Hindsight Bias Impedes Learning

Shaudi Mahdavi, M. Amin Rahimian
; Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, PMLR 58:111-127, 2017.

Abstract

We propose a model that addresses an open question in the cognitive science literature: How can we rigorously model the cognitive bias known as hindsight bias such that we fully account for critical experimental results? Though hindsight bias has been studied extensively, prior work has failed to produce a consensus theoretical model sufficiently general to account for several key experimental results, or to fully demonstrate how hindsight impedes our ability to learn the truth in a repeated decision or social network setting. We present a model in which agents aim to learn the quality of their signals through repeated interactions with their environment. Our results indicate that agents who are subject to hindsight bias will always believe themselves to be high-type “expert” regardless of whether they are actually high- or low-type.

Cite this Paper


BibTeX
@InProceedings{pmlr-v58-mahdavi17a, title = {Hindsight Bias Impedes Learning}, author = {Shaudi Mahdavi and M. Amin Rahimian}, booktitle = {Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers}, pages = {111--127}, year = {2017}, editor = {Tatiana V. Guy and Miroslav Kárný and David Rios-Insua and David H. Wolpert}, volume = {58}, series = {Proceedings of Machine Learning Research}, address = {Centre de Convencions Internacional de Barcelona, Barcelona, Spain}, month = {09 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v58/mahdavi17a/mahdavi17a.pdf}, url = {http://proceedings.mlr.press/v58/mahdavi17a.html}, abstract = {We propose a model that addresses an open question in the cognitive science literature: How can we rigorously model the cognitive bias known as hindsight bias such that we fully account for critical experimental results? Though hindsight bias has been studied extensively, prior work has failed to produce a consensus theoretical model sufficiently general to account for several key experimental results, or to fully demonstrate how hindsight impedes our ability to learn the truth in a repeated decision or social network setting. We present a model in which agents aim to learn the quality of their signals through repeated interactions with their environment. Our results indicate that agents who are subject to hindsight bias will always believe themselves to be high-type “expert” regardless of whether they are actually high- or low-type.} }
Endnote
%0 Conference Paper %T Hindsight Bias Impedes Learning %A Shaudi Mahdavi %A M. Amin Rahimian %B Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers %C Proceedings of Machine Learning Research %D 2017 %E Tatiana V. Guy %E Miroslav Kárný %E David Rios-Insua %E David H. Wolpert %F pmlr-v58-mahdavi17a %I PMLR %J Proceedings of Machine Learning Research %P 111--127 %U http://proceedings.mlr.press %V 58 %W PMLR %X We propose a model that addresses an open question in the cognitive science literature: How can we rigorously model the cognitive bias known as hindsight bias such that we fully account for critical experimental results? Though hindsight bias has been studied extensively, prior work has failed to produce a consensus theoretical model sufficiently general to account for several key experimental results, or to fully demonstrate how hindsight impedes our ability to learn the truth in a repeated decision or social network setting. We present a model in which agents aim to learn the quality of their signals through repeated interactions with their environment. Our results indicate that agents who are subject to hindsight bias will always believe themselves to be high-type “expert” regardless of whether they are actually high- or low-type.
APA
Mahdavi, S. & Rahimian, M.A.. (2017). Hindsight Bias Impedes Learning. Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, in PMLR 58:111-127

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