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Pooya Molavi

Personal Details

First Name:Pooya
Middle Name:
Last Name:Molavi
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RePEc Short-ID:pmo980
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Affiliation

(50%) Becker Friedman Institute for Research in Economics
University of Chicago

Chicago, Illinois (United States)
http://bfi.uchicago.edu/
RePEc:edi:mfichus (more details at EDIRC)

(50%) Managerial Economics and Decision Sciences (MEDS)
Kellogg Graduate School of Management
Northwestern University

Evanston, Illinois (United States)
http://www.kellogg.northwestern.edu/meds/index.htm
RePEc:edi:menwuus (more details at EDIRC)

Research output

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Jump to: Working papers Articles

Working papers

  1. Pooya Molavi, 2019. "Macroeconomics with Learning and Misspecification: A General Theory and Applications," 2019 Meeting Papers 1584, Society for Economic Dynamics.

Articles

  1. Pooya Molavi & Alireza Tahbaz‐Salehi & Ali Jadbabaie, 2018. "A Theory of Non‐Bayesian Social Learning," Econometrica, Econometric Society, vol. 86(2), pages 445-490, March.
  2. 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.
  3. 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. Pooya Molavi, 2019. "Macroeconomics with Learning and Misspecification: A General Theory and Applications," 2019 Meeting Papers 1584, Society for Economic Dynamics.

    Cited by:

    1. George-Marios Angeletos & Chen Lian, 2020. "Confidence and the Propagation of Demand Shocks," NBER Working Papers 27702, National Bureau of Economic Research, Inc.
    2. Frick, Mira & Iijima, Ryota & Ishii, Yuhta, 2021. "Belief Convergence under Misspecified Learning: A Martingale Approach," CEPR Discussion Papers 16788, C.E.P.R. Discussion Papers.
    3. Angeletos, Georges Marios & Collard, Fabrice & Dellas, Harris, 2020. "Business Cycle Anatomy," TSE Working Papers 20-1065, Toulouse School of Economics (TSE).
    4. Anastasios G. Karantounias, 2020. "Doubts about the Model and Optimal Policy," FRB Atlanta Working Paper 2020-12, Federal Reserve Bank of Atlanta.
    5. Nick Netzer & Arthur Robson & Jakub Steiner & Pavel Kocourek, 2022. "Endogenous Risk Attitudes," CESifo Working Paper Series 9547, CESifo.
    6. Eleni Iliopulos & Erica Perego & Thepthida Sopraseuth, 2019. "International Business Cycles: Information Matters," Working Papers 2019-03, CEPII research center.
    7. Barrero, Jose Maria, 2022. "The micro and macro of managerial beliefs," Journal of Financial Economics, Elsevier, vol. 143(2), pages 640-667.
    8. Peter Andre & Carlo Pizzinelli & Christopher Roth & Johannes Wohlfart, 2021. "Subjective Models of the Macroeconomy: Evidence From Experts and Representative Samples," ECONtribute Discussion Papers Series 119, University of Bonn and University of Cologne, Germany.
    9. Kfir Eliaz & Ran Spiegler & Yair Weiss, 2019. "Cheating with (Recursive) Models," Papers 1911.01251, arXiv.org.
    10. Kevin He & Jonathan Libgober, 2021. "Evolutionarily Stable (Mis)specifications:Theory and Applications," PIER Working Paper Archive 21-020, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    11. Drew Fudenberg & Giacomo Lanzani & Philipp Strack, 2021. "Limit Points of Endogenous Misspecified Learning," Econometrica, Econometric Society, vol. 89(3), pages 1065-1098, May.
    12. Takeshi Murooka & Yuichi Yamamoto, 2021. "Misspecified Bayesian Learning by Strategic Players: First-Order Misspecification and Higher-Order Misspecification," OSIPP Discussion Paper 21E008, Osaka School of International Public Policy, Osaka University.
    13. Macaulay, Alistair & Song, Wenting, 2022. "Narrative-Driven Fluctuations in Sentiment: Evidence Linking Traditional and Social Media," MPRA Paper 113620, University Library of Munich, Germany.
    14. George-Marios Angeletos & Zhen Huo & Karthik A. Sastry, 2020. "Imperfect Macroeconomic Expectations: Evidence and Theory," NBER Chapters, in: NBER Macroeconomics Annual 2020, volume 35, pages 1-86, National Bureau of Economic Research, Inc.
    15. Gáti, Laura, 2022. "Monetary policy & anchored expectations: an endogenous gain learning model," Working Paper Series 2685, European Central Bank.
    16. Peter Andrebriq & Carlo Pizzinelli & Christopher Roth & Johannes Wohlfart, 2022. "Subjective Models of the Macroeconomy: Evidence From Experts and Representative Samples [Rationally Confused: On the Aggregate Implications of Information Provision Policies]," Review of Economic Studies, Oxford University Press, vol. 89(6), pages 2958-2991.
    17. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Stability and Robustness in Misspecified Learning Models," Cowles Foundation Discussion Papers 2235, Cowles Foundation for Research in Economics, Yale University.
    18. Ignacio Esponda & Demian Pouzo & Yuichi Yamamoto, 2019. "Asymptotic Behavior of Bayesian Learners with Misspecified Models," Papers 1904.08551, arXiv.org, revised Oct 2019.
    19. Yingkai Li & Harry Pei, 2020. "Misspecified Beliefs about Time Lags," Papers 2012.07238, arXiv.org.
    20. Takeshi Murooka & Yuichi Yamamoto, 2021. "Multi-Player Bayesian Learning with Misspecified Models," OSIPP Discussion Paper 21E001, Osaka School of International Public Policy, Osaka University.
    21. Fudenberg, Drew & Lanzani, Giacomo, 0. "Which misspecifications persist?," Theoretical Economics, Econometric Society.
    22. Alistair Macaulay, 2022. "Heterogeneous Information, Subjective Model Beliefs, and the Time-Varying Transmission of Shocks," CESifo Working Paper Series 9733, CESifo.
    23. Fudenberg, Drew & Lanzani, Giacomo & Strack, Philipp, 0. "Pathwise concentration bounds for Bayesian beliefs," Theoretical Economics, Econometric Society.
    24. Anmol Bhandari & Jaroslav Borovicka & Paul Ho, 2019. "Survey Data and Subjective Beliefs in Business Cycle Models," Working Paper 19-14, Federal Reserve Bank of Richmond.

