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Widening Access to Bayesian Problem Solving

Nicole Cruz, Saoirse Connor Desai, Stephen Dewitt, Ulrike Hahn, David Lagnado, Alice Liefgreen, Kirsty Phillips, Toby Pilditch, Marko Tešić

Frontiers in Psychology, Volume: 11

Swansea University Author: Alice Liefgreen

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DOI (Published version): 10.3389/fpsyg.2020.00660

Abstract

Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network...

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Published in: Frontiers in Psychology
ISSN: 1664-1078
Published: Frontiers Media SA 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa60562
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spelling 2022-08-19T11:26:48.1651097 v2 60562 2022-07-20 Widening Access to Bayesian Problem Solving 5a11aaeb0cd68f36ec54c5534dc541bd Alice Liefgreen Alice Liefgreen true false 2022-07-20 LAWD Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making. Journal Article Frontiers in Psychology 11 Frontiers Media SA 1664-1078 Bayesian networks, assistive software technology, reasoning, decision making, probabilistic 9 4 2020 2020-04-09 10.3389/fpsyg.2020.00660 COLLEGE NANME Law COLLEGE CODE LAWD Swansea University This research was based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), to the BARD project (Bayesian Reasoning via Delphi) of the CREATE programme under Contract (2017-16122000003). 2022-08-19T11:26:48.1651097 2022-07-20T14:16:10.5940222 Faculty of Humanities and Social Sciences Hilary Rodham Clinton School of Law Nicole Cruz 1 Saoirse Connor Desai 2 Stephen Dewitt 3 Ulrike Hahn 4 David Lagnado 5 Alice Liefgreen 6 Kirsty Phillips 7 Toby Pilditch 8 Marko Tešić 9 60562__24965__d5b542053bb545a2ba606c64c4fc2808.pdf 60562.pdf 2022-08-19T11:25:26.7631211 Output 1067523 application/pdf Version of Record true Copyright © 2020 Cruz, Desai, Dewitt, Hahn, Lagnado, Liefgreen, Phillips, Pilditch and Teši´c. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) true eng http://creativecommons.org/licenses/by/4.0/
title Widening Access to Bayesian Problem Solving
spellingShingle Widening Access to Bayesian Problem Solving
Alice Liefgreen
title_short Widening Access to Bayesian Problem Solving
title_full Widening Access to Bayesian Problem Solving
title_fullStr Widening Access to Bayesian Problem Solving
title_full_unstemmed Widening Access to Bayesian Problem Solving
title_sort Widening Access to Bayesian Problem Solving
author_id_str_mv 5a11aaeb0cd68f36ec54c5534dc541bd
author_id_fullname_str_mv 5a11aaeb0cd68f36ec54c5534dc541bd_***_Alice Liefgreen
author Alice Liefgreen
author2 Nicole Cruz
Saoirse Connor Desai
Stephen Dewitt
Ulrike Hahn
David Lagnado
Alice Liefgreen
Kirsty Phillips
Toby Pilditch
Marko Tešić
format Journal article
container_title Frontiers in Psychology
container_volume 11
publishDate 2020
institution Swansea University
issn 1664-1078
doi_str_mv 10.3389/fpsyg.2020.00660
publisher Frontiers Media SA
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str Hilary Rodham Clinton School of Law{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}Hilary Rodham Clinton School of Law
document_store_str 1
active_str 0
description Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making.
published_date 2020-04-09T04:18:46Z
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score 11.03559

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