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Understanding the impact of network layout on cognitive understanding of Bayesian networks

Primary supervisor

Michael Wybrow


BARD: Bayesian Argumentation via Delphi [1] is a software system designed to help groups of intelligence analysts make better decisions. The software was funded by IARPA as part of the larger Crowdsourcing Evidence, Argumentation, Thinking and Evaluation (CREATE) program. The tool, developed at Monash University, uses causal Bayesian networks as underlying structured representations for argument analysis. It uses automated Delphi methods to help groups of analysts develop, improve and present their analyses.


While Bayesian networks have long been visualised graphically, very little attention has been paid to the layout of these networks. Network layout itself is also a well studied field [2] We believe network layout is important in Bayesian networks to assist users to define structure, notice similarities or differences, understand the network, or to detect errors. This research aims to explore approaches for Bayesian network specific layout and evaluate the importance of this when using Bayesian Networks for cognitive understanding or causal explanations. The existing network visualisation in the BARD system is based on webcola [3] and React, and is built by researchers at Monash. This project would involve extension of the network visualisation component in BARD (to vary the layout and create a simple interface for evaluation) as well as conducting a series of controlled user studies to explore the effects of layout on cognitive understanding.


[1] Korb KB, Nyberg EP, Oshni Alvandi A, Thakur S, Ozmen M, Li Y, Pearson R and Nicholson AE (2020). Individuals vs. BARD: Experimental Evaluation of an Online System for Structured, Collaborative Bayesian Reasoning. Front. Psychol. 11:1054. doi: 10.3389/fpsyg.2020.01054.



Required knowledge

Some experience with JavaScript would be useful.