Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/266961 
Year of Publication: 
2020
Citation: 
[Journal:] EconomiA [ISSN:] 1517-7580 [Volume:] 21 [Issue:] 2 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2020 [Pages:] 279-296
Publisher: 
Elsevier, Amsterdam
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.
Subjects: 
Agent-based modeling
Evolutionary model
Genetic algorithms
Industrial dynamics
JEL: 
L16
C63
C61
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.