IDEAS home Printed from https://ideas.repec.org/f/pmo980.html
   My authors  Follow this author

Pooya Molavi

Personal Details

First Name:Pooya
Middle Name:
Last Name:Molavi
Suffix:
RePEc Short-ID:pmo980
[This author has chosen not to make the email address public]

Affiliation

Economics Department
Massachusetts Institute of Technology (MIT)

Cambridge, Massachusetts (United States)
http://econ-www.mit.edu/

: (617) 253-3361
(617) 253-1330
50 Memorial Drive, E52-391, Cambridge, MA 02142
RePEc:edi:edmitus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Ali Jadbabaie & Pooya Molavi & Alvaro Sandroni & Alireza Tahbaz-Salehi, 2009. "Non-Bayesian Social Learning, Third Version," PIER Working Paper Archive 11-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 05 Aug 2011.

Articles

  1. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Ali Jadbabaie & Pooya Molavi & Alvaro Sandroni & Alireza Tahbaz-Salehi, 2009. "Non-Bayesian Social Learning, Third Version," PIER Working Paper Archive 11-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 05 Aug 2011.

    Cited by:

    1. Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
    2. Bar Ifrach & Costis Maglaras & Marco Scarsini, 2012. "Monopoly Pricing in the Presence of Social Learning," Working Papers 12-01, NET Institute, revised Sep 2012.
    3. Fang, Aili & Wang, Lin & Zhao, Jiuhua & Wang, Xiaofan, 2013. "Chaos in social learning with multiple true states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5786-5792.

Articles

  1. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.

    Cited by:

