Competing R&D Strategies in an Evolutionary Industry Model

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.Learning,Innovation, Industry dynamics, Bounded rationality, Learning, Genetic algorithms