Bayesian Reasoning For Better Thinking

D. Hemmi
Monash University,

Keywords: Bayesian argumentation via Delphi, IARPA funded


Monash University built a tool that uses causal Bayesian networks as underlying structured representations for argument analysis. The tool has automated Delphi methods to help groups of analysts develop, improve and present their analyses. The tool is based on new means of interacting with Bayesian networks, including means of assessing potential in causal explanations. In BARD, we designed and produced a Graphical User Interface (GUI) for using causal Bayesian networks as the underlying engines for arguments, allowing analysts to build and test competing or complementary arguments and to examine the impact of different pieces of evidence in an intuitive environment based on the principles of Delphi. elphi methods have been used for fifty years to help bring experts to improved opinions in domains of uncertainty, minimizing group think and other biases using anonymising moderation. We’ve developed automated support for the Delphi construction of Bayesian networks, which we will further enhance in BARD. BARD’s principal investigators include experts in Delphi from the University of Strathclyde and experts in the psychology of causal reasoning from Birkbeck College London and University College London.