Overinference from Weak Signals and Underinference from Strong Signals

TL;DR

This paper demonstrates that people tend to overinfer from weak signals and underinfer from strong signals across various environments, based on a model of belief updating.

Contribution

It introduces a model explaining belief revision behavior and empirically tests it across multiple real-world and experimental settings.

Findings

Overinference from weak signals observed

Underinference from strong signals observed

Findings consistent across diverse environments

Abstract

When people receive new information, sometimes they revise their beliefs too much, and sometimes too little. In this paper, we show that a key driver of whether people overinfer or underinfer is the strength of the information. Based on a model in which people know which direction to update in, but not exactly how much to update, we hypothesize that people will overinfer from weak signals and underinfer from strong signals. We then test this hypothesis across four different environments: abstract experiments, a naturalistic experiment, sports betting markets, and financial markets. In each environment, our consistent and robust finding is overinference from weak signals and underinference from strong signals. Our framework and findings can help harmonize apparently contradictory results from the experimental and empirical literatures.