Confidence biases and learning among intuitive Bayesians

TL;DR

This paper investigates how people develop overconfidence faster than their true ability through a game-based experiment and models confidence biases within an intuitive Bayesian framework, explaining persistent misjudgments.

Contribution

It introduces an intuitive Bayesian model that explains confidence biases and learning dynamics, linking overconfidence and other biases to Bayesian updating with illusory signals.

Findings

People learn overconfidence faster than true ability.

Confidence biases can be explained by Bayesian updating with illusory signals.

Biases tend to persist due to error accumulation in Bayesian inference.

Abstract

We design a double-or-quits game to compare the speed of learning one's specific ability with the speed of rising confidence as the task gets increasingly difficult. We find that people on average learn to be overconfident faster than they learn their true ability and we present an intuitive-Bayesian model of confidence which integrates confidence biases and learning. Uncertainty about one's true ability to perform a task in isolation can be responsible for large and stable confidence biases, namely limited discrimination, the hard--easy effect, the Dunning--Kruger effect, conservative learning from experience and the overprecision phenomenon (without underprecision) if subjects act as Bayesian learners who rely only on sequentially perceived performance cues and contrarian illusory signals induced by doubt. Moreover, these biases are likely to persist since the Bayesian aggregation of past information consolidates the accumulation of errors and the perception of contrarian illusory signals generates conservatism and under-reaction to events. Taken together, these two features may explain why intuitive Bayesians make systematically wrong predictions of their own performance.