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

Abstract

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 19, 2021
Source ID
10.1111/risa.13759

Entities

People

  • A.k.m. Azad
  • Abraham Oshni Alvandi
  • Ann E. Nicholson
  • David Lagnado
  • Erik P. Nyberg
  • Fergus Bolger
  • Ingrid Zukerman
  • Jeff Riley
  • Kevin B. Korb
  • Matthieu Herrmann
  • Michael Wybrow
  • Ross Pearson
  • Shane Morris
  • Shreshth Thakur
  • Steven Mascaro
  • Ulrike Hahn

Organizations

  • Birkbeck, University of London
  • Intelligence Advanced Research Projects Activity
  • Monash University
  • University College London
  • University of Strathclyde

Tags

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference