Hierarchical Drift-Diffusion Model for Moral Dilemma: Understanding Reaction Times and Choices

AbstractDiscrete choice models (e.g. logistic regression) are popular models in the economics literature that understand and predict choices between two or more discrete alternatives. These models have been successfully used to describe utility-based decisions including decisions in moral dilemmas. Alternatively, in psychological literature, sequential-sampling models that predict decisions, as well as reaction time as a measure of uncertainty, have become the standard approach to the study of sensation and perception in decision-making. Here we propose a variant of a hierarchical drift-diffusion model, factor drift-diffusion, that combines the utility-based approach of discrete choice models with that of evidence accumulation mechanism of sequential-sampling models to understand and predict decisions in moral dilemmas. Using a dataset of 6500 moral decisions by 500 respondents on a popular web platform, Moral Machine, we show that factor drift-diffusion model uncovers latent factors that predict both reaction times and choices in moral dilemmas.

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