The effect of Delphi group size and opinion diversity on participant experience

Belton, Ian; Wright, George W.; Sissons, Aileen; Bolger, Fergus; Crawford, Megan; Hamlin, Iain; Lūka, Courtney Taylor Browne; Vasilichi, Alexandrina

doi:10.31219/osf.io/e9ubm

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“…This investigation will be facilitated by the configurability of BARD user access, with three different versions of Delphi already available. Further possible research on collaboration includes investigating the best group size and the factors that may influence it (e.g., Belton et al, 2021); how a group may be split across different tasks; and how outputs from multiple groups working in parallel might be best combined, e.g., by a meta-level BARD group. BARD is also well suited to educational applications, either to teach BN modeling or to use it as an analytic method for specific questions.…”

Section: Discussionmentioning

confidence: 99%

BARD: A Structured Technique for Group Elicitation of Bayesian Networks to Support Analytic Reasoning

et al. 2021

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In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively.

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Section: Discussionmentioning

confidence: 99%