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Social Learning about Consumption

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  • Isabelle Salle

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - CNRS : UMR5113 - Université Montesquieu - Bordeaux IV, CeNDEF - Center for Nonlinear Dynamics in Economics and Finance - Universiteit van Amsterdam)

  • Pascal Seppecher

    ()
    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS : UMR7321 - Université Nice Sophia Antipolis (UNS))

Abstract

This paper applies a social learning model to the optimal consumption rule of Allen & Carroll (2001), and delivers convincing convergence dynamics towards the optimal rule. These findings constitute a significant improvement regarding previous results in the literature, both in terms of speed of convergence and parsimony of the learning model. The learning model exhibits several appealing features: it is frugal, easy to apply to a range of learning objectives, requires few procedures and little information. Particular care is given to behavioural interpretation of the modelling assumptions in light of evidence from the fields of psychology and social science. Our results highlight the need to depart from the genetic metaphor, and account for intentional decision-making, based on agents' relative performances. By contrast, we show that convergence is strongly hindered by exact imitation processes, or random exploration mechanisms, which are usually assumed when modelling social learning behaviour. Our results suggest a method for modelling bounded rationality, which could be tested most interestingly within the framework of a wide range of economic models with adaptive dynamics.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number hal-00989233.

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Date of creation: 10 Sep 2013
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Handle: RePEc:hal:wpaper:hal-00989233

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Keywords: learning; bounded rationality; evolutionary algorithms; consumption rule;

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  1. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
  2. Todd W. Allen & Christopher D. Carroll, 2001. "Individual Learning About Consumption," NBER Working Papers 8234, National Bureau of Economic Research, Inc.
  3. Binswanger, Johannes, 2011. "Dynamic decision making with feasibility goals: A procedural-rationality approach," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 219-228, May.
  4. Christopher D. Carroll, 1996. "Buffer-Stock Saving and the Life Cycle/Permanent Income Hypothesis," NBER Working Papers 5788, National Bureau of Economic Research, Inc.
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  6. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Scholarly Articles 3196332, Harvard University Department of Economics.
  7. Isabelle SALLE (GREThA, CNRS, UMR 5113) & Martin ZUMPE (GREThA, CNRS, UMR 5113) & Murat YILDIZOGLU (GREThA, CNRS, UMR 5113) & Marc-Alexandre SENEGAS (GREThA, CNRS, UMR 5113), 2012. "Modelling Social Learning in an Agent-Based New Keynesian Macroeconomic Model," Cahiers du GREThA 2012-20, Groupe de Recherche en Economie Théorique et Appliquée.
  8. Jasmina Arifovic & James B. Bullard & Olena Kostyshyna, 2007. "Social learning and monetary policy rules," Working Papers 2007-007, Federal Reserve Bank of St. Louis.
  9. 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.
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  12. Murat Yildizoglu & Marc-Alexandre Sénégas & Isabelle Salle & Martin Zumpe, 2011. "Learning the optimal buffer-stock consumption rule of Carroll," Working Papers halshs-00573689, HAL.
  13. Dawid, Herbert, 1997. "Learning of equilibria by a population with minimal information," Journal of Economic Behavior & Organization, Elsevier, vol. 32(1), pages 1-18, January.
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  16. Drew Fudenberg & David K. Levine, 1996. "The Theory of Learning in Games," Levine's Working Paper Archive 624, David K. Levine.
  17. Ulrich Hoffrage & Torsten Reimer, 2004. "Models of Bounded Rationality: The Approach of Fast and Frugal Heuristics," management revue. Socio-economic Studies, Rainer Hampp Verlag, vol. 15(4), pages 437-459.
  18. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 1998. "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades," Journal of Economic Perspectives, American Economic Association, vol. 12(3), pages 151-170, Summer.
  19. A. Banerjee & Drew Fudenberg, 2010. "Word-of-Mouth Communication and Social Learning," Levine's Working Paper Archive 425, David K. Levine.
  20. Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2009. "Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies," ERIM Report Series Research in Management ERS-2009-028-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  21. Arifovic, Jasmina, 2000. "Evolutionary Algorithms In Macroeconomic Models," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 373-414, September.
  22. Arthur, W Brian, 1991. "Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality," American Economic Review, American Economic Association, vol. 81(2), pages 353-59, May.
  23. Judd, Kenneth L., 2006. "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 17, pages 881-893 Elsevier.
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