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Learning the optimal buffer-stock consumption rule of Carroll

  • Murat Yildizoglu


    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579)

  • Marc-Alexandre Sénégas

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - CNRS : UMR5113 - Université Montesquieu - Bordeaux IV)

  • Isabelle Salle

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - CNRS : UMR5113 - Université Montesquieu - Bordeaux IV)

  • Martin Zumpe


    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - CNRS : UMR5113 - Université Montesquieu - Bordeaux IV)

This article questions the rather pessimistic conclusions of Allen et Carroll (2001) about the ability of consumer to learn the optimal buffer-stock based consumption rule. To this aim, we develop an agent based model where alternative learning schemes can be compared in terms of the consumption behaviour that they yield. We show that neither purely adaptive learning, nor social learning based on imitation can ensure satisfactory consumption behaviours. By contrast, if the agents can form adaptive expectations, based on an evolving individual mental model, their behaviour becomes much more interesting in terms of its regularity, and its ability to improve performance (which is as a clear manifestation of learning). Our results indicate that assumptions on bounded rationality, and on adaptive expectations are perfectly compatible with sound and realistic economic behaviour, which, in some cases, can even converge to the optimal solution. This framework may therefore be used to develop macroeconomic models with adaptive dynamics.

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Paper provided by HAL in its series Working Papers with number halshs-00573689.

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Date of creation: 04 Mar 2011
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Handle: RePEc:hal:wpaper:halshs-00573689
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  1. Thomas Vallée & Murat Yildizoglu, 2009. "Convergence in the Finite Cournot Oligopoly with Social and Individual Learning," Working Papers halshs-00368274, HAL.
  2. Murat Yildizoglu, 2001. "Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks," Working Papers 2001-2, Equipe Industries Innovation Institutions, Université Bordeaux IV, France.
  3. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
  4. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  5. Happe, Kathrin, 2005. "Agent-Based Modelling and Sensitivity Analysis by Experimental Design and Metamodelling: An Application to Modelling Regional Structural Change," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24464, European Association of Agricultural Economists.
  6. Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Computing in Economics and Finance 1999 343, Society for Computational Economics.
  7. Oeffner, Marc, 2008. "Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation," MPRA Paper 18199, University Library of Munich, Germany, revised Oct 2009.
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