The Logic of Protest Campaigns:
From Empirical Data to Dynamic Models (and Back)
HSE University, Moscow, Russia, email@example.com
elibrary_id: 124097 | ORCID: 0000-0001-8002-7307 | RESEARCHER_ID: L-3000-2015
HSE University, Moscow, Russia, firstname.lastname@example.org
elibrary_id: 1092630 | ORCID: 0000-0002-5209-7655 | RESEARCHER_ID: ABG-2861-2020
Keldysh Institute for Applied Mathematics (Russian Academy of Science), Moscow, Russia, email@example.com
elibrary_id: 15671 | ORCID: 0000-0001-5244-8286 | RESEARCHER_ID: R-6729-2016
This research is supported by the Russian Science Foundation under grant no. 20-18-00274, National Research University Higher School of Economics. We also express our gratitude to S.A. Zheglov, PhD student of Doctoral School of Political Science in National Research University Higher School of Economics, for help in the analysis of scientific literature and datasets, as well as for help in realization of computational model presented in this article in Python programming language.
The most important political protests are long-lasting campaigns where a large number of various interrelated events and actions – both for protesters and for the authorities – intersect. At the same time, empirical research tools – statistical methods and data sets – are more suitable for the study of one-off events, not related to each other in time. In this article, on the broad basis of existing research, the authors demonstrate two characteristic features of this approach – the phenomenon of “over-aggregation” and the problem of “independent events”. In the first case, all parameters of protest episodes are averaged over the campaign as a whole, or over years or months. In the second case, researchers tend to form a sample of such episodes on the assumption that they are not related to each other. Both of these perspectives lead to the internal dynamics of protest campaigns being ignored, failing to take into account a number of their important features. Among such features are the nature of information asymmetry between protesters and authorities, decision-making based on information about the previous protest events (their size on the first place), the systemic effects of “cascades” and “tipping points” in the development of the protest movement, and the effects of learning. We propose agent-based mathematical model to solve these methodological problems and to depict these dynamics. In the model we present the set of potential protesters as a social network; individuals make decisions about participation in “today’s” protest event on the basis of previous events. This approach considers a protest campaign as a chain of events, and enables empirically testable hypotheses to be formulated inferred not only from theory but also from the results of model experiment.
Akhremenko A., Petrov A. 2020. Modeling the Protest-Repression Nexus. – Proceedings of the MACSPro Workshop 2020. Venice, Italy, October 22-24. CEUR Workshop Proceedings.
Akhremenko A., Yureskul E., Petrov A. 2019. Latent Factors of Protest Participation: A Basic Computational Model. – 2019 Twelfth International Conference “Management of Large-Scale System Development” (MLSD). Moscow: IEEE. https://doi.org/10.1109/MLSD.2019.8910999
Ayanian A., Tausch N. 2016. How Risk Perception Shapes Collective Action Intentions in Repressive Contexts: A Study of Egyptian Activists during the 2013 Post-coup Uprising. – British Journal of Social Psychology. Vol. 55. No. 4. P. 700-721. https://doi.org/10.1111/bjso.12164
Ayoub P. 2010. Repressing Protest: Threat and Weakness in the European Context, 1975-1989. – Mobilization: An International Quarterly. Vol. 15. No. 4. P. 465-488. https://doi.org/10.17813/maiq.15.4.f6306060j4511u58
Bell S., Murdie A. 2018. The Apparatus for Violence: Repression, Violent Protest, and Civil War in a Cross-National Framework. – Conflict Management and Peace Science. Vol. 35. No. 4. P. 336-354. https://doi.org/10.1177/0738894215626848
Braithwaite A., Braithwaite J. M., Kucik J. 2015. The Conditioning Effect of Protest History on the Emulation of Nonviolent Conflict. – Journal of Peace Research. Vol. 52. No. 6. P. 697-711. https://doi.org/10.1177/0022343315593993
Buechler S. 2004. The Strange Career of Strain and Breakdown Theories of Collective Action. – The Blackwell Companion to Social Movements. Ed. by D. Snow, S. Soule, H. Kriesi. Malden, MA: Blackwell. P.47-65. https://doi.org/10.1002/9780470999103
Butcher C., Svensson I. 2014. Manufacturing Dissent. Modernization and the Onset of Major Nonviolent Resistance Campaigns. – Journal of Conflict Resolution. Vol. 60. No. 2. P. 311-339. https://doi.org/10.1177/0022002714541843
Carey S. 2006. The Dynamic Relationship between Protest and Repression. – Political Research Quarterly. Vol. 59. No. 1. P. 1-11. https://doi.org/10.1177/106591290605900101
Carey S. 2010. The Use of Repression as a Response to Domestic Dissent. – Political Studies. Vol. 58. No. 1. P. 167-186. https://doi.org/10.1111/j.1467-9248.2008.00771.x
Casella G., Berger R. 2002. Statistical Inference. 2nd Edition. Pacific Grove: Duxbury, Thomson Learning.
