Cogprints

Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)

Yildizoglu, Murat (2001) Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982). [Preprint]

Full text available as:

[img]
Preview
PDF
424Kb

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
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

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Butz, M. V. [2000], XCSJava 1.0: An Implementation of the XCS classifier system in Java , Technical Report

2000027, Illinois Genetic Algorithms Laboratory.

Butz, M. V. & Wilson, S.W. [2000], An Algorithmic Description of XCS, Technical Report 2000017, Illinois

Genetic Algorithms Laboratory.

Goldberg, D. E. [1991], Genetic Algorithms, Addison-Wesley, Reading: MA.

Jonard, N. & Yildizoglu, M. [1998], ‘Technological diversity in an evolutionary industry model with localized

learning and network externalities’, Structural Change and Economic Dynamics 9(1), 35–55.

Knight, F. H. [1921], Risk, Uncertainty and Profits, number Reprint, Chicago University Press, Chicago.

Kwasnicki,W.& Kwasnicka, H. [1992], ‘Market, innovation, competition. an evolutionary model of industrial

dynamics’, Journal of Economic Behavior and Organization 19, 343–368.

Lanzi, P. L., Stoltzmann, W. & Wilson, S. W. [2000], Learning Classifier Systems. From Foundations to

Applications, Vol. 1813 of LNAI, Springer, Berlin.

Nelson, R. R. & Winter, S. [1982], An Evolutionary Theory of Economic Change, The Belknap Press of

Harvard University, London.

Oltra, V. & Yildizoglu, M. [1999], ‘Expectations and adaptive behaviour: the missing trade-off in models of

innovation’, WP BETA No. 9905, Universite Louis Pasteur, Strasbourg .

Silverberg, G., Dosi, G. & Orsenigo, L. [1988], ‘Innovation, diversity and diffusion: a self-organization

model’, Economic Journal 98, 1032–1054.

Silverberg, J. & Verspagen, B. [1996], From the artificial to the endogenous, in M. Helmstadter, Ernst; Perlman,

ed., ‘Behavioral norms, technological progress, and economic dynamics: Studies in Schumpeterian

economics’, University of Michigan Press, Ann Arbor.

Simon, H. A. [1958], The role of expectations in adaptive or behavoristic model, in M. Bowman, ed., ‘Expectations,

Uncertainty and Business Behavior’, Social Science Council, New York, pp. 49–58.

Simon, H. A. [1976], From substantial to procedural rationality, in Latsis, S. J. (ed), Method and Appraisal in

Economics, Cambridge University Press, Cambridge, pp. 129–148.

Watson, C. J., Billingsley, D. J., Croft, D. J. & Huntsberger, D. V. [1993], Statistics for Management and

Economics, fifth edition, Allyn and Bacon, Boston.

Wilson, S. W. [1995], ‘Classifier Fitness Based on Accuracy’, Evolutionary Computation 3(2), 149–175.

http://prediction-dynamics.com/.

Yildizoglu, M. [2001], ‘Competing r&d strategies in an evolutionary industry model’, forthcoming in Computational

Economics. Available at http://yildizoglu.montesquieu.u-bordeaux.fr/ .

Metadata

Repository Staff Only: item control page