Articles

  1. Pooya Molavi & Alireza Tahbaz‐Salehi & Ali Jadbabaie, 2018. "A Theory of Non‐Bayesian Social Learning," Econometrica, Econometric Society, vol. 86(2), pages 445-490, March.

    Cited by:

    1. 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".
    2. Ghosh, Aniruddha & Khan, M. Ali, 2021. "On a diversity of perspectives and world views: Learning under Bayesian vis-á-vis DeGroot updating," Economics Letters, Elsevier, vol. 202(C).
    3. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    4. Sebastiano Della Lena & Luca Paolo Merlino, 2021. "Group Identity, Social Learning and Opinion Dynamics," Papers 2110.07226, arXiv.org, revised May 2022.
    5. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    6. Glass, Catherine A. & Glass, David H., 2021. "Opinion dynamics of social learning with a conflicting source," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    7. Crès, Hervé & Tvede, Mich, 2022. "Aggregation of opinions in networks of individuals and collectives," Journal of Economic Theory, Elsevier, vol. 199(C).
    8. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2022. "Misinformation due to asymmetric information sharing," FSES Working Papers 528, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Goutte, Maud-Rose, 2022. "Do actions speak louder than words? Evidence from microblogs," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).
    10. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    11. Wanying Huang & Philipp Strack & Omer Tamuz, 2021. "Learning in Repeated Interactions on Networks," Papers 2112.14265, arXiv.org, revised May 2022.
    12. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2020. "Robust Opinion Aggregation and its Dynamics," Working Papers 662, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    13. Kivinen, Steven & Tumennasan, Norovsambuu, 2019. "Consensus in social networks: Revisited," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 11-18.
    14. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    15. Wang, Pengyu & Fang, Debin & Cao, GangCheng, 2022. "How social learning affects customer behavior under the implementation of TOU in the electricity retailing market," Energy Economics, Elsevier, vol. 106(C).
    16. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    17. Marcos R. Fernandes, 2022. "Confirmation Bias in Social Networks," Papers 2207.12594, arXiv.org, revised Jan 2023.
    18. Lou, Youcheng & Wang, Shouyang, 2021. "The equivalence of two rational expectations equilibrium economies with different approaches to processing neighbors’ information," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 93-105.

