Modeling decision-making under uncertainty with qualitative outcomes

Abstract

Abstract Modeling decision-making under uncertainty typically relies on quantitative outcomes. Many decisions, however, are qualitative in nature, posing problems for traditional models. Here, we aimed to model uncertainty attitudes in decisions with qualitative outcomes. Participants made choices between certain outcomes and the chance for more favorable outcomes in quantitative (monetary) and qualitative (medical) modalities. Using computational modeling, we estimated the values participants assigned to qualitative outcomes and compared uncertainty attitudes across domains. Our model provided a good fit for the data, including quantitative estimates for qualitative outcomes. The model outperformed a utility function in quantitative decisions. Additionally, we found an association between ambiguity attitudes across domains. Results were replicated in an independent sample. We demonstrate the ability to extract quantitative measures from qualitative outcomes, leading to better estimation of subjective values. This allows for the characterization of individual behavior traits under a wide range of conditions. Author Summary In the current study, we explored how people make decisions when the outcomes aren’t easily measured in numbers, such as in medical choices. Traditional mathematical models, which rely on numerical data, often fall short in these situations, leading to a gap in understanding how people evaluate these qualitative outcomes. Using hierarchical Bayesian modeling, we developed a model that bridges this gap by translating qualitative outcomes into individualized quantitative values, enabling us to better understand the underlying decision-making processes. Our model not only provides a better fit to real-world data than existing models with qualitative or quantitative outcomes but also allows for meaningful comparisons of how people handle uncertainty across different decision-making scenarios. This approach opens new doors for studying decision-making in areas where traditional methods struggle, offering a more nuanced view of human behavior in complex situations.

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