Competing R&D Strategies in an Evolutionary Industry Model
This article aims to test the relevance of learning through genetic algorithms, in contrast to 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): simulations results clearly show that learning is a source of technological and social efficiency as well as a means for market domination. Copyright 2002 by Kluwer Academic Publishers
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Volume (Year): 19 (2002)
Issue (Month): 1 (February)
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"Technological diversity in an evolutionary industry model with localized learning and network externalities,"
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- GÃ©rard Ballot & Erol Taymaz, 1999. "Technological Change, Learning and Macro-Economic Coordination: an Evolutionary Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 2(2), pages 1-3.
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