  2. 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.

    Cited by:

    1. Furkan Sezer & Hossein Khazaei & Ceyhun Eksin, 2021. "Social Welfare Maximization and Conformism via Information Design in Linear-Quadratic-Gaussian Games," Papers 2102.13047, arXiv.org.
    2. Furkan Sezer & Ceyhun Eksin, 2022. "Information Preferences of Individual Agents in Linear-Quadratic-Gaussian Network Games," Papers 2203.13056, arXiv.org.
    3. Hüning, Hendrik & Meub, Lukas, 2015. "Optimal public information dissemination: Introducing observational learning into a generalized beauty contest," University of Göttingen Working Papers in Economics 260, University of Goettingen, Department of Economics.
    4. Matthew O. Jackson & Brian W. Rogers & Yves Zenou, 2016. "Networks: An Economic Perspective," Papers 1608.07901, arXiv.org.
    5. Edward Anderson & David Gamarnik & Anton Kleywegt & Asuman Ozdaglar, 2016. "Preface to the Special Issue on Information and Decisions in Social and Economic Networks," Operations Research, INFORMS, vol. 64(3), pages 561-563, June.
    6. Hüning, Hendrik & Meub, Lukas, 2016. "Optimal public information dissemination: Introducing multiplier effects into a generalized beauty contest," University of Göttingen Working Papers in Economics 260 [rev.], University of Goettingen, Department of Economics.
    7. Hüning, Hendrik & Meub, Lukas, 2015. "Optimal public information dissemination: Introducing observational learning into a generalized beauty contest," HWWI Research Papers 169, Hamburg Institute of International Economics (HWWI).

