Reference Class Forecasting: Resolving Its Challenge to Statistical Modeling

Abstract

Statisticians generally consider statistical modeling superior (or at least a useful supplement) to experience-based intuition for estimating the outputs of a complex system. But recent psychological research has led to an enhancement of experience-based intuition known as reference class forecasting. The reference class forecasting approach has been championed as a superior alternative to statistical modeling and is already well-regarded in the planning community. This presents a challenge to statistical modeling. To address this challenge, this article uses a Bayesian approach for combining the reference class forecast and the model-based forecast. The Bayesian prior is informed by the reference class information. A likelihood function was constructed to reflect the model’s information. This approach was used to estimate healthcare costs under a voluntary employee benefit association (VEBA). The resulting Bayesian posterior forecast had lower variance (and lower forecast error) than either the model-based forecast or the reference-class forecast.

• Applications and case studies
• Bayesian methods
• Computer experiments
• Healthcare
• Inference
• Probability

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