Centola D., Becker J., Brackbill D., Baronchelli A. 2018. Experimental Evidence for Tipping Points in Social Convention. – Science. Vol. 360. No. 6393. P. 1116-1119. https://doi.org/10.1126/science.aas8827
Chenoweth E., Belgioioso M. 2019. The Physics of Dissent and the Effects of Movement Momentum. – Nature Human Behavior. Vol. 3. P. 1088-1095. https://doi.org/10.1038/s41562-019-0665-8
Chenoweth E., Lewis O. 2013. Unpacking Nonviolent Campaigns: Introducing the NAVCO 2.0 Dataset. – Journal of Peace Research. Vol. 50. No .3. P. 415-423. https://doi.org/10.1177/0022343312471551
Chenoweth E., Pinckney J., Lewis O. 2018. Days of Rage: Introducing the NAVCO 3.0 Dataset. – Journal of Peace Research. Vol. 55. No. 4. P. 524-534. https://doi.org/10.1177/0022343318759411
Chenoweth E., Stephan M. 2011. Why Civil Resistance Works: The Strategic Logic of Nonviolent Conflict. New York: Columbia University Press.
Clark D., Regan P. 2016. Mass Mobilization Protest Data. – Harvard Dataverse. Version 4. https://doi.org/10.7910/DVN/HTTWYL
Davenport C., Soule C., Armstrong D. 2011. Protesting While Black? The Differential Policing of American Activism, 1960 to 1990. – American Sociological Review. Vol. 76. No. 1. P. 152-178. https://doi.org/10.1177/0003122410395370
Davis D., Leeds B., Moore W. 1998. Measuring Dissident and State Behavior: The Intranational Political Interactions (IPI) Project. – The Workshop on Cross-National Data Collection. Texas A&M University.
Demirel-Pegg T. 2017. The Demobilization of the Protest Campaigns. – Oxford Research Encyclopedia of Politics. https://doi.org/10.1093/acrefore/9780190228637.013.251
Drury J., Reicher S. D. 2000. Collective Action and Psychological Change: The Emergence of New Social Identities. – British Journal of Social Psychology. Vol. 39. No. 4. P. 579-604. https://doi.org/10.1348/014466600164642
Earl J., Soule S., McCarthy J.2003. Protest Under Fire? Explaining the Policing of Protest. – American Sociological Review. Vol. 68. No. 4. P.581-606. https://doi.org/10.2307/1519740
Epstein J.M. 2002. Modeling Civil Violence: An Agent-Based Computational Approach. – Proceedings of the National Academy of Sciences. Vol. 99. No. 3. P. 7243-7250. https://doi.org/10.1073/pnas.092080199
Francisco R. 2009. Dynamics of Conflict. New York: Springer. https://doi.org/10.1007/978-0-387-75242-6
Fonoberova M., Fonoberov V., Mezic I., Mezic J., Brantingham P. 2012. Nonlinear Dynamics of Crime and Violence in Urban Settings. – Journal of Artificial Societies and Social Simulation. Vol. 15. No. 1. https://doi.org/10.18564/jasss.1921
Gerber A., Green D., Kaplan E. 2004. The Illusion of Learning from Observational Research. – Problems and Methods in the Study of Politics. Ed. by I. Shapiro, S. Smith, T. Massoud. New York: Cambridge University Press. P. 251-273.
Girod D., Stewart M., Walters M. 2016. Mass Protests and the Resource Curse: The Politics of Demobilization in Rentier Autocracies. – Conflict Management and Peace Science. Advance. Vol. 35. No. 5. P. 503-522. https://doi.org/10.1177/0738894216651826
Granovetter M. 1978. Threshold Models of Collective Behavior. – The American Journal of Sociology. Vol. 83. No. 6. P. 1420-1443. https://doi.org/10.1086/226707
Grimm J., Harders C. 2018. Unpacking the Effects of Repression: The Evolution of Islamist Repertoires of Contention in Egypt after the Fall of President Morsi. – Social Movement Studies. Vol. 17. No. 1. P. 1-18. https://doi.org/10.1080/14742837.2017.1344547
Gurr T. 1970. Why Men Rebel? Princeton, NJ: Princeton University Press.
