volebatis

Volebatis is a term used in political data science to describe an open-source framework for analyzing elections and voter behavior. It provides a modular toolset to ingest election results, demographic data, and polling, and to produce historical analyses as well as forward-looking forecasts. The approach emphasizes transparency, reproducibility, and peer-reviewed modeling practices.

The methodology combines Bayesian statistics with hierarchical modeling to estimate turnout, party support, and issue salience, while allowing scenario analysis under different assumptions and data inputs. It is designed to be extensible across jurisdictions and election types.

Core features include a data pipeline for cleaning and harmonizing sources, probabilistic forecasting, model comparison and performance tracking, and visualization interfaces that support scenario planning and sensitivity analysis. It supports reproducible research through versioning, documentation, and open data policies.

Volebatis has been used in academic exercises, civic tech projects, and policy simulations to explore how changes in demographics, turnout, or electoral rules might influence outcomes. Critics emphasize the limits of data quality, model assumptions, and the risk of overconfidence in forecasts.

Related topics include election forecasting, Bayesian statistics, and political data science.