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A model for the competition between political mono-polarization and bi-polarization
Nicolas Saintier1, Juan Pablo Pinasco1, Federico Vazquez2
1Departamento de Matemática and IMAS, UBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires Pabellón I, Ciudad Universitaria, Buenos Aires C1428EGA, Argentina.
Chaos (Woodbury, N.Y.)
|July 3, 2020
Summary
This study models political polarization dynamics. It finds that bi-polarization is stable at lower nonlinearity (q
Area of Science:
- Computational Social Science
- Statistical Physics
- Political Science
Background:
- Political polarization is a significant societal phenomenon.
- Agent-based models are crucial for understanding collective behavior dynamics.
- Previous models explored voting intention but lacked detailed analysis of polarization stability.
Purpose of the Study:
- To investigate the conditions favoring political bi-polarization versus mono-polarization.
- To analyze the impact of interaction nonlinearity on polarization states.
- To determine the critical transition point between bi-polarization and mono-polarization.
Main Methods:
- Utilized a generalized agent-based model for voting intention dynamics.
- Introduced a nonlinearity parameter (q) to modulate agent interactions.
- Employed rate equation analysis and continuum limit transport equations.
- Conducted Monte Carlo simulations to validate findings and measure transition times.
Main Results:
- Identified a critical nonlinearity parameter (q_c) separating bi-polarization and mono-polarization.
- Found bi-polarization stable for q < q_c and mono-polarization for q > q_c.
- Demonstrated that q_c decreases with increasing interaction strength (h).
- Observed scaling of mean transition time (τ) with system size (N) at q_c.
Conclusions:
- The system's polarization state is highly sensitive to the nonlinearity of agent interactions.
- Bi-polarization is favored under conditions of weaker interaction nonlinearity.
- Mono-polarization emerges as the dominant state with increasing interaction nonlinearity.
- The transition dynamics are further elucidated by analytical and simulation approaches.
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