Hussain M., Howard Ph. 2013. What Best Explains Successful Protest Cascades? ICTs and the Fuzzy Causes of the Arab Spring. – International Studies Review. Vol. 15. No. 1. P. 18-66. https://doi.org/10.1111/misr.12020
Ives B., Lewis J. 2019. From Rallies to Riots: Why Some Protests Become Violent. – Journal of Conflict Resolution. Vol. 64. No. 5. P. 958-986. https://doi.org/10.1177/0022002719887491
Johnson J., Thyne C. 2018. Squeaky Wheels and Troop Loyalty: How Domestic Protests Influence Coups d’etat, 1951-2005. – Journal of Conflict Resolution. Vol. 62. No. 3. P. 597-625. https://doi.org/10.1177/0022002716654742
Kim J., Hanneman R. 2011. A Computational Model of Worker Protest. – Journal of Artificial Societies and Social Simulation. Vol. 14. No. 3. https://doi.org/10.18564/jasss.1770
Klandermans B. 1984. Mobilization and Participation: Social-Psychological Expansisons of Resource Mobilization Theory. – American Sociological Review. Vol. 49. No. 5. P. 583-600. https://doi.org/10.2307/2095417
Kriesi H., Koopmans R., Duyvendak J., Giugni M. 1995. New Social Movements in Western Europe: A Comparative Analysis. Minneapolis: University of Minnesota Press.
Kuran T. 1989. Sparks and Prairie Fires: A Theory of Unanticipated Political Revolution. – Public Choice. No. 61. P. 41-74. https://doi.org/10.1007/bf00116762
Lemos C. 2018. Agent-Based Modeling of Social Conflict from Mechanisms to Complex Behavior. Switzerland: Springer International Publishing.
Lohmann S. 1994. The Dynamics of Informational Cascades: The Monday Demonstrations in Leipzig, East Germany, 1989-91. – World Politics. Vol. 47. No. 1. P. 42-101. https://doi.org/10.2307/2950679
Makowsky M., Rubin J. 2013. An Agent-Based Model of Centralized Institutions, Social Network Technology, and Revolution. – PLoS ONE. Vol. 8. No.11: e80380. https://doi.org/10.1371/journal.pone.0080380
McAdam D., Tarrow S., Tilly C. 2001. Dynamics of contention. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511805431
Moore W. 1998. Repression and Dissent: Substitution, Context, and Timing. – American Journal of Political Science. Vol. 42. No. 3. P. 851-873. https://doi.org/10.2307/2991732
Moro A. 2016. Understanding the Dynamics of Violent Political Revolutions in an Agent-Based Framework. – PLoS ONE. Vol. 11. No. 4: e0154175. https://doi.org/10.1371/journal.pone.0154175
Nardulli P., Althaus S., Hayes M. 2015. A Progressive Supervised-learning Approach to Generating Rich Civil Strife Data. – Sociological Methodology. Vol. 45. No. 1. P. 148-183. https://doi.org/10.1177/0081175015581378
Opp K-D., Roehl W. 1990. Repression, Micromobilization, and Political Protest. – Social Forces. Vol. 69. No. 2. P. 521-524. https://doi.org/10.2307/2579672
Pierskalla J. 2010. Protest, Deterrence, and Escalation: The Strategic Calculus of Government Repression. – Journal of Conflict Resolution. Vol. 54. No. 1. P. 117-145. https://doi.org/10.1177/0022002709352462
Raleigh C., Linke A., Hegre H., Karlsen J. 2010. Introducing ACLED – Armed Conflict Location and Event Data. – Journal of Peace Research. Vol. 47. No. 5. P. 651-660. https://doi.org/10.1177/0022343310378914
Rasler K. 1996. Concessions, Repression, and Political Protest in the Iranian Revolution. – American Sociological Review. Vol. 61. No. 1. P. 132-152. https://doi.org/10.2307/2096410
Rasler K. 2017. Dynamics, Endogeneity, and Complexity in Protest Campaigns. – Oxford Research Encyclopedia of Politics. URL: https://doi.org/10.1093/acrefore/9780190228637.013.321
Ross B., Pilz L., Cabrera B., Brachten F., Neubaum G., Stieglitz S. 2019. Are Social Bots a Real Threat? an Agent-Based Model of the Spiral of Silence to Analyse the Impact of Manipulative Actors in Social Networks. – European Journal of Information System. Vol. 28. No. 4. P. 394-412. https://doi.org/10.1080/0960085X.2018.1560920
Salehyan I. Cullen S., Hamner J., Case C., Linebarger C., Stull E., Williams J. 2012. Social Conflict in Africa: A New Database. – International Interactions. Vol. 38. No. 4. P. 503-511. https://doi.org/10.1080/03050629.2012.697426
Schelling T. 1969. Models of Segregation. – The American Economic Review. Vol. 59. No. 2. P. 488-493.
