IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach

Listed author(s):
  • Chang, Chia-ling
  • Chen, Shu-heng

While DSGE models have been widely used by central banks for policy analysis, they seem to have been ineffective in calibrating the models for anticipating financial crises. To bring DSGE models closer to real situations, some of researchers have revised the traditional DSGE models. One of the modified DSGE models is the adaptive belief system model. In this framework, changes in sentiment can be expounded by a Boltzmann-Gibbs distribution, and in addition to externally caused fluctuations endogenous interactions are also considered. Methodologically, heuristic switching models are mesoscopic. For this reason, the social network structure is not described in the adaptive belief system models, even though the network structure is an important factor of interaction. The interaction behavior should ideally be based on some kind of social network structures. Today, the Boltzmann-Gibbs distribution is widely used in economic modeling. However, the question is whether the Boltzmann-Gibbs distribution can be directly applied, without considering the underlying social network structure more seriously. To this day, it seems that few scholars have discussed the relationship between social networks and the Boltzmann-Gibbs distribution. Therefore, this paper proposes a network based ant model and tries to compare the population dynamics in the Boltzmann-Gibbs model with different network structure models applied to stylized DSGE models. We find that both the Boltzmann-Gibbs model and the network-based ant model could generate herding behavior. However, it is difficult to envisage the population dynamics generated by the Boltzmann-Gibbs model and the network-based ant model having the same distribution, particularly in popular empirical network structures such as small world networks and scale-free networks. In addition, our simulation results further suggest that the population dynamics of the Boltzmann-Gibbs model and the circle network ant model can be considered with the same distribution under specific parameters settings. This finding is consistent with the study of thermodynamics, on which the Boltzmann-Gibbs distribution is based, namely, the local interaction.

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:
Download Restriction: no

File URL:
Download Restriction: no

Paper provided by Kiel Institute for the World Economy (IfW) in its series Economics Discussion Papers with number 2011-25.

in new window

Date of creation: 2011
Handle: RePEc:zbw:ifwedp:201125
Contact details of provider: Postal:
Kiellinie 66, D-24105 Kiel

Phone: +49 431 8814-1
Fax: +49 431 8814528
Web page:

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. Smets, Frank & Wouters, Raf, 2004. "Forecasting with a Bayesian DSGE model: an application to the euro area," Working Paper Series 0389, European Central Bank.
  2. Alfarano, Simone & Milaković, Mishael & Raddant, Matthias, 2009. "Network hierarchy in Kirman's ant model: fund investment can create systemic risk," Economics Working Papers 2009,09, Christian-Albrechts-University of Kiel, Department of Economics.
  3. Brock, W.A., 1995. "A Rational Route to Randomness," Working papers 9530, Wisconsin Madison - Social Systems.
  4. Iori, G. & Masi, G. D. & Precup, O. V. & Gabbi, G. & Caldarelli, G., 2005. "A network analysis of the Italian oversight money market," Working Papers 05/05, Department of Economics, City University London.
  5. Orphanides, Athanasios & Williams, John C., 2003. "Imperfect knowledge, inflation expectations, and monetary policy," CFS Working Paper Series 2003/40, Center for Financial Studies (CFS).
  6. L. Blume, 2010. "The Statistical Mechanics of Strategic Interaction," Levine's Working Paper Archive 488, David K. Levine.
  7. Lengnick, Matthias & Wohltmann, Hans-Werner, 2010. "Agent-based financial markets and New Keynesian macroeconomics: A synthesis," Economics Working Papers 2010,10, Christian-Albrechts-University of Kiel, Department of Economics.
  8. Paul Grauwe, 2010. "The scientific foundation of dynamic stochastic general equilibrium (DSGE) models," Public Choice, Springer, vol. 144(3), pages 413-443, September.
  9. Fabio Milani, 2009. "Adaptive Learning and Macroeconomic Inertia in the Euro Area," Journal of Common Market Studies, Wiley Blackwell, vol. 47, pages 579-599, 06.
  10. Athanasios Orphanides & John C. Williams, 2007. "Robust monetary policy with imperfect knowledge," Finance and Economics Discussion Series 2007-33, Board of Governors of the Federal Reserve System (U.S.).
  11. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
  12. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521857406, May.
  13. Michael Milakovic & Simone Alfarano, 2007. "Should Network Structure Matter in Agent-Based Finance?," Working Papers wp07-02, Warwick Business School, Finance Group.
  14. Yu-chin Chen & Pisut Kulthanavit, 2008. "Monetary Policy Design under Imperfect Knowledge: An Open Economy Analysis," Working Papers UWEC-2008-14, University of Washington, Department of Economics.
  15. Giorgio Fagiolo & Javier Reyes & Stefano Schiavo, 2007. "International Trade and Financial Integration : a Weighted Network Analysis," Working Papers hal-00973118, HAL.
  16. Branch, William A. & McGough, Bruce, 2009. "A New Keynesian model with heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1036-1051, May.
  17. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, Oxford University Press, vol. 108(1), pages 137-156.
  18. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521674096, May.
  19. Bask, Mikael, 2007. "Long swings and chaos in the exchange rate in a DSGE model with a Taylor rule," Research Discussion Papers 19/2007, Bank of Finland.
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:zbw:ifwedp:201125. 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: (ZBW - German National Library of Economics)

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.