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Herding, social influence and expert opinion

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  • Michelle Baddeley

Abstract

This paper analyzes the impact of social influences on opinion formation among academics and other experts. Some social influences are valuable – for example, replicating results is a valuable aspect of scientific research and if a hypothesis has genuinely been verified across a range of different studies, then that may be because it is more probable. In uncertain situations, however, people employ heuristics and rules of thumb to guide their interpretation of events, and this can create problems of cognitive bias, including group biases when beliefs tend to coincide with the prior opinions of others, thus creating herding and path dependency. This tendency to follow others may be magnified by other social influences including reputation-building and conformity preference. Insights about herding and social influence are used to build a model of relative rewards to consensus versus contrarianism. This paper concludes with an analysis of implications and policies designed to moderate the negative herding externalities.

Suggested Citation

  • Michelle Baddeley, 2013. "Herding, social influence and expert opinion," Journal of Economic Methodology, Taylor & Francis Journals, vol. 20(1), pages 35-44.
    • Handle: RePEc:taf:jecmet:v:20:y:2013:i:1:p:35-44
    DOI: 10.1080/1350178X.2013.774845
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    Cited by:

    1. Richard Arena & Eric Nasica, 2021. "Keynes's Methodology and the Analysis of Economic Agent Behavior in a Complex World," GREDEG Working Papers 2021-10, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    2. Altman, Morris, 2020. "A more scientific approach to applied economics: Reconstructing statistical, analytical significance, and correlation analysis," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 315-324.
    3. Alexander Lord & Yiquan Gu, 2019. "Can the market be tamed? A thought experiment on the value(s) of planning," Environment and Planning A, , vol. 51(1), pages 11-24, February.
    4. Michael P. Schlaile & Johannes Zeman & Matthias Mueller, 2021. "It’s a Match! Simulating Compatibility-based Learning in a Network of Networks," Economic Complexity and Evolution, in: Michael P. Schlaile (ed.), Memetics and Evolutionary Economics, chapter 0, pages 99-140, Springer.
    5. Ali, Mazhar & Amir, Dr.Huma & Shamsi, Dr.Aamir, 2021. "Consumer Herding Behavior in Online Buying: A Literature Review," MPRA Paper 107435, University Library of Munich, Germany.
    6. David Dequech, 2016. "Some Institutions (Social Norms And Conventions) Of Contemporary Mainstream Economics, Macroeconomics, And Financial Economics," Anais do XLIII Encontro Nacional de Economia [Proceedings of the 43rd Brazilian Economics Meeting] 006, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    7. Morris Altman, 2020. "Smart thinking, lockdown and COVID-19: Implications for public policy," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 4(S), pages 23-33, June.
    8. Michelle Baddeley, 2017. "Experts in policy land - Insights from behavioral economics on improving experts' advice for policy-makers," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 1(1), pages 27-31, February.

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