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Global Civil Unrest: Contagion, Self-Organization, and Prediction

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  • Dan Braha

Abstract

Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, records of civil unrest of 170 countries during the period 1919–2008 are analyzed. It is demonstrated that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.

Suggested Citation

  • Dan Braha, 2012. "Global Civil Unrest: Contagion, Self-Organization, and Prediction," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-9, October.
    • Handle: RePEc:plo:pone00:0048596
    DOI: 10.1371/journal.pone.0048596
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    Cited by:

    1. Alessandro Moro, 2016. "Understanding the Dynamics of Violent Political Revolutions in an Agent-Based Framework," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-17, April.
    2. Sergei Petrovskii & Weam Alharbi & Abdulqader Alhomairi & Andrew Morozov, 2020. "Modelling Population Dynamics of Social Protests in Time and Space: The Reaction-Diffusion Approach," Mathematics, MDPI, vol. 8(1), pages 1-19, January.
    3. Caroline T. Witte & Martijn J. Burger & Elena Ianchovichina, 2020. "Subjective Well‐Being and Peaceful Uprisings," Kyklos, Wiley Blackwell, vol. 73(1), pages 120-158, February.
    4. Jose Cadena & Gizem Korkmaz & Chris J Kuhlman & Achla Marathe & Naren Ramakrishnan & Anil Vullikanti, 2015. "Forecasting Social Unrest Using Activity Cascades," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-27, June.
    5. David Anzola & Peter Barbrook-Johnson & Juan I. Cano, 2017. "Self-organization and social science," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 221-257, June.
    6. Zeeshan Ahmed & Shahid Rasool & Qasim Saleem & Mubashir Ali Khan & Shamsa Kanwal, 2022. "Mediating Role of Risk Perception Between Behavioral Biases and Investor’s Investment Decisions," SAGE Open, , vol. 12(2), pages 21582440221, May.
    7. Mumtaz Hussain & Salma Sadiq & Muhammad Haroon Rasheed & Khurram Amin, 2022. "Exploring the Dynamics of Investors’ Decision Making in Pakistan Stock Market: A Study of Herding Behavior," Journal of Economic Impact, Science Impact Publishers, vol. 4(1), pages 165-173.
    8. Zain UI Abideen & Zeeshan Ahmed & Huan Qiu & Yiwei Zhao, 2023. "Do Behavioral Biases Affect Investors’ Investment Decision Making? Evidence from the Pakistani Equity Market," Risks, MDPI, vol. 11(6), pages 1-32, June.
    9. Braha, Dan & de Aguiar, Marcus A. M., 2018. "Voting contagion: Modeling and analysis of a century of U.S. presidential elections," SocArXiv mzxnr, Center for Open Science.
    10. Merve Alanyali & Tobias Preis & Helen Susannah Moat, 2016. "Tracking Protests Using Geotagged Flickr Photographs," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-8, March.
    11. Suresh G., 2024. "Impact of Financial Literacy and Behavioural Biases on Investment Decision-making," FIIB Business Review, , vol. 13(1), pages 72-86, January.

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