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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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    References listed on IDEAS

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    7. Francesca Chiaromonte & Giovanni Dosi & Luigi Orsenigo, 2000. "Innovative Learning and Institutions in the Process of Development: On the Microfoundation of Growth Regimes," Chapters, in: Innovation, Organization and Economic Dynamics, chapter 17, pages 501-533, Edward Elgar Publishing.
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    10. 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.
    11. Ballot, Gérard & Taymaz, Erol, 1993. "Firms' Sponsored Training and Performance: A Comparison between France and Sweden Based on Firms Data," Working Paper Series 387, Research Institute of Industrial Economics.
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    Cited by:

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    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. Eliasson, Gunnar, 2023. "Bringing markets back into economics," Journal of Economic Behavior & Organization, Elsevier, vol. 216(C), pages 686-710.
    8. Murat Yildizoglu, 2001. "Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks," Post-Print hal-00125106, HAL.
    9. 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.
    10. Alp Eren Yurtseven & Mehmet Teoman Pamukçu, 2022. "Innovation patterns in firms and intra-industry heterogeneity empirical evidence from Turkey," Evolutionary and Institutional Economics Review, Springer, vol. 19(2), pages 645-679, September.
    11. Gunnar Eliasson, 2018. "Why Complex, Data Demanding and Difficult to Estimate Agent Based Models? Lessons from a Decades Long Research Program," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 4-60.

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