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Technological Change, Learning and Macro-Economic Coordination: an Evolutionary Model

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Abstract

The purpose of the paper is to model the process of rule generation by firms that must allocate their resources between physical assets, training, and R&D, and to study the microeconomic performances as well as the aggregate outcomes. The framework is a complete micro-macroeconomic Leontieff-Keynesian model initialised with Swedish firms, and provides one of the first applications of the " artificial world " methodology to a complete economic system. The model also displays detailed features of technological change and firms' human capital. In this complex and evolving Schumpeterian environment, firms are "boundedly rational" and use rules. They learn better rules to survive, and we model this process with the use of classifiers. We are able to show that the diversity of rules is sustained over time, as well as the heterogeneity of firms' performances. Simple rules appear to secure larger market shares than complex rules. The learning process improves macroeconomic performance to a large extent whereas barriers to entry are also detrimental for macroeconomic performance.

Suggested Citation

  • Gérard Ballot & Erol Taymaz, 1999. "Technological Change, Learning and Macro-Economic Coordination: an Evolutionary Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 2(2), pages 1-3.
  • Handle: RePEc:jas:jasssj:1998-14-1
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    File URL: http://jasss.soc.surrey.ac.uk/2/2/3/3.pdf
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    1. Fagerberg, Jan, 1994. "Technology and International Differences in Growth Rates," Journal of Economic Literature, American Economic Association, vol. 32(3), pages 1147-1175, September.
    2. Eliasson, Gunnar, 1977. "Competition and Market Processes in a Simulation Model of the Swedish Economy," American Economic Review, American Economic Association, vol. 67(1), pages 277-281, February.
    3. Silverberg, Gerald & Verspagen, Bart, 1994. "Collective Learning, Innovation and Growth in a Boundedly Rational, Evolutionary World," Journal of Evolutionary Economics, Springer, vol. 4(3), pages 207-226, September.
    4. Sargent, Thomas J., 1993. "Bounded Rationality in Macroeconomics: The Arne Ryde Memorial Lectures," OUP Catalogue, Oxford University Press, number 9780198288695.
    5. Akerlof, George A & Yellen, Janet L, 1985. "Can Small Deviations from Rationality Make Significant Differences to Economic Equilibria?," American Economic Review, American Economic Association, vol. 75(4), pages 708-720, September.
    6. Erol Taymaz & G, rard Ballot, 1997. "The dynamics of firms in a micro-to-macro model: The role of training, learning and innovation," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 435-457.
    7. Kenneth J. Arrow, 1962. "The Economic Implications of Learning by Doing," Review of Economic Studies, Oxford University Press, vol. 29(3), pages 155-173.
    8. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
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    Cited by:

    1. Ballot, Gerard, 2002. "Modeling the labor market as an evolving institution: model ARTEMIS," Journal of Economic Behavior & Organization, Elsevier, vol. 49(1), pages 51-77, September.
    2. Murat Yildizoglu, 2001. "Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks," Post-Print hal-00125106, HAL.
    3. Vanessa Oltra & Murat Yildizoglu, 1999. "Non Expectations and Adaptive Behaviours: the Missing Trade-off in Models of Innovation," Working Papers of BETA 9915, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    4. Yildizoglu, Murat, 2002. "Competing R&D Strategies in an Evolutionary Industry Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 51-65, February.
    5. Ma, Tieju & Nakamori, Yoshiteru, 2005. "Agent-based modeling on technological innovation as an evolutionary process," European Journal of Operational Research, Elsevier, vol. 166(3), pages 741-755, November.
    6. Gerard Ballot & Antoine Mandel & Annick Vignes, 2015. "Agent-based modeling and economic theory: where do we stand?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 199-220, October.
    7. repec:ijm:journl:v10:y:2018:i:1:p:4-60 is not listed on IDEAS
    8. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
    9. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.

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