Ballots, Bayonets, Crowns and Beyond - Dynamics of Political Regimes: Formation, Consolidation and Collapse of Governments

Abstract

In this paper, we describe the formation, consolidation, and collapse of political regimes from quantitative perspective. This approach provides an elaborate context for analysis and qualitative interpretations of the dynamics of political regimes, in a general and consistent manner.  Accordingly, the evolution in the history of political regimes is modeled using mathematical formalism, which provides us with the tools to identify, define and analyze the properties of the regimes' dynamics adequately.

As a frame of reference for the study, we consider the dynamics of political regimes as the processes through which regimes emerge, maintain power, and eventually exit from power. These processes can unfold through a variety of mechanisms, such as electoral transitions, coups, resignations, assassinations (or deaths of leaders from natural causes), and other forms of regime exit.

In the light of the history of political regimes, we hold the view that a regime’s evolution depends more on the present than the past. The past is embedded in the present, which shapes the paths to the future. Thus, we adopt a Markov chain approach to examine the modes of regime emergence, consolidation, and collapse, across different global contexts.

A political regime is modeled as a Markov chain of six states, with a stability spectrum that consists of three classes. The classes S1, S2, and S3 form the stable states class. S4 and S5 form the unstable states class, and S6 is the collapsed state class (the absorbing state). These classes, together with the transition matrix, define the Markov process. We complement this model with the concepts from information theory and physics, such as entropy, conditional entropy, mutual information, symmetry formation and breaking, and phase transitions, which allow us to identify and categorize the properties of both short and long term dynamics.

We performed several simulation experiments, from which the properties of the dynamics, such as symmetry transformations and phase transitions, and their manifestation in forms of: stability, instability and collapse, are identified, measured, and analyzed. Consequently, five types of symmetries — automorphism, approximate, partial, statistical, and temporary — are identified and analyzed. Through the simulations, possible trajectories such as: most likely paths, most used path, middle path and least likely path are identified. Retrodictive analysis, on time-reversed Markov chain dynamics of the regimes, provides us insight about the past, which led to the present conditions.