IDEAS home Printed from https://ideas.repec.org/p/boe/boeewp/0503.html
   My bibliography  Save this paper

Peering into the mist: social learning over an opaque observation network

Author

Listed:
  • Barrdear, John

    () (Bank of England)

Abstract

I present a model of social learning over an exogenous, directed network that may be readily nested within broader macroeconomic models with dispersed information and combines the attributes that agents (a) act repeatedly and simultaneously; (b) are Bayes-rational; and (c) have strategic interaction in their decision rules. To overcome the challenges imposed by these requirements, I suppose that the network is opaque: agents do not know the full structure of the network, but do know the link distribution. I derive a specific law of motion for the hierarchy of aggregate expectations, which includes a role for network shocks (weighted sums of agents' idiosyncratic shocks). The network causes agents' beliefs to exhibit increased persistence, so that average expectations overshoot the truth following an aggregate shock. When the network is sufficiently (and plausibly) irregular, transitory idiosyncratic shocks cause persistent aggregate effects, even when agents are identically sized and do not trade.

Suggested Citation

  • Barrdear, John, 2014. "Peering into the mist: social learning over an opaque observation network," Bank of England working papers 503, Bank of England.
  • Handle: RePEc:boe:boeewp:0503
    as

    Download full text from publisher

    File URL: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2014/peering-into-the-mist-social-learning-over-an-opaque-observation-network.pdf?la=en&hash=DFF2ECE1E988EDD38277D832BFAB9800881531F0
    File Function: Full text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nimark, Kristoffer, 2008. "Dynamic pricing and imperfect common knowledge," Journal of Monetary Economics, Elsevier, vol. 55(2), pages 365-382, March.
    2. Angeletos, George-Marios & La’O, Jennifer, 2009. "Incomplete information, higher-order beliefs and price inertia," Journal of Monetary Economics, Elsevier, vol. 56(S), pages 19-37.
    3. Kristoffer Nimark, 2009. "A low dimensional Kalman filter for systems with lagged observables," Economics Working Papers 1182, Department of Economics and Business, Universitat Pompeu Fabra.
    4. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    5. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, Oxford University Press, vol. 117(4), pages 1295-1328.
    6. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    7. Graham, Liam & Wright, Stephen, 2010. "Information, heterogeneity and market incompleteness," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 164-174, March.
    8. Sanjeev Goyal, 2007. "Introduction to Connections: An Introduction to the Economics of Networks," Introductory Chapters,in: Connections: An Introduction to the Economics of Networks Princeton University Press.
    9. Susanne M. Schennach, 2013. "Long memory via networking," CeMMAP working papers CWP13/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. N. Gregory Mankiw & Ricardo Reis, 2007. "Sticky Information in General Equilibrium," Journal of the European Economic Association, MIT Press, vol. 5(2-3), pages 603-613, 04-05.
    11. Lars Ljungqvist & Thomas J. Sargent, 2004. "Recursive Macroeconomic Theory, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026212274x, May.
    12. Kristoffer Nimark, 2007. "Dynamic higher order expectations," Economics Working Papers 1118, Department of Economics and Business, Universitat Pompeu Fabra, revised Mar 2011.
    13. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," Review of Economic Studies, Oxford University Press, vol. 78(4), pages 1201-1236.
    14. Banerjee, Abhijit & Fudenberg, Drew, 2004. "Word-of-mouth learning," Games and Economic Behavior, Elsevier, vol. 46(1), pages 1-22, January.
    15. Xavier Gabaix, 2011. "The Granular Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 79(3), pages 733-772, May.
    16. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, Oxford University Press, vol. 118(3), pages 909-968.
    17. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    18. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
    19. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    20. Leonardo Melosi, 2014. "Estimating Models with Dispersed Information," American Economic Journal: Macroeconomics, American Economic Association, vol. 6(1), pages 1-31, January.
    21. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    22. Lucas, Robert Jr., 1972. "Expectations and the neutrality of money," Journal of Economic Theory, Elsevier, vol. 4(2), pages 103-124, April.
    23. N. Gregory Mankiw & Ricardo Reis, 2006. "Pervasive Stickiness," American Economic Review, American Economic Association, vol. 96(2), pages 164-169, May.
    24. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    25. Stephen Morris & Hyun Song Shin, 2002. "Social Value of Public Information," American Economic Review, American Economic Association, vol. 92(5), pages 1521-1534, December.
    26. Mueller-Frank, Manuel, 2013. "A general framework for rational learning in social networks," Theoretical Economics, Econometric Society, vol. 8(1), January.
    27. Townsend, Robert M, 1983. "Forecasting the Forecasts of Others," Journal of Political Economy, University of Chicago Press, vol. 91(4), pages 546-588, August.
    28. N. Gregory Mankiw & Ricardo Reis, 2006. "Pervasive Stickiness (Expanded Version)," NBER Working Papers 12024, National Bureau of Economic Research, Inc.
    29. Guido Lorenzoni, 2009. "A Theory of Demand Shocks," American Economic Review, American Economic Association, vol. 99(5), pages 2050-2084, December.
    30. Antoni Calvó-Armengol & Joan de Martí, 2007. "Communication Networks: Knowledge and Decisions," American Economic Review, American Economic Association, vol. 97(2), pages 86-91, May.
    31. Matthew O. Jackson & Brian W. Rogers, 2007. "Meeting Strangers and Friends of Friends: How Random Are Social Networks?," American Economic Review, American Economic Association, vol. 97(3), pages 890-915, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Dispersed information; network learning; heterogeneous agents; aggregate volatility;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:boe:boeewp:0503. 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: (Digital Media Team). General contact details of provider: http://edirc.repec.org/data/boegvuk.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.