Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | IDM@NIPS | Computer science,Artificial intelligence,Hindsight bias,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaudi Mahdavi | 1 | 0 | 0.34 |
| M. Amin Rahimian | 2 | 3 | 4.11 |