IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

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

  • 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)

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://cahiersdugretha.u-bordeaux4.fr/2012/2012-20.pdf
Download Restriction: no

Paper provided by Groupe de Recherche en Economie Théorique et Appliquée in its series Cahiers du GREThA with number 2012-20.

as
in new window

Length:
Date of creation: 2012
Date of revision:
Handle: RePEc:grt:wpegrt:2012-20
Contact details of provider: Postal: Avenue Léon Duguit, 33608 Pessac Cedex
Phone: +33 (0)5.56.84.25.75
Fax: +33 (0)5.56.84.86.47
Web page: http://gretha.u-bordeaux4.fr/Email:


More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Thomas Vallée & Murat Yildizoglu, 2009. "Convergence in the Finite Cournot Oligopoly with Social and Individual Learning," Working Papers halshs-00368274, HAL.
  2. Jasmina Arifovic & James Bullard & Olena Kostyshyna, 2013. "Social Learning and Monetary Policy Rules," Economic Journal, Royal Economic Society, vol. 123(567), pages 38-76, 03.
  3. George W. Evans & Seppo Honkapohja & Kaushik Mitra, 2007. "Anticipated Fiscal Policy and Adaptive Learning," CDMA Working Paper Series 200717, Centre for Dynamic Macroeconomic Analysis.
  4. Bruce Preston, 2003. "Learning about monetary policy rules when long-horizon expectations matter," Working Paper 2003-18, Federal Reserve Bank of Atlanta.
  5. George W. Evans & Seppo Honkapohja, 2009. "Learning and Macroeconomics," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 421-451, 05.
  6. 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.
  7. Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Computing in Economics and Finance 2006 527, Society for Computational Economics.
  8. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  9. Tesfatsion, Leigh, 2006. "Agent-Based Computational Modeling And Macroeconomics," Staff General Research Papers 12402, Iowa State University, Department of Economics.
  10. Arifovic, Jasmina, 2000. "Evolutionary Algorithms In Macroeconomic Models," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 373-414, September.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:grt:wpegrt:2012-20. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Emmanuel Petit)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.