creators_name: Yildizoglu, Murat
creators_id: yildi
type: preprint
datestamp: 2004-10-08
lastmod: 2011-03-11 08:55:42
metadata_visibility: show
title: Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)
subjects: comp-sci-mach-learn
subjects: socsim
full_text_status: public
keywords:  Learning, Learning Classifier Systems, Bounded Rationality, Technical Progress, Innovation
note: JEL Classification: O3, L1, D83
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.
date: 2001
date_type: published
refereed: FALSE
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citation:   Yildizoglu, Murat  (2001) Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982).  [Preprint]     
document_url: http://cogprints.org/3864/1/2001-1.pdf