title: Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)
creator: Yildizoglu, Murat
subject: Machine Learning
subject: Social simulation
description: 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
type: Preprint
type: NonPeerReviewed
format: application/pdf
identifier: http://cogprints.org/3864/1/2001-1.pdf
identifier:   Yildizoglu, Murat  (2001) Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982).  [Preprint]     
relation: http://cogprints.org/3864/