha & Stiglitz, Joseph E, 1988. "Learning-by-Doing, Market Structure and Industrial and Trade Policies," Oxford Economic Papers, Oxford University Press, vol. 40(2), pages 246-268, June.
    2. Parente Stephen L., 1994. "Technology Adoption, Learning-by-Doing, and Economic Growth," Journal of Economic Theory, Elsevier, vol. 63(2), pages 346-369, August.
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    Cited by:

    1. Francisco Álvarez & Emilio Cerdá, 1999. "Analytical solution for a class of learning by doing models with multiplicative uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(1), pages 1-23, June.

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