Minimizing Errors or Surprises?

TL;DR

This paper investigates whether individuals minimize prediction errors or surprises in forecasting, finding that students tend to minimize surprises, supporting the Reference Model Based Learning hypothesis, while professional forecasters do not.

Contribution

It provides empirical evidence favoring surprise minimization over error minimization in student forecasting behavior, contrasting with professional forecasters.

Findings

Students minimize surprises in forecasting tasks.

Professional forecasters do not minimize surprises.

Surprise minimization may be a simple heuristic in complex environments.

Abstract

Traditional finance and macroeconomic models usually assume people can form rational expectations or reach them via a learning path by minimizing prediction errors. The recent Reference Model Based Learning (RMBL) model provides a new perspective: It hypothesizes that people minimize surprises instead of errors. Following the spirit of Simon's "satisficing" criteria, RMBL predicts that they will minimize errors only when the prediction error exceeds a threshold. We conduct meta-analyses based on 18 Learning-to-Forecast Experiments (LtFEs; N=41,490). Our results from the horse race test consistently show that student participants minimize surprises instead of errors in the LtFEs. In contrast, the results based on the data from the Survey of Professional Forecasters (SPF) show no evidence that they minimize surprises. Together, our results suggest that minimizing surprises by implementing RMBL may be the simple procedure people employ when navigating complexity in forecasting.