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The mechanics of contentious politics: an agent-based modeling approach

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  • Eugenio Dacrema
  • Stefano Benati

Abstract

“Contentious politics” has become the main label to define a wide range of previously separated fields of research encompassing topics such as collective action, radicalization, armed insurgencies, and terrorism. Over the past two decades, scholars have tried to bring these various strands together into a unified field of study. In so doing, they have developed a methodology to isolate and analyze the common social and cognitive mechanisms underlying several diverse historical phenomena such as “insurgencies,” “revolutions,” “radicalization,” or “terrorism.” A multidisciplinary approach was adopted open to contributions from diverse fields such as economics, sociology, and psychology. The aim of this paper is to add to the multidisciplinarity of the field of Contentious Politics (CP) and introduce the instruments of Agent-Based Modeling and network game-theory to the study of some fundamental mechanisms analyzed within this literature. In particular, the model presented in this paper describes the dynamics of one process, here defined as “the radicalization of politics,” and its main underlying mechanisms. Their mechanics are analyzed in diverse social contexts differentiated by the values of four parameters: the extent of repression, inequality, social tolerance, and interconnectivity. The model can be used to explain the basic dynamics underlying different phenomena such as the development of radicalization, populism, and popular rebellions. In the final part, different societies characterized by diverse values of the aforementioned four parameters are tested through Python simulations, thereby offering an overview of the different outcomes that the mechanics of our model can shape according to the contexts in which they operate.

Suggested Citation

  • Eugenio Dacrema & Stefano Benati, 2020. "The mechanics of contentious politics: an agent-based modeling approach," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 44(3), pages 163-198, July.
  • Handle: RePEc:taf:gmasxx:v:44:y:2020:i:3:p:163-198
    DOI: 10.1080/0022250X.2020.1753187
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    Cited by:

    1. Loretta Mastroeni & Maurizio Naldi & Pierluigi Vellucci, 2023. "Personal Finance Decisions with Untruthful Advisors: An Agent-Based Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1477-1522, April.

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