Widening Access to Bayesian Problem Solving - Cruz, Nicole; Desai, Saoirse Connor; Dewitt, Stephen; Hahn, Ulrike; Lagnado, David; Liefgreen, Alice; Phillips, Kirsty; Pilditch, Toby; Tešić, Marko (Frontiers in Psychology, 2020)
Bayesian reasoning and decision making is widely considered normative because it minimises 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 modelling 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 modelling 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 modelling tool, adapted to provide basic training and guidance on the modelling 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.