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Modelling Social Learning in an Agent-Based New Keynesian Macroeconomic Model

Author

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  • 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)

Abstract

We propose an agent-based macroeconomic model (ABM) inspired by the New Keynesian general equilibrium model (NKM, Woodford 2003). We analyse the aggregate economic dynamics resulting from social learning of agents (households and firms). Households’ labour supply and consumption demand, as well as firms\' labour demand and wage offers evolve through imitation and random experimenting by the agents. We study, in this setting, the aggregate properties of the economy and the ability of those learning agents to coordinate on the intra-temporal equilibrium of the original model. Our approach is quite different from the existing learning literature in the NKM (à la Evans & Honkapohja, that mainly focuses on learning for testing local stability of equilibria), since learning is directly embedded in the behaviour of the individual agents. This original approach opens new perspectives about the NKM, and allows us to ask new questions about the coordination problems that can result from social learning. First, our computational analysis (Monte Carlo simulations) shows that social learning does not allow the agents to correctly learn about the interdependence between markets, because of the emergence of coordination problems that result in insufficient labour supply and depressive dynamics. Second, we shed light on the general properties of social learning that are behind these results in a general (dis)equilibrium setting, and prove that their neutralisation, at least on the one side of the markets, can significantly improve the performance of the economy. Our results point to the importance of carefully modelling learning mechanisms within macroeconomic ABMs.

Suggested Citation

  • 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.
  • Handle: RePEc:grt:wpegrt:2012-20
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    References listed on IDEAS

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    1. Jasmina Arifovic & James Bullard & Olena Kostyshyna, 2013. "Social Learning and Monetary Policy Rules," Economic Journal, Royal Economic Society, vol. 123(567), pages 38-76, March.
    2. William A. Branch & George W. Evans & Bruce McGough, 2010. "Finite Horizon Learning," University of Oregon Economics Department Working Papers 2010-15, University of Oregon Economics Department.
    3. Isabelle Salle & Murat Yildizoglu & Martin Zumpe & Marc-Alexandre Sénégas, 2012. "Modelling social learning in an Agent-Based new keynesian macroeconomic model," Post-Print hal-00779045, HAL.
    4. Evans, George W. & Honkapohja, Seppo & Mitra, Kaushik, 2009. "Anticipated fiscal policy and adaptive learning," Journal of Monetary Economics, Elsevier, vol. 56(7), pages 930-953, October.
    5. 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.
    6. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    7. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880 Elsevier.
    8. Tesfatsion, Leigh, 2006. "Agent-Based Computational Modeling And Macroeconomics," Staff General Research Papers Archive 12402, Iowa State University, Department of Economics.
    9. Tesfatsion, Leigh, 2006. "Agent-Based Computational Modeling and Macroeconomics," ISU General Staff Papers 200601010800001585, Iowa State University, Department of Economics.
    10. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    11. Arifovic, Jasmina, 2000. "Evolutionary Algorithms In Macroeconomic Models," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 373-414, September.
    12. George W. Evans & Seppo Honkapohja, 2009. "Learning and Macroeconomics," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 421-451, May.
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    Cited by:

    1. Lengnick, Matthias, 2013. "Agent-based macroeconomics: A baseline model," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 102-120.
    2. 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.
    3. 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 2013-24, Groupe de Recherche en Economie Théorique et Appliquée.
    4. Salle, Isabelle & Seppecher, Pascal, 2016. "Social Learning About Consumption," Macroeconomic Dynamics, Cambridge University Press, vol. 20(07), pages 1795-1825, October.
    5. Francesco Lissoni & Fabio Montobbio, 2015. "The Ownership of Academic Patents and Their Impact. Evidence from Five European Countries," Revue économique, Presses de Sciences-Po, vol. 66(1), pages 143-171.
    6. Gerard Ballot & Antoine Mandel & Annick Vignes, 2015. "Agent-based modeling and economic theory: where do we stand?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 199-220, October.
    7. Emmanuel PETIT (GREThA, CNRS, UMR 5113) & Anna TCHERKASSOF (Laboratoire Interuniversitaire de Psychologie. Personnalité, Cognition et Changement Social (LIP/PC2S), Université Pierre Mendès France) & X, 2012. "Sincere Giving and Shame in a Dictator Game," Cahiers du GREThA 2012-25, Groupe de Recherche en Economie Théorique et Appliquée.

    More about this item

    Keywords

    Computational Economics; Agent-Based Modelling; Social Learning; New Keynesian Model; General Equilibrium; Coordination Problems;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D51 - Microeconomics - - General Equilibrium and Disequilibrium - - - Exchange and Production Economies
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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