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Applications of the bias-variance decomposition to human forecasting

Authors
Patrick Kane
McGill University ~ McGill University
Stephen Broomell
Carnegie Mellon University ~ School of Decision Sciences
Abstract

Forecasts are generated by both human experts and statistical models, and their forecast accuracy can be understood using error decompositions. However, the assumptions that underlie decompositions used in the analysis of human error differ substantially from those used in the analysis of models. The lens model, one of the most popular error decompositions for human errors, treats the beliefs of the human forecaster as fixed parameters to be estimated. Modern decompositions of model error treat the model as a random result from the process of fitting to noisy data. We highlight how these different approaches can be combined, expanding the application of the lens model to groups and opening up new perspectives on the study of human forecasting. We argue that treating human beliefs as the result of a process of learning from noisy data (even without specifying that process) can help to explain many documented phenomena in the world of forecasting such as: what kinds of environments human judgment will have difficulty with and what kinds they will be successful in; what conditions underlie the success of bootstrapping and aggregation of independent forecasts. Just as understanding statistical models as random variables has helped to improve the understanding of error in statistics and machines learning, we believe this framework will be able to help guide the literature on human judgment to a better understanding of error, its determinants and the mechanisms capable of improving forecasting accuracy.

Tags

Keywords

Forecasting
Judgment
Statistical Models
Bias-variance decomposition
Lens Model

Topics

Learning
Theory development
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