No one to blame: Biased belief updating without attribution (with Leonie Gerhards and Zahra Murad)
A growing body of evidence suggests that individuals are on average overconfident about their ability, affecting career and financial decisions, among others. We investigate how overconfidence may persist in the face of objective feedback. Self-attribution biases are said to exist when we take credit for good outcomes, but blame poor outcomes on external factors. While heavily studied in social psychology, and often referenced in economics, rigorous evidence is scarce. We present a modified Bayesian model of self-attribution bias, which gives testable predictions for two types of this bias, (1) noisy: which generates positive asymmetric updating about self, and as such has been studied previously, and (2) fundamental: individuals mis-attribute positive feedback to themselves, and negative feedback to an external fundamental. We test the theory in an experiment where subjects are matched into two person teams. Individuals are overconfident, and take too much credit for positive feedback. However, both types of self-attribution bias are rejected, as subjects significantly under-weight negative feedback, without attribution to either teammate.