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Widening Access to Bayesian Problem Solving
Frontiers in Psychology, Volume: 11
Swansea University Author: Alice Liefgreen
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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)Download (1.02MB)
DOI (Published version): 10.3389/fpsyg.2020.00660
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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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.
Bayesian networks, assistive software technology, reasoning, decision making, probabilistic
Faculty of Humanities and Social Sciences
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).