Resolution criteria

This market resolves to YES if, at any point before January 1, 2100, probabilistic forecasting methodology ceases to exist as an active field of practice and research.

Specifically, this requires that:

1. Probabilistic predictions (assigning calibrated numerical probabilities or probability distributions to future uncertain events) are no longer systematically utilized, published, or taught across major domains, including meteorology/climatology, finance/econometrics, public health, and statistical forecasting; OR

2. A formal paradigm shift or technological development renders explicit probabilistic modeling entirely obsolete, unused, and abandoned in mainstream science and institutional decision-making.

This market resolves to NO if, by January 1, 2100, probabilistic forecasting methods (e.g., ensemble weather forecasting, Bayesian predictive distributions, quantitative risk assessment, or prediction markets) remain actively researched, applied, or recognized as a valid methodology.

If civilizational collapse, human extinction, or an equivalent existential catastrophe permanently halts institutional scientific reporting before 2100, this market resolves to N/A unless unambiguous consensus among remaining market participants indicates the criteria were met prior to the event.

Note: The market creator may clarify or refine specific domain thresholds prior to market close.

Background

Probabilistic forecasting quantifies uncertainty by providing probability distributions or confidence levels over potential outcomes, rather than point (deterministic) forecasts. It is standard practice in fields such as:

• Numerical weather prediction, where ensemble forecasts model atmospheric chaos.

• Epidemiology and public health, modeling disease trajectory scenarios.

• Economics and quantitative finance, assessing portfolio risk (e.g., Value at Risk) and macroeconomic distributions.

• Superforecasting and prediction markets, where crowd aggregation and Bayesian updating evaluate geopolitical, technological, and socio-economic risks.

Arguments anticipating obsolescence generally posit radical shifts such as deterministic mastery via superintelligence, fundamental epistemological shifts in scientific paradigms, or the displacement of numerical probability by alternative uncertainty-handling frameworks.