Reference class forecasting

Futures studies
Concepts
Accelerating change Cashless society Existential risk Future Earth Mathematics Race Climate Space exploration Universe Historical materialism Kondratiev cycle Kardashev scale Moore's law Peak oil Population cycle Resource depletion Singularity Swanson's law
Techniques
Backcasting Causal layered analysis Chain-linked model Consensus forecast Cross impact analysis Delphi Real-time Delphi Foresight Future-proof Futures wheel Futures workshops Horizon scanning Reference class forecasting Scenario planning Systems analysis Threatcasting Trend analysis
Technology assessment and forecasting
Critical design Design fiction Exploratory engineering FTA Hype cycle Science fiction prototyping Speculative design TRL Technology scouting
v t e

Reference class forecasting or comparison class forecasting is a method of predicting the future by looking at similar past situations and their outcomes. The theories behind reference class forecasting were developed by Daniel Kahneman and Amos Tversky. The theoretical work helped Kahneman win the Nobel Prize in Economics.

Reference class forecasting is so named as it predicts the outcome of a planned action based on actual outcomes in a reference class of similar actions to that being forecast.

Discussion of which reference class to use when forecasting a given situation is known as the reference class problem.