Political Contagion Modeling

Traditional models of political contagion rely heavily on geographic proximity or narrow institutional classifications. We sought to test a more nuanced hypothesis:

Can structural similarity – across political, economic, historical, and social dimensions – influence the probability of regime failure contagion?

Delivering this required:

• Expanding and harmonising regime-level data across eight decades

• Integrating dozens of contextual variables from heterogeneous sources

• Designing a method to quantify similarity between observations in high-dimensional space

• Managing missingness and scale variance across indicators

• Modeling the time-varying risk of regime failure using appropriate survival frameworks

The core challenge was methodological – converting complex, multi-dimensional political context into a measurable, statistically defensible similarity construct suitable for longitudinal hazard analysis.

We designed and implemented a large-scale data engineering and modeling program to translate political theory into a computationally rigorous analytical system.

Key elements included:

• Building and harmonizing a longitudinal regime dataset spanning nearly a century of political history, covering hundreds of countries and incorporating close to one hundred contextual variables — representing close to one million structured data points

• Engineering a regime-year panel architecture to support time-varying analysis, including structured imputation, transformation, and cross-source standardization

• Designing a high-dimensional geometric similarity model, treating each regime-year as a point in Euclidean space and quantifying contextual proximity across many of dimensions representing political, economic, historical, and social factors

• Implementing machine learning classification techniques to manage dimensional complexity and reduce noise in similarity estimation

• Integrating similarity scores into multivariate regression and survival (hazard) models to evaluate time-to-breakdown dynamics under inter-regime interaction and volatility effects

The result was a scalable, defensible modeling framework capable of quantifying abstract structural similarity and embedding it directly within longitudinal risk analysis.

The project delivered:

• A high-dimensional similarity quantification framework

• An expanded, research-grade regime dataset

• A validated hazard modeling system for time-to-breakdown analysis

• A transferable architecture for studying diffusion, contagion, and systemic risk

Beyond the project's immediate findings, the engagement demonstrated our ability to:

• Translate political theory into computational design

• Engineer complex longitudinal datasets from heterogeneous sources

• Apply machine learning classification to structured policy data

• Integrate geometric modeling with survival analysis

• Deliver academically rigorous yet operationally reusable analytical systems

The resulting framework provides a methodological foundation for future research and policy analysis on regime risk, diffusion dynamics, and structural vulnerability in political systems.