Pollinges

Pollinges is a term used in political data science to describe both a data construct and the methodological approach of producing a probabilistic forecast from poll data. In this framework, pollinges refer to an ensemble of simulated poll outcomes, derived by integrating multiple polls and treating uncertainty about sampling, timing, and turnout as stochastic components. The resulting object provides a distribution for public opinion rather than a single point estimate.

Construction involves collecting polls from multiple organizations, aligning questions, adjusting for timing, and applying statistical models such as Bayesian hierarchical models or bootstrap procedures to generate a set of plausible poll outcomes. This process yields a collection of forecasts that reflect both sampling error and model-based uncertainty, from which estimates and prediction intervals can be derived.

Origin and scope: The term appears in academic discussions during the 2010s and 2020s as researchers sought to formalize ensemble approaches to polling. It is not widely used outside specialist literature, but it has appeared in forecasting software and methodological papers to describe the ensemble approach rather than a single poll.

Applications: Pollinges are used to compute prediction intervals, conduct scenario analyses under different turnout assumptions, and compare forecast robustness across pollsters. They aim to enhance transparency about uncertainty and to reduce reliance on narrow point estimates in political forecasting.

Limitations and criticism: The reliability of pollinges depends on data quality and model assumptions. Critics warn that ensemble outputs can be misinterpreted as precise forecasts if the inherent uncertainty is not clearly communicated. See also public opinion polling, ensemble methods, Bayesian statistics.