Journal article 464 views 29 downloads
Propensities and Second Order Uncertainty: A Modified Taxi Cab Problem
Frontiers in Psychology, Volume: 11
Swansea University Author: Alice Liefgreen
-
PDF | Version of Record
© 2020 Dewitt, Fenton, Liefgreen and Lagnado. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Download (1.31MB)
DOI (Published version): 10.3389/fpsyg.2020.503233
Abstract
The study of people’s ability to engage in causal probabilistic reasoning has typically used fixed-point estimates for key figures. For example, in the classic taxi-cab problem, where a witness provides evidence on which of two cab companies (the more common ‘green’/less common ‘blue’) were responsi...
| Published in: | Frontiers in Psychology |
|---|---|
| ISSN: | 1664-1078 |
| Published: |
Frontiers Media SA
2020
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa60563 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
The study of people’s ability to engage in causal probabilistic reasoning has typically used fixed-point estimates for key figures. For example, in the classic taxi-cab problem, where a witness provides evidence on which of two cab companies (the more common ‘green’/less common ‘blue’) were responsible for a hit and run incident, solvers are told the witness’s ability to judge cab color is 80%. In reality, there is likely to be some uncertainty around this estimate (perhaps we tested the witness and they were correct 4/5 times), known as second-order uncertainty, producing a distribution rather than a fixed probability. While generally more closely matching real world reasoning, a further important ramification of this is that our best estimate of the witness’ accuracy can and should change when the witness makes the claim that the cab was blue. We present a Bayesian Network model of this problem, and show that, while the witness’s report does increase our probability of the cab being blue, it simultaneously decreases our estimate of their future accuracy (because blue cabs are less common). We presented this version of the problem to 131 participants, requiring them to update their estimates of both the probability the cab involved was blue, as well as the witness’s accuracy, after they claim it was blue. We also required participants to explain their reasoning process and provided follow up questions to probe various aspects of their reasoning. While some participants responded normatively, the majority self-reported ‘assuming’ one of the probabilities was a certainty. Around a quarter assumed the cab was green, and thus the witness was wrong, decreasing their estimate of their accuracy. Another quarter assumed the witness was correct and actually increased their estimate of their accuracy, showing a circular logic similar to that seen in the confirmation bias/belief polarization literature. Around half of participants refused to make any change, with convergent evidence suggesting that these participants do not see the relevance of the witness’s report to their accuracy before we know for certain whether they are correct or incorrect. |
|---|---|
| Keywords: |
causal Bayesian networks, second order uncertainty, propensity, uncertainty, confirmation bias |
| College: |
Faculty of Humanities and Social Sciences |