    1. Kwon, Seokbeom & Motohashi, Kazuyuki, 2017. "How institutional arrangements in the National Innovation System affect industrial competitiveness: A study of Japan and the U.S. with multiagent simulation," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 221-235.
    2. Eger, Steffen, 2016. "Opinion dynamics and wisdom under out-group discrimination," Mathematical Social Sciences, Elsevier, vol. 80(C), pages 97-107.
    3. Berno Buechel & Tim Hellmann & Stefan Kölßner, 2014. "Opinion Dynamics and Wisdom under Conformity," Working Papers 2014.51, Fondazione Eni Enrico Mattei.
    4. Pietro Dindo & Filippo Massari, 2017. "The Wisdom of the Crowd in Dynamic Economies," Working Papers 2017:17, Department of Economics, University of Venice "Ca' Foscari", revised 2018.
    5. Aislinn Bohren & Daniel Hauser, 2017. "Bounded Rationality And Learning: A Framwork and A Robustness Result," PIER Working Paper Archive 17-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 May 2017.
    6. Battiston, Pietro & Stanca, Luca, 2015. "Boundedly rational opinion dynamics in social networks: Does indegree matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 400-421.
    7. KWON Seokbeom & MOTOHASHI Kazuyuki, 2015. "How Institutional Arrangements in the National Innovation System Affect Industrial Competitiveness: A study of Japan and the United States with multiagent simulation," Discussion papers 15065, Research Institute of Economy, Trade and Industry (RIETI).
    8. Krishna Dasaratha & Kevin He, 2017. "Network Structure and Naive Sequential Learning," Papers 1703.02105, arXiv.org, revised Jul 2019.
    9. Christoph Aymanns & Jakob Foerster & Co-Pierre Georg, 2017. "Fake News in Social Networks," Papers 1708.06233, arXiv.org.
    10. Matthew Ellman, 2017. "Online Social Networks: Approval by Design," Working Papers 17-18, NET Institute.
    11. Christoph Aymanns & Jakob Foerster & Co-Pierre Georg, 2017. "Fake News in Social Networks," Working Papers on Finance 1804, University of St. Gallen, School of Finance.
    12. Lobel, Ilan & Sadler, Evan, 2015. "Information diffusion in networks through social learning," Theoretical Economics, Econometric Society, vol. 10(3), September.
    13. Pietro Battiston & Luca Stanca, 2014. "Boundedly Rational Opinion Dynamics in Directed Social Networks: Theory and Experimental Evidence," Working Papers 267, University of Milano-Bicocca, Department of Economics, revised Jan 2014.
    14. Aislinn Bohren & Daniel Hauser, 2018. "Social Learning with Model Misspeciification: A Framework and a Robustness Result," PIER Working Paper Archive 18-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jul 2018.
    15. Krishna Dasaratha & Benjamin Golub & Nir Hak, 2018. "Social Learning in a Dynamic Environment," Papers 1801.02042, arXiv.org, revised Feb 2019.
    16. Ilai Bistritz & Nasimeh Heydaribeni & Achilleas Anastasopoulos, 2019. "Characterizing non-myopic information cascades in Bayesian learning," Papers 1905.01327, arXiv.org.
    17. Tomasz Makarewicz, 2017. "Contrarian Behavior, Information Networks and Heterogeneous Expectations in an Asset Pricing Model," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 231-279, August.
    18. Drago, Francesco & Mengel, Friederike & Traxler, Christian, 2015. "Compliance Behavior in Networks: Evidence from a Field Experiment," IZA Discussion Papers 9443, Institute of Labor Economics (IZA).
    19. Sebastiano Della Lena, 2019. "Non-Bayesian Social Learning and the Spread of Misinformation in Networks," Working Papers 2019:09, Department of Economics, University of Venice "Ca' Foscari".
    20. Mueller-Frank, Manuel, 2015. "Reaching Consensus in Social Networks," IESE Research Papers D/1116, IESE Business School.
    21. Boğaçhan Çelen & Sen Geng & Huihui Li, 2019. "Belief Error and Non-Bayesian Social Learning: Experimental Evidence," Working Papers 2019-07-08, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    22. Matjaž Steinbacher & Mitja Steinbacher, 2019. "Opinion Formation with Imperfect Agents as an Evolutionary Process," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 479-505, February.
    23. Guodong Shi & Alexandre Proutiere & Mikael Johansson & John S. Baras & Karl H. Johansson, 2016. "The Evolution of Beliefs over Signed Social Networks," Operations Research, INFORMS, vol. 64(3), pages 585-604, June.
    24. Ionel Popescu & Tushar Vaidya, 2019. "Averaging plus Learning in financial markets," Papers 1904.08131, arXiv.org, revised Jun 2019.
    25. Ilan Lobel & Evan Sadler, 2013. "Preferences, Homophily, and Social Learning," Working Papers 13-01, NET Institute.
    26. Pooya Molavi & Ceyhun Eksin & Alejandro Ribeiro & Ali Jadbabaie, 2016. "Learning to Coordinate in Social Networks," Operations Research, INFORMS, vol. 64(3), pages 605-621, June.
    27. Schwarz, Marco A., 2017. "The Impact of Social Media On Belief Formation," Rationality and Competition Discussion Paper Series 57, CRC TRR 190 Rationality and Competition.
    28. Rajiv Sethi & Muhamet Yildiz, 2013. "Perspectives, Opinions, and Information Flows," Levine's Working Paper Archive 786969000000000934, David K. Levine.
    29. Munther A. Dahleh & Alireza Tahbaz-Salehi & John N. Tsitsiklis & Spyros I. Zoumpoulis, 2016. "Technical Note—Coordination with Local Information," Operations Research, INFORMS, vol. 64(3), pages 622-637, June.
    30. Fu, Guiyuan & Zhang, Weidong & Li, Zhijun, 2015. "Opinion dynamics of modified Hegselmann–Krause model in a group-based population with heterogeneous bounded confidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 558-565.
    31. Maxim Raginsky & Angelia Nedić, 2016. "Online Discrete Optimization in Social Networks in the Presence of Knightian Uncertainty," Operations Research, INFORMS, vol. 64(3), pages 662-679, June.
    32. Golub Benjamin & Jackson Matthew O., 2012. "Does Homophily Predict Consensus Times? Testing a Model of Network Structure via a Dynamic Process," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-31, September.
    33. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2015. "Networks, Shocks, and Systemic Risk," NBER Working Papers 20931, National Bureau of Economic Research, Inc.
    34. Fang, Aili & Wang, Lin & Wei, Xinjiang, 2019. "Social learning with multiple true states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 375-386.
    35. Matan Harel & Elchanan Mossel & Philipp Strack & Omer Tamuz, 2014. "Rational Groupthink," Papers 1412.7172, arXiv.org, revised Dec 2018.
    36. Marina Azzimonti & Marcos Fernandes, 2018. "Social Media Networks, Fake News, and Polarization," NBER Working Papers 24462, National Bureau of Economic Research, Inc.
    37. Wang, Huanjing & Shang, Lihui, 2015. "Opinion dynamics in networks with common-neighbors-based connections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 180-186.
    38. Liu, Qipeng & Wang, Xiaofan, 2013. "Social learning with bounded confidence and heterogeneous agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2368-2374.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Pooya Molavi should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can 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.