Schelling T. 1971. Dynamic Models of Segregation. – Journal of Mathematical Sociology. Vol. 1. P. 143-186. https://doi.org/10.1080/0022250X.1971.9989794
Shadmehr M. 2014. Mobilization, Repression, and Revolution: Grievances and Opportunities in Contentious Politics. – Journal of Politics. Vol. 76. No. 3. P.621-635. https://doi.org/10.1017/S002238161400026
Siegel D. 2011. When Does Repression Work? Collective Action and Social Networks. – Journal of Politics. Vol. 73. No. 4. P. 993-1010.
Soares M., Barbosa M., Matos R., Mendes S. 2018. Public Protest and Police Violence: Moral Disengagement and Its Role in Police Repression of Public Demonstrations in Portugal. – Peace and Conflict: Journal of Peace Psychology. Vol. 24. P. 27-35. https://doi.org/10.1037/pac0000277
Slantchev B., Matush K. 2020. The Authoritarian Wager: Political Action and the Sudden Collapse of Repression. – Comparative Political Studies. Vol. 53. No. 2. P. 214-252.
Sturmer S., Simon B. 2004. The Role of Collective Identification in Social Movement Participation: A Panel Study in the Context of the German Gay Movement. – Personality and Social Psychology Bulletin. Vol. 30. No. 3. P. 263-277. https://doi.org/10.1177/0146167203256690
Sullivan C. 2016. Undermining Resistance: Mobilization, Repression, and the Enforcement of Political Order. – Journal of Conflict Resolution. Vol. 60. No. 7. P. 1163-1190. https://doi.org/10.1177/0022002714567951
Sutton J., Butcher C., Svensson I. 2014. Explaining Political Jiu-Jitsu: Institution-Building and the Outcomes of Regime Violence against Unarmed Protests. – Journal of Peace Research. Vol. 51. No. 5. P. 559-573. https://doi.org/10.1177/0022343314531004
Van Stekelenburg J., Klandermans B. 2017. Individuals in Movements: A Social Psychology of Contention. – Handbook of Social Movements across Disciplines. Ed. by C. Roggeband, B. Klandermans. Cham: Springer. P. 103-139. https://doi.org/10.1007/978-3-319-57648-0
Watts D., Strogatz S. 1998. Collective Dynamics of Small-World Networks. – Nature. Vol. 393. No. 6684. P. 440-442. https://doi.org/10.1038/30918
Weidmann N., Rod E. 2019. Chapter 4: Coding Protest Events in Autocracies. – The Internet and Political Protest in Autocracies. New York: Oxford University Press. P. 35-60.
Will M., Groeneveld J., Frank K., Muller B. 2020. Combining Social Network Analysis and Agent-Based Modelling to Explore Dynamics of Human Interaction: A Review. – Socio-Environmental Systems Modelling. Vol. 2. 16325. https://doi.org/10.18174/sesmo.2020a16325
Wooldridge J. 2013. Introductory Econometrics. A Modern Approach. Mason: South Western.
Akhremenko A., Petrov A., Zheglov S. 2021. How Information and Communication Technologies Change Trends in Political Processes Modeling: Towards an Agent-Based Approach. – Political Science (RU). No. 1. P. 12-45. (In Russ.) https://doi.org/10.31249/poln/2021.01.01
Korotaev A., Isaev L., Vasiliev A. 2015. Quantitative Analysis of 2013-2014 Revolutionary Wave. – Sociological Studies. No. 8. P.119-127. (In Russ.) URL: http://socis.isras.ru/article/5621 (accessed 11.03.2021).
LITEX. Vol. 4. No. 2. P. 80-100. (In Russ.)
Rozov N. 2017. Crisis and Revolution: Fields of Interaction, Actors’ Strategies, and Trajectories of Conflict Dynamics. – Polis. Political Studies. No. 6. P. 92-108. (In Russ.) https://doi.org/10.17976/jpps/2017.06.07
Akhremenko A.S., Petrov A.P.,
Anger, identity or efficacy belief? Dynamics of motivation and participation in 2020 Belarusian protests. – Polis. Political Studies. 2023. No2
Systemico-Synergetic Model of a Political System. – Polis. Political Studies. 2009. No3
Melville A.Yu., Malgin A.V., Mironyuk M.G., Stukal D.K.,
Empirical challenges and methodological approaches in comparative politics (through the lens of the Political Atlas of the Modern World 2.0). – Polis. Political Studies. 2023. No5
The Stanford Model of Development Crisis. – Polis. Political Studies. 2009. No3
The Functioning of the Two-Level Models of the Local Self-Government Territorial Organization in RF (With the Voronezhskaya and Orlovskaya Regions as Example). – Polis. Political Studies. 2006. No3