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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.

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File URL: http://jasss.soc.surrey.ac.uk/2/2/3.html
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Bibliographic Info

Article provided by Journal of Artificial Societies and Social Simulation in its journal Journal of Artificial Societies and Social Simulation.

Volume (Year): 2 (1999)
Issue (Month): 2 ()
Pages: 3

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Handle: RePEc:jas:jasssj:1998-14-1

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Keywords: Technological Change; Human Capital; Endogenous Growth; Artificial Intelligence; Artificial Worlds; Classifier Systems; Microsimulation; Evolutionary Theory;

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Cited by:
  1. Ma, Tieju & Nakamori, Yoshiteru, 2005. "Agent-based modeling on technological innovation as an evolutionary process," European Journal of Operational Research, Elsevier, Elsevier, vol. 166(3), pages 741-755, November.
  2. Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Computing in Economics and Finance 1999, Society for Computational Economics 343, Society for Computational Economics.
  3. 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.
  4. Ballot, Gerard, 2002. "Modeling the labor market as an evolving institution: model ARTEMIS," Journal of Economic Behavior & Organization, Elsevier, Elsevier, vol. 49(1), pages 51-77, September.
  5. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind and Society: Cognitive Studies in Economics and Social Sciences, Fondazione Rosselli, Fondazione Rosselli, vol. 1(1), pages 57-72, March.
  6. 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.
  7. Murat Yildizoglu, 2001. "Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks," Working Papers, Equipe Industries Innovation Institutions, Université Bordeaux IV, France 2001-2, Equipe Industries Innovation Institutions, Université Bordeaux IV, France.

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