IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/52143.html
   My bibliography  Save this paper

Limited Information Aggregation and Externalities - A Simple Model of Metastable Market

Author

Listed:
  • Gong, Zheng
  • Tian, Feng
  • Xu, Boyan

Abstract

We analyze a model in which agents’ decisions to enter or exit investments are influenced from their individual and external parties’ transaction histories. Actual investment outcomes are unknown to all participants until the end of decision periods, but outcomes do change depending on the number of participating players in the market and the market’s current state of condition. In this particular model, agents have access to external parties’ information from those who are within their specific social network. Our study of limited information aggregation mainly focuses on market responses to investors’ decisions of exiting the investment. With social structures complicating investment outcomes, we present a model that describes how markets can enter relatively stable statuses long enough for exiting participants to return, which brings the investment back to normal conditions. Our model also supports previous studies that limited information aggregation can cause the exogenous shock effect of global collapse.

Suggested Citation

  • Gong, Zheng & Tian, Feng & Xu, Boyan, 2013. "Limited Information Aggregation and Externalities - A Simple Model of Metastable Market," MPRA Paper 52143, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:52143
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/52143/1/MPRA_paper_52143.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. , & , & ,, 2014. "Dynamics of information exchange in endogenous social networks," Theoretical Economics, Econometric Society, vol. 9(1), January.
    2. Hikmet Gunay, 2008. "The role of externalities and information aggregation in market collapse," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 35(2), pages 367-379, May.
    3. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    4. Curtis R. Taylor & Thomas D. Jeitschko, 2001. "Local Discouragement and Global Collapse: A Theory of Coordination Avalanches," American Economic Review, American Economic Association, vol. 91(1), pages 208-224, March.
    5. Guarino, Antonio & Huck, Steffen & Jeitschko, Thomas D., 2006. "Averting economic collapse and the solipsism bias," Games and Economic Behavior, Elsevier, vol. 57(2), pages 264-285, November.
    6. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 1998. "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades," Journal of Economic Perspectives, American Economic Association, vol. 12(3), pages 151-170, Summer.
    Full references (including those not matched with items on IDEAS)

    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. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    2. Drehmann, Mathias & Oechssler, Jorg & Roider, Andreas, 2007. "Herding with and without payoff externalities -- an internet experiment," International Journal of Industrial Organization, Elsevier, vol. 25(2), pages 391-415, April.
    3. James C. D. Fisher & John Wooders, 2017. "Interacting information cascades: on the movement of conventions between groups," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 63(1), pages 211-231, January.
    4. Duffy, John & Lafky, Jonathan, 2021. "Social conformity under evolving private preferences," Games and Economic Behavior, Elsevier, vol. 128(C), pages 104-124.
    5. Fishman, Arthur & Fishman, Ram & Gneezy, Uri, 2019. "A tale of two food stands: Observational learning in the field," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 101-108.
    6. Wang, Peiwen & Chen, Minghua & Wu, Ji & Yan, Yuanyun, 2023. "Do peer effects matter in bank risk? Some cross-country evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    7. Feri, Francesco & Meléndez-Jiménez, Miguel A. & Ponti, Giovanni & Vega-Redondo, Fernando, 2011. "Error cascades in observational learning: An experiment on the Chinos game," Games and Economic Behavior, Elsevier, vol. 73(1), pages 136-146, September.
    8. Patrick Hummel & Brian Knight, 2015. "Sequential Or Simultaneous Elections? A Welfare Analysis," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(3), pages 851-887, August.
    9. Jonathan E. Alevy & Michael S. Haigh & John List, 2006. "Information Cascades: Evidence from An Experiment with Financial Market Professionals," NBER Working Papers 12767, National Bureau of Economic Research, Inc.
    10. Pierdzioch, Christian & Reid, Monique B. & Gupta, Rangan, 2016. "Inflation forecasts and forecaster herding: Evidence from South African survey data," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 62(C), pages 42-50.
    11. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    12. Dorothea Kübler & Georg Weizsäcker, 2004. "Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(2), pages 425-441.
    13. Marco Castillo & Gregory Leo & Ragan Petrie, 2013. "Room Effects," Working Papers 1040, George Mason University, Interdisciplinary Center for Economic Science, revised Apr 2013.
    14. Anna K. Edenbrandt & Christian Gamborg & Bo Jellesmark Thorsen, 2020. "Observational learning in food choices: The effect of product familiarity and closeness of peers," Agribusiness, John Wiley & Sons, Ltd., vol. 36(3), pages 482-498, June.
    15. Dong, Bin & Dulleck, Uwe & Torgler, Benno, 2012. "Conditional corruption," Journal of Economic Psychology, Elsevier, vol. 33(3), pages 609-627.
    16. Agranov, Marina & Elliott, Matt & Ortoleva, Pietro, 2021. "The importance of Social Norms against Strategic Effects: The case of Covid-19 vaccine uptake," Economics Letters, Elsevier, vol. 206(C).
    17. Jonathan E. Alevy & Michael S. Haigh & John A. List, 2007. "Information Cascades: Evidence from a Field Experiment with Financial Market Professionals," Journal of Finance, American Finance Association, vol. 62(1), pages 151-180, February.
    18. Tetsuya Kasahara, 2015. "Strategic Technology Adoption Under Dispersed Information and Information Learning," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 12(06), pages 1-18, December.
    19. Jurui Zhang & Yong Liu & Yubo Chen, 2015. "Social Learning in Networks of Friends versus Strangers," Marketing Science, INFORMS, vol. 34(4), pages 573-589, July.
    20. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.

    More about this item

    Keywords

    Information aggregation; Social structure; Internet Externality; Simulation;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

    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:pra:mprapa:52143. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.