No-one to Blame: Biased Updating without Attribution
Speaker: Alexander Coutts (Nova School of Business and Economics)
Date: Nov 7, Wednesday, 2018
Time: 12 pm - 1 pm
Venue: Room 1300 at Pudong Campus
Abstract:
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. Feedback in most contexts comes bundled with other dimensions of uncertainty. We consider a natural context of updating with two dimensions of uncertainty, two person teams, where the ability of each teammate is unknown. We distinguish two theories of self-attribution 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 their teammate. Our study is the first to be able to distinguish these types of attribution biases. In fact we find that neither explains the patterns observed. Individuals are overconfident, and asymmetrically biased in their updating. However they significantly under-weight negative signals, without attribution to either teammate.
Speaker’s website:
http://alexandercoutts.com/
Speaker: Alexander Coutts (Nova School of Business and Economics)
Date: Nov 7, Wednesday, 2018
Time: 12 pm - 1 pm
Venue: Room 1300 at Pudong Campus
Abstract:
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. Feedback in most contexts comes bundled with other dimensions of uncertainty. We consider a natural context of updating with two dimensions of uncertainty, two person teams, where the ability of each teammate is unknown. We distinguish two theories of self-attribution 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 their teammate. Our study is the first to be able to distinguish these types of attribution biases. In fact we find that neither explains the patterns observed. Individuals are overconfident, and asymmetrically biased in their updating. However they significantly under-weight negative signals, without attribution to either teammate.
Speaker’s website:
http://alexandercoutts.com/