Competing R&D Strategies in an Evolutionary Industry Model

Murat Yildizoglu

Working Papers of BETA from Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg

Abstract: This article aims to test the relevance of learning through Genetic Algorithms, in opposition with fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These two R&D strategies are compared from the points of view of industry performance (welfare) and firms' relative performance (competitive edge): the results of simulations clearly show that learning is a source of technological and social efficiency as well as a mean for market domination.

Keywords: Learning; Innovation; Industry dynamics; Bounded rationality; Learning; Genetic algorithms (search for similar items in EconPapers)
Date: 1999
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Related works:
Journal Article: Competing R&D Strategies in an Evolutionary Industry Model (2002)
Working Paper: Competing R&D Strategies in an Evolutionary Industry Model (2002)
Working Paper: Competing R&D Strategies in an Evolutionary Industry Model (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:ulp:sbbeta:9914

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