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When Is Society Susceptible to Manipulation?

Author

Listed:
  • Mohamed Mostagir

    (Ross Business School, University of Michigan, Ann Arbor, Michigan 48109)

  • Asuman Ozdaglar

    (Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • James Siderius

    (Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

We consider a social learning model where agents learn about an underlying state of the world from individual observations as well as from exchanging information with each other. A principal (e.g., a firm or a government) interferes with the learning process in order to manipulate the beliefs of the agents. By utilizing the same forces that give rise to the “wisdom of the crowd” phenomenon, the principal can get the agents to take an action that is not necessarily optimal for them but is in the principal’s best interest. We characterize the social norms and network structures that are susceptible to this kind of manipulation and derive conditions under which a social network is impervious and cannot be manipulated. In the process, we develop a new centrality measure and describe how our model offers insights into designing networks that are resistant to manipulation.

Suggested Citation

  • Mohamed Mostagir & Asuman Ozdaglar & James Siderius, 2022. "When Is Society Susceptible to Manipulation?," Management Science, INFORMS, vol. 68(10), pages 7153-7175, October.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:10:p:7153-7175
    DOI: 10.1287/mnsc.2021.4265
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Zafer Kanik & Zaruhi Hakobyan, 2026. "Strategic Expression, Popularity Traps, and Welfare in Social Media," Papers 2601.01370, arXiv.org, revised Mar 2026.
    2. Mohamed Mostagir & James Siderius, 2023. "Strategic Reviews," Management Science, INFORMS, vol. 69(2), pages 904-921, February.
    3. Elina H. Hwang & Stephanie Lee, 2025. "A Nudge to Credible Information as a Countermeasure to Misinformation: Evidence from Twitter," Information Systems Research, INFORMS, vol. 36(1), pages 621-636, March.
    4. Sanghyun Park & Phanish Puranam, 2024. "Vicarious Learning Without Knowledge Differentials," Management Science, INFORMS, vol. 70(5), pages 2999-3019, May.
    5. Mohamed Mostagir & James Siderius, 2023. "Social Inequality and the Spread of Misinformation," Management Science, INFORMS, vol. 69(2), pages 968-995, February.
    6. Guo, Feng & Li, Qing & Fan, Qihui & Zheng, Qiyun, 2026. "Does too much or too little entrepreneurial experience benefit crowdfunding performance? A social learning theory perspective," Journal of Business Research, Elsevier, vol. 203(C).
    7. Kimon Drakopoulos & Ali Makhdoumi, 2023. "Providing Data Samples for Free," Management Science, INFORMS, vol. 69(6), pages 3536-3560, June.
    8. Yanzhe (Murray) Lei & Sentao Miao & Ruslan Momot, 2024. "Privacy-Preserving Personalized Revenue Management," Management Science, INFORMS, vol. 70(7), pages 4875-4892, July.
    9. Della Lena, Sebastiano, 2024. "The spread of misinformation in networks with individual and social learning," European Economic Review, Elsevier, vol. 168(C).
    10. Mohamed Mostagir & James Siderius, 2022. "Learning in a Post-Truth World," Management Science, INFORMS, vol. 68(4), pages 2860-2868, April.

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