  3. 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. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    2. Matan Harel & Elchanan Mossel & Philipp Strack & Omer Tamuz, 2021. "Rational Groupthink," The Quarterly Journal of Economics, Oxford University Press, vol. 136(1), pages 621-668.
      • Matan Harel & Elchanan Mossel & Philipp Strack & Omer Tamuz, 2014. "Rational Groupthink," Papers 1412.7172, arXiv.org, revised Jun 2020.
    3. Huihui Ding & Marcus Pivato, 2021. "Deliberation and epistemic democracy," Post-Print hal-03637874, HAL.
    4. Edoardo Gallo & Alastair Langtry, 2020. "Social networks, confirmation bias and shock elections," Papers 2011.00520, arXiv.org.
    5. 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.
    6. Eger, Steffen, 2016. "Opinion dynamics and wisdom under out-group discrimination," Mathematical Social Sciences, Elsevier, vol. 80(C), pages 97-107.
    7. Berno Buechel & Tim Hellmann & Stefan Kölßner, 2014. "Opinion Dynamics and Wisdom under Conformity," Working Papers 2014.51, Fondazione Eni Enrico Mattei.
    8. 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.
    9. Benjamin Golub & Stephen Morris, 2020. "Expectations, Networks, and Conventions," Papers 2009.13802, arXiv.org.
    10. Mueller-Frank, Manuel, 2015. "Reaching Consensus in Social Networks," IESE Research Papers D/1116, IESE Business School.
    11. Low, Nicholas Kah Yean & Melatos, Andrew, 2022. "Discerning media bias within a network of political allies and opponents: The idealized example of a biased coin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    12. Pnina Feldman & Yiangos Papanastasiou & Ella Segev, 2019. "Social Learning and the Design of New Experience Goods," Management Science, INFORMS, vol. 65(5), pages 1502-1519, April.
    13. 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.
    14. 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.
    15. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    16. 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.
    17. 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.
    18. 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).
    19. Ionel Popescu & Tushar Vaidya, 2019. "Averaging plus Learning Models and Their Asymptotics," Papers 1904.08131, arXiv.org, revised Oct 2022.
    20. Sebastiano Della Lena & Luca Paolo Merlino, 2021. "Group Identity, Social Learning and Opinion Dynamics," Papers 2110.07226, arXiv.org, revised May 2022.
    21. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    22. Levy, Gilat & Razin, Ronny, 2018. "Information diffusion in networks with the Bayesian Peer Influence heuristic," LSE Research Online Documents on Economics 86554, London School of Economics and Political Science, LSE Library.
    23. Christoph Aymanns & Jakob Foerster & Co-Pierre Georg, 2017. "Fake News in Social Networks," Papers 1708.06233, arXiv.org.
    24. Glass, Catherine A. & Glass, David H., 2021. "Opinion dynamics of social learning with a conflicting source," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    25. Ilan Lobel & Evan Sadler, 2013. "Preferences, Homophily, and Social Learning," Working Papers 13-01, NET Institute.
    26. Matthew Ellman, 2017. "Online Social Networks: Approval by Design," Working Papers 17-18, NET Institute.
    27. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2022. "Misinformation due to asymmetric information sharing," FSES Working Papers 528, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    28. Bar Ifrach & Costis Maglaras & Marco Scarsini, 2012. "Monopoly Pricing in the Presence of Social Learning," Working Papers 12-01, NET Institute, revised Sep 2012.
    29. 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.
    30. 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.
    31. 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.
    32. Rajiv Sethi & Muhamet Yildiz, 2013. "Perspectives, Opinions, and Information Flows," Levine's Working Paper Archive 786969000000000934, David K. Levine.
    33. , & ,, 2015. "Information diffusion in networks through social learning," Theoretical Economics, Econometric Society, vol. 10(3), September.
    34. Fang, Aili, 2021. "The influence of communication structure on opinion dynamics in social networks with multiple true states," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    35. Ozan Candogan & Nicole Immorlica & Bar Light & Jerry Anunrojwong, 2022. "Social Learning under Platform Influence: Consensus and Persistent Disagreement," Papers 2202.12453, arXiv.org.
    36. 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.
    37. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    38. Daron Acemoglu & Asuman E. Ozdaglar & Alireza Tahbaz Salehi, 2015. "Networks, Shocks, and Systemic Risk," Levine's Bibliography 786969000000001187, UCLA Department of Economics.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. Li, Wei & Tan, Xu, 2020. "Locally Bayesian learning in networks," Theoretical Economics, Econometric Society, vol. 15(1), January.
    44. Francesco Drago & Friederike Mengel & Christian Traxler, 2015. "Compliance Behavior in Networks: Evidence from a Field Experiment," CSEF Working Papers 419, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    45. Jan Hązła & Ali Jadbabaie & Elchanan Mossel & M. Amin Rahimian, 2021. "Bayesian Decision Making in Groups is Hard," Operations Research, INFORMS, vol. 69(2), pages 632-654, March.
    46. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2020. "Robust Opinion Aggregation and its Dynamics," Working Papers 662, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    47. Kivinen, Steven & Tumennasan, Norovsambuu, 2019. "Consensus in social networks: Revisited," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 11-18.
    48. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    49. 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.
    50. Wang, Pengyu & Fang, Debin & Cao, GangCheng, 2022. "How social learning affects customer behavior under the implementation of TOU in the electricity retailing market," Energy Economics, Elsevier, vol. 106(C).
    51. Marina Azzimonti & Marcos Fernandes, 2018. "Social Media Networks, Fake News, and Polarization," NBER Working Papers 24462, National Bureau of Economic Research, Inc.
    52. 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.
    53. Xiyang Hu & Yan Huang & Beibei Li & Tian Lu, 2022. "Uncovering the Source of Machine Bias," Papers 2201.03092, arXiv.org.
    54. Gallo, E. & Langtry, A., 2020. "Social Networks, Confirmation Bias and Shock Elections," Cambridge Working Papers in Economics 2099, Faculty of Economics, University of Cambridge.
    55. 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.
    56. Low, Nicholas Kah Yean & Melatos, Andrew, 2022. "Vacillating about media bias: Changing one’s mind intermittently within a network of political allies and opponents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    57. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    58. Marcos Fernandes, 2019. "Confirmation Bias in Social Networks," Department of Economics Working Papers 19-05, Stony Brook University, Department of Economics.
    59. 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.
    60. 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.
    61. Lou, Youcheng & Wang, Shouyang, 2021. "The equivalence of two rational expectations equilibrium economies with different approaches to processing neighbors’ information," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 93-105.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-DGE: Dynamic General Equilibrium (1) 2019-09-30

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