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

Murat Yildizoglu ()

Working Papers from Equipe Industries Innovation Institutions, Université Bordeaux IV, France

Abstract: 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.

Keywords: Learning; Learning Classifier Systems; Bounded Rationality; Technical Progress; Innovation (search for similar items in EconPapers)
JEL-codes: O3 L1 D83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-evo, nep-ino and nep-tid
Date: 2001

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Persistent link: http://EconPapers.repec.org/RePEc:iii:wpeiii:2001-1

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