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

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Author Info
Gerard Ballot ()
Erol Taymaz ()

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

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

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  1. Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Working Papers of BETA 9914, Bureau d'Economie Théorique et Appliquée, ULP, Strasbourg. [Downloadable!]
    Other versions:
  2. Murat Yildizoglu, 2002. "Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks," Computing in Economics and Finance 2002 200, Society for Computational Economics. [Downloadable!]
    Other versions:
  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, ULP, Strasbourg. [Downloadable!]
  4. 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, vol. 1(1), pages 57-72, March. [Downloadable!] (restricted)
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