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Compound Learning, Neural Nets And The Competitive Process

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  • Mario Calderini
  • Stan Metcalfe

Abstract

In this paper we try to assess the potential application of neural networks as a modelling tool for complex evolutionary processes. The concept that we wish to investigate is the one of compound learning, that is the fact that, in a complex environment, what and how much economic entities learn depends upon what has been learnt in other entities in an interactive fashion. Our application consists of a stylised environment in which two firms learn how to innovate their product and to sell it on a market which learns how to evaluate the product which is being supplied. We seek to demonstrate that what matters for competitive advantage is not the absolute value of learning capability but the differential learning capability between the competing firms and between the firms and the market. Another appealing way to see it is that the chance for one of the two firms to gain competitive advantage is not unlimited but is constrained by own learning capability and the learning capability of the market.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Economics of Innovation and New Technology.

Volume (Year): 7 (1998)
Issue (Month): 4 ()
Pages: 271-302

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Handle: RePEc:taf:ecinnt:v:7:y:1998:i:4:p:271-302

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Related research

Keywords: Learning; Competition; Neural networks; JEL Classification: L20; O30;

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Cited by:
  1. Husted, Kenneth, 1999. "Between Autonomy and Control: The role of industrial researchers’ decision-making," Working Papers 11/1999, Copenhagen Business School, Department of Management, Politics & Philosophy.

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