Harris, AJL;
Hahn, U;
Madsen, JK;
Hsu, AS;
(2016)
The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach.
Cognitive Science
, 40
(6)
pp. 1496-1533.
10.1111/cogs.12276.
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Abstract
The appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme-based treatment of the argument form is formalised within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert’s expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to how people evaluate this argument, suggesting that such an approach might be beneficial to argumentation research generally. We subsequently present two experiments as an example of the potential for future research in this vein, demonstrating that participants’ quantitative ratings of the convincingness of a proposition that has been supported with an appeal to expert opinion were broadly consistent with the predictions of the Bayesian model.
| Type: | Article |
|---|---|
| Title: | The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/cogs.12276 |
| Publisher version: | http://dx.doi.org/10.1111/cogs.12276 |
| Language: | English |
| Additional information: | This is the peer reviewed version of the following article: Harris, AJL; Hahn, U; Madsen, JK; Hsu, AS; (2015) The Appeal to Expert Opinion: Quantitative support for a Bayesian Network Approach. Cognitive Science, which has been published in final form at: http://dx.doi.org/10.1111/cogs.12276. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
| Keywords: | Argumentation; Appeal to authority; Appeal to expert opinion; Epistemic authority; Bayesian probability; Quantitative modeling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1468674 |
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