Interactive visualizations to improve Bayesian reasoning

Interactive;Visualizations;Bayesian reasoning;"Bayes theorem";Interfaces;Judgment;Debiasing;Cognitive biases;Base rate neglect;Chains of reasoning;Coherence;Correspondence

Tsai, Jennifer

关键词: Interactive;    Visualizations;    Bayesian reasoning;    "Bayes theorem";    Interfaces;    Judgment;    Debiasing;    Cognitive biases;    Base rate neglect;    Chains of reasoning;    Coherence;    Correspondence;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/34556/Tsai_Jennifer.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Proper Bayesian reasoning is critical across a broad swath of domains that require practitioners to make predictions about the probability of events contingent upon earlier actions or events. However, much research on judgment has shown that people who are unfamiliar with Bayes’ Theorem often reason quite poorly with conditional probabilities due to various cognitive biases. As such, this dissertation chronicles the development and evaluation of an interactive visualization designed to aid Bayes-naïve people in solving conditional probability problems, in part by leveraging its graphical properties to head off the occurrence of biases. In three experiments, the visualization was tested with different classes of Bayesian problems. Experiment 1 showed that participants using the interactive visualization substantially improved their reasoning performance above that of previous debiasing methods for common, academic elementary Bayesian problems. Experiment 2 suggests that some measure of this improvement is retained for more complicated chains of reasoning Bayesian problems, with the majority of benefit going to those participants who self-assess themselves to be better in math ability than their peers. Experiment 3 showed that in real-time prediction/updating with a concrete, to-be-resolved Bayesian problem tied to a sporting event, participants using the visualization achieved better reasoning performance, seemed to suffer less from detrimental effects of overconfidence, and had internal reasoning accuracy that was solidly predictive of their accuracy with respect to matching the external event/world – a desirable property that allows for estimations of judges’ outcome performance, based on readily available process information. Altogether, findings from three experiments point to visualizations being a rich area to mine, and prime candidate for expanding the toolbox of techniques that can be used to more accurately elicit the predictions of judges whose expertise lies beyond the realm of statistics.