IDEAS home Printed from https://ideas.repec.org/p/eti/dpaper/20092.html
   My bibliography  Save this paper

Three Minds Equal Manjushari's Wisdom: An Anatomy of Informal Social Learning with Heterogenous Agents by the Hierarchical Bayesian Approach

Author

Listed:
  • SATO Masahiro
  • OTA Rui
  • ITO Arata
  • YANO Makoto

Abstract

We learn from all sorts of informal social learning devices, which convey information only inaccurately. Despite this, however, a case supporting a positive contribution of such a device has not been captured in the existing empirical literature. This study builds a discrete choice model of consumption in which informal social learning takes place in a Beta-Bernoulli process of information update. The model is estimated by the Bayesian statistical method with Markov chain Monte Carlo simulation. It provides evidence supporting the positive role of an informal device, to which individual heterogeneity and the effacing of bad news contribute.

Suggested Citation

  • SATO Masahiro & OTA Rui & ITO Arata & YANO Makoto, 2020. "Three Minds Equal Manjushari's Wisdom: An Anatomy of Informal Social Learning with Heterogenous Agents by the Hierarchical Bayesian Approach," Discussion papers 20092, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:dpaper:20092
    as

    Download full text from publisher

    File URL: https://www.rieti.go.jp/jp/publications/dp/20e092.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xavier Vives, 1993. "How Fast do Rational Agents Learn?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(2), pages 329-347.
    2. Mark Israel, 2005. "Services as Experience Goods: An Empirical Examination of Consumer Learning in Automobile Insurance," American Economic Review, American Economic Association, vol. 95(5), pages 1444-1463, December.
    3. Ching, Andrew T., 2010. "Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 619-638, November.
    4. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    6. Rachael Meager, 2019. "Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 57-91, January.
    7. Meager, Rachael, 2019. "Understanding the average impact of microcredit expansions: a Bayesian hierarchical analysis of seven randomized experiments," LSE Research Online Documents on Economics 88190, London School of Economics and Political Science, LSE Library.
    8. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    9. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    10. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    11. Brian Tomlin, 2009. "Impact of Supply Learning When Suppliers Are Unreliable," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 192-209, August.
    12. Honryo, Takakazu & Yano, Makoto, 2021. "Idiosyncratic Information and Vague Communication," American Political Science Review, Cambridge University Press, vol. 115(1), pages 165-178, February.
    13. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, August.
    14. M. Tolga Akçura & Füsun F. Gönül & Elina Petrova, 2004. "Consumer Learning and Brand Valuation: An Application on Over-the-Counter Drugs," Marketing Science, INFORMS, vol. 23(1), pages 156-169, April.
    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. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    2. Szymanowski, M.G., 2009. "Consumption-based learning about brand quality : Essays on how private labels share and borrow reputation," Other publications TiSEM b12825d8-5e21-4437-adda-b, Tilburg University, School of Economics and Management.
    3. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    4. Günter J. Hitsch, 2006. "An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty," Marketing Science, INFORMS, vol. 25(1), pages 25-50, 01-02.
    5. Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    6. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    7. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    8. Karthik Sridhar & Ram Bezawada & Minakshi Trivedi, 2012. "Investigating the Drivers of Consumer Cross-Category Learning for New Products Using Multiple Data Sets," Marketing Science, INFORMS, vol. 31(4), pages 668-688, July.
    9. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
    10. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    11. Szymanowski, Maciej & Gijsbrechts, Els, 2013. "Patterns in consumption-based learning about brand quality for consumer packaged goods," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 219-235.
    12. Neha Bairoliya & Pinar Karaca-Mandic & Jeffrey S. McCullough & Amil Petrin, 2017. "Consumer Learning and the Entry of Generic Pharmaceuticals," NBER Working Papers 23662, National Bureau of Economic Research, Inc.
    13. Li, Dong & Nagurney, Anna & Yu, Min, 2018. "Consumer learning of product quality with time delay: Insights from spatial price equilibrium models with differentiated products," Omega, Elsevier, vol. 81(C), pages 150-168.
    14. Arjen van Lin & Els Gijsbrechts, 2019. "“Hello Jumbo!” The Spatio-Temporal Rollout and Traffic to a New Grocery Chain After Acquisition," Management Science, INFORMS, vol. 67(5), pages 2388-2411, May.
    15. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    16. Yingjie Zhang & Beibei Li & Ramayya Krishnan, 2020. "Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers," Information Systems Research, INFORMS, vol. 31(4), pages 1301-1321, December.
    17. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    18. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    19. Pradeep Chintagunta & Renna Jiang & Ginger Jin, 2009. "Information, learning, and drug diffusion: The case of Cox-2 inhibitors," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 399-443, December.
    20. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.

    More about this item

    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:eti:dpaper:20092. 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: TANIMOTO, Toko (email available below). General contact details of provider: https://edirc.repec.org/data/rietijp.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.