Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)

This article aims to test the relevance of learning through Genetic Algorithms (GA) and Learning Classifier Systems (LCS), in opposition with fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These three R&D strategies are compared from the points of view of industry performance (welfare): the results of simulations clearly show that learning is a source of technological and social efficiency.

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Year of publication:	2001
Authors:	Yildizoglu, Murat
Institutions:	Groupe de Recherche en Économie Théorique et Appliquée (GREThA), Université de Bordeaux
Subject:	Learning \| Learning Classifier Systems \| Bounded Rationality \| Technical Progress \| Innovation

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Extent:	application/pdf
Series:	Working Papers.
Type of publication:	Book / Working Paper
Classification:	O3 - Technological Change; Research and Development ; L1 - Market Structure, Firm Strategy, and Market Performance ; D83 - Search, Learning, Information and Knowledge
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005729424