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Title: Genetic algorithms applied to an evolutionary model of industrial dynamics.
Authors: Passos, Gustavo de Castro Silva Versiani
Barrenechea, Martin Harry Vargas
Keywords: Agent-based modeling
Issue Date: 2020
Citation: PASSOS, G. C. S.; BARRENECHEA, M. H. Genetic algorithms applied to an evolutionary model of industrial dynamics. Revista Economia da ANPEC, v. 21, p. 279-296, 2020. Disponível em: <>. Acesso em: 24 maio 2021.
Abstract: In order to verify the effects of machine learning in a market structure, an evolutionary model containing firms that use a genetic algorithm to decide their investment in innovative R&D was developed. These firms share the market, with two other types of firms, those with a fixed rate of investment and those with random strategies. A model of industrial dynamics was implemented and simulated using several population distributions of the three types of firms. The availability of external credit and the length of learning periods were evaluated and their effects, in the market structure, analysed. The simulations results brought contrasting findings when compared to previous works, as it confirmed that machine learning led to market dominance, but the same did not occur when considering the improvement of technological efficiency and social welfare.
ISSN: 1517-7580
metadata.dc.rights.license: This is an open access article under the CC BY-NC-ND license ( Fonte: o próprio PDF.
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