The Rational Agent Benchmark for Data Visualization

Yifan Wu, Ziyang Guo, Michalis Mamakos, Jason Hartline, Jessica Hullman

DOI: 10.1109/TVCG.2023.3326513

Room: 109

2023-10-25T00:33:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T00:33:00Z
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Understanding how helpful a visualization is from experimental results is difficult because the observed performance is confounded with aspects of the study design, such as how useful the information that is visualized is for the task. We develop a rational agent framework for designing and interpreting visualization experiments. Our framework conceives two experiments with the same setup: one with behavioral agents (human subjects), and the other one with a hypothetical rational agent. Our framework can be used to pre-experimentally and post-experimentally evaluate the experiment design.
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Keywords

Evaluation, decision-making, rational agent, scoring rule

Abstract

Understanding how helpful a visualization is from experimental results is difficult because the observed performance is confounded with aspects of the study design, such as how useful the information that is visualized is for the task. We develop a rational agent framework for designing and interpreting visualization experiments. Our framework conceives two experiments with the same setup: one with behavioral agents (human subjects), and the other one with a hypothetical rational agent. A visualization is evaluated by comparing the expected performance of behavioral agents to that of a rational agent under different assumptions. Using recent visualization decision studies from the literature, we demonstrate how the framework can be used to pre-experimentally evaluate the experiment design by bounding the expected improvement in performance from having access to visualizations, and post-experimentally to deconfound errors of information extraction from errors of optimization, among other analyses.