Bayesian Methods

This study asks how economic and geospatial factors shape different types of conflict within Nigeria and presents a high-resolution risk methodology that combines spatial econometrics with the greed–grievance framework. Using a Bayesian spatial approach, the authors model how socio-economic development, natural resources, energy infrastructure and multiple spatial predictors relate to four conflict typologies over 1997–2023, producing predictive risk maps intended to inform planning and stabilization.

Current methods for measuring international norms often rely on single indicators or case studies, limiting cross-national analysis.

Media freedom's role in democracy is well-established but often oversimplified with single indicators. This article analyzes ten existing measures to capture its complexity.

Peace agreements are notoriously fragile in the post-conflict period, but measuring their inherent strength has been elusive. Existing approaches either lack nuance or rely on subjective weighting.

Governments often gain responsibility for ongoing economic issues with time in office, potentially increasing voter accountability. However, if voters are Bayesian learners and accumulate information about government competence over tenure, this accumulated knowledge might crowd out the influence of current economic conditions on voting decisions.

Spatial econometric models are increasingly popular across political science but require specifying a dependence network (W) before estimation. The true structure of W is rarely known, and theories often provide little guidance, producing a form of model uncertainty labeled here as network uncertainty.