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Agent-based models, statistics reveal the turning point of revolutions

Date:
April 26, 2016
Source:
Ca' Foscari University of Venice
Summary:
Computational models and statistics can shed light on why several recent cases of violent revolts and sustained periods of turmoil in North Africa and in the Middle East resulted in entirely different outcomes, ranging from successful revolutions in Tunisia to everlasting civil unrest in Libya and Syria. The difference lies in the timing of organized opposition groups’ intervention.
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Computational models and statistics can shed light on why several recent cases of violent revolts and sustained periods of turmoil in North Africa and in the Middle East resulted in entirely different outcomes, ranging from successful revolutions in Tunisia to everlasting civil unrest in Libya and Syria. The difference lies in the timing of organized opposition groups' intervention.

Alessandro Moro, a young statistician enrolled in the PhD program in Economics at Ca' Foscari University of Venice, studied a unified model capable of generating this multiplicity of outcomes. In a field where social scientists and economists abound, Moro was able to show that political instability can surprisingly be analyzed using heterogeneous agents' models, simulations and statistics. The work, "Understanding the Dynamics of Violent Political Revolutions in an Agent-Based Framework," was published in PLOS ONE.

Three distinct outcomes can be identified by running Moro's model: a successful revolution in which all "loyal policemen" are removed by revolutionaries, leading to an overthrow of the central government (e.g., Tunisia); a failed revolution followed by a state of permanent anarchy due to the large number of policemen killed (e.g., Libya or Syria); a completely failed revolution and a relatively quick return to the status quo after the uprising (e.g., Bahrain).

"It may be easy to obtain different outcomes with distinct models or using several sets of assumptions. However, it is remarkable to be able to get them all in a one-size-fits-all model" commented the mathematician Paolo Pellizzari, who supervised Moro's research.

The paper demonstrates that in terms of institutions and political systems, similar countries may experience revolutionary events at different points in time, or some may not experience revolutions at all.

Even more importantly, despite the impossibility to predict with certainty when and which riots will degenerate into a revolution, simulations show that the timing of organized opposition movements as they enter into supporting popular unrest has a significant role in deciding the ultimate fate of the insurrection. Early interventions, at a time when mass support is still low, put them in the "line of fire" of governmental forces. At the other extreme, waiting for too long may never trigger scattered riots into a full-blown success. According to the model, effective timing must subtly balance the two.


Story Source:

Materials provided by Ca' Foscari University of Venice. Note: Content may be edited for style and length.


Journal Reference:

  1. Alessandro Moro. Understanding the Dynamics of Violent Political Revolutions in an Agent-Based Framework. PLOS ONE, 2016; 11 (4): e0154175 DOI: 10.1371/journal.pone.0154175

Cite This Page:

Ca' Foscari University of Venice. "Agent-based models, statistics reveal the turning point of revolutions." ScienceDaily. ScienceDaily, 26 April 2016. <www.sciencedaily.com/releases/2016/04/160426092124.htm>.
Ca' Foscari University of Venice. (2016, April 26). Agent-based models, statistics reveal the turning point of revolutions. ScienceDaily. Retrieved May 23, 2017 from www.sciencedaily.com/releases/2016/04/160426092124.htm
Ca' Foscari University of Venice. "Agent-based models, statistics reveal the turning point of revolutions." ScienceDaily. www.sciencedaily.com/releases/2016/04/160426092124.htm (accessed May 23, 2017).

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