Yildizoglu, Murat (2001) Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982). [Preprint]
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Abstract
This article aims to test the relevance of learning through Genetic Algorithms (GA) and Learning Classifier Systems (LCS), in opposition with fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These three R&D strategies are compared from the points of view of industry performance (welfare): the results of simulations clearly show that learning is a source of technological and social efficiency.
Item Type: | Preprint |
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Additional Information: | JEL Classification: O3, L1, D83 |
Keywords: | Learning, Learning Classifier Systems, Bounded Rationality, Technical Progress, Innovation |
Subjects: | Computer Science > Machine Learning Psychology > Social Psychology > Social simulation |
ID Code: | 3864 |
Deposited By: | Yildizoglu, Prof. Murat |
Deposited On: | 08 Oct 2004 |
Last Modified: | 11 Mar 2011 08:55 |
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