A Human-Computer Interface Design for Quantitative Measure of Regret Theory
This work addresses the challenge of quantifying human decision-making under risk for psychology and HCI researchers, but it is incremental as it builds on existing theories like fuzzy-set theory.
The paper tackles the problem of measuring psychological preferences for regret theory by designing a human-computer interface that reduces procedural influence and cognitive workload, resulting in a model with prediction accuracy equivalent to subjects' revisit-performance.
Regret theory is a theory that describes human decision-making under risk. The key of obtaining a quantitative model of regret theory is to measure the preference in humans' mind when they choose among a set of options. Unlike physical quantities, measuring psychological preference is not procedure invariant, i.e. the readings alter when the methods change. In this work, we alleviate this influence by choosing the procedure compatible with the way that an individual makes a choice. We believe the resulting model is closer to the nature of human decision-making. The preference elicitation process is decomposed into a series of short surveys to reduce cognitive workload and increase response accuracy. To make the questions natural and familiar to the subjects, we follow the insight that humans generate, quantify and communicate preference in natural language. The fuzzy-set theory is hence utilized to model responses from subjects. Based on these ideas, a graphical human-computer interface (HCI) is designed to articulate the information as well as to efficiently collect human responses. The design also accounts for human heuristics and biases, e.g. range effect and anchoring effect, to enhance its reliability. The overall performance of the survey is satisfactory because the measured model shows prediction accuracy equivalent to the revisit-performance of the subjects.