IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/halshs-00573689.html
   My bibliography  Save this paper

Learning the optimal buffer-stock consumption rule of Carroll

Author

Listed:
  • Murat Yildizoglu

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Marc-Alexandre Sénégas

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

  • Isabelle Salle

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

  • Martin Zumpe

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

Abstract

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.

Suggested Citation

  • Murat Yildizoglu & Marc-Alexandre Sénégas & Isabelle Salle & Martin Zumpe, 2011. "Learning the optimal buffer-stock consumption rule of Carroll," Working Papers halshs-00573689, HAL.
  • Handle: RePEc:hal:wpaper:halshs-00573689
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00573689v2
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-00573689v2/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Vallée, Thomas & YIldIzoglu, Murat, 2009. "Convergence in the finite Cournot oligopoly with social and individual learning," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 670-690, November.
    2. Murat Yildizoglu, 2001. "Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks," Post-Print hal-00125106, HAL.
    3. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    4. Deaton, Angus, 1991. "Saving and Liquidity Constraints," Econometrica, Econometric Society, vol. 59(5), pages 1221-1248, September.
    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. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
    7. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
    8. 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.
    9. Howitt, Peter & Özak, Ömer, 2014. "Adaptive consumption behavior," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 37-61.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Isabelle Salle & Murat Yıldızoğlu, 2014. "Efficient Sampling and Meta-Modeling for Computational Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 507-536, December.
    2. Arifovic, Jasmina & Yıldızoğlu, Murat, 2019. "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 191-208.
    3. Meissner, Thomas & Rostam-Afschar, Davud, 2017. "Learning Ricardian Equivalence," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 273-288.
    4. Isabelle SALLE & Marc-Alexandre SENEGAS & Murat YILDIZOGLU, 2013. "How Transparent About Its Inflation Target Should a Central Bank be? An Agent-Based Model Assessment," Cahiers du GREThA (2007-2019) 2013-24, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    5. Salle, Isabelle & Seppecher, Pascal, 2016. "Social Learning About Consumption," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1795-1825, October.
    6. Salle, Isabelle & Yıldızoğlu, Murat & Sénégas, Marc-Alexandre, 2013. "Inflation targeting in a learning economy: An ABM perspective," Economic Modelling, Elsevier, vol. 34(C), pages 114-128.
    7. Isabelle Salle & Marc-Alexandre Sénégas & Murat Yıldızoğlu, 2019. "How transparent about its inflation target should a central bank be?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 391-427, March.
    8. Salle, Isabelle L., 2015. "Modeling expectations in agent-based models — An application to central bank's communication and monetary policy," Economic Modelling, Elsevier, vol. 46(C), pages 130-141.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salle, Isabelle & Seppecher, Pascal, 2016. "Social Learning About Consumption," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1795-1825, October.
    2. Marco Casari, 2002. "Can genetic algorithms explain experimental anomalies? An application to common property resources," UFAE and IAE Working Papers 542.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    3. Casari, Marco, 2008. "Markets in equilibrium with firms out of equilibrium: A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 65(2), pages 261-276, February.
    4. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
    5. Shu-Heng Chen & Chia-Hsuan Yeh, 1999. "Evolving Traders and the Faculty of the Business School: A New Architecture of the Artificial Stock Market," Computing in Economics and Finance 1999 613, Society for Computational Economics.
    6. Herbert Dawid & Philipp Harting, 2012. "Capturing Firm Behavior in Agent-based Models of Industry Evolution and Macroeconomic Dynamics," Chapters, in: Guido Buenstorf (ed.), Evolution, Organization and Economic Behavior, chapter 6, Edward Elgar Publishing.
    7. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    8. Marco Casari, 2003. "Does bounded rationality lead to individual heterogeneity? The impact of the experimentation process and of memory constraints," UFAE and IAE Working Papers 583.03, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    9. Marco Casari, 2004. "Can Genetic Algorithms Explain Experimental Anomalies?," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 257-275, March.
    10. Anufriev, Mikhail & Kopányi, Dávid, 2018. "Oligopoly game: Price makers meet price takers," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 84-103.
    11. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    12. Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 355-370, November.
    13. Georges, Christophre, 2006. "Learning with misspecification in an artificial currency market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(1), pages 70-84, May.
    14. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    15. Mattheos Protopapas & Francesco Battaglia & Elias Kosmatopoulo, 2008. "Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games," Working Papers 004, COMISEF.
    16. Murat YILDIZOGLU, 2009. "Evolutionary approaches of economic dynamics (In French)," Cahiers du GREThA (2007-2019) 2009-16, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    17. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    18. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
    19. Arifovic, Jasmina & Yıldızoğlu, Murat, 2019. "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 191-208.
    20. Salle, Isabelle & Yildizoglu, Murat & Zumpe, Martin & Sénégas, Marc-Alexandre, 2017. "Coordination through social learning in a general equilibrium model," Journal of Economic Behavior & Organization, Elsevier, vol. 141(C), pages 64-82.

    More about this item

    Keywords

    Consumption decisions; Learning; Expectations; Adaptive behaviour; Computational economics;
    All these keywords.

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:halshs-00573689. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.