A dataset of risky and ambiguous decisions using a novel Linked Colored Lottery Task across two studies

TL;DR

This paper introduces a new task to study how people make decisions when outcomes are uncertain, capturing individual differences in beliefs about probabilities.

Contribution

The Linked Colored Lottery Task allows for examining decision-making under ambiguity beyond max-min assumptions by incorporating beliefs about probabilities.

Findings

The dataset includes demographic and questionnaire data alongside decision-making behavior in ambiguous scenarios.

The task enables analysis of how individual beliefs about probabilities influence decisions beyond worst-case and best-case assumptions.

Abstract

How individuals make decisions under ambiguity (i.e., uncertain situations where the probability of an outcome is unknown) has been related to numerous individual differences of clinical importance including aging, substance use, and autism spectrum disorders. Despite this, many studies rely on a max-min model of ambiguity decision-making, which assumes that individuals evaluate ambiguous options based solely on worst-case and best-case scenarios. However, this approach does not account for the role of individual beliefs about underlying probabilities, which can significantly shape decision-making behavior. We introduce a novel task, the Linked Colored Lottery Task, in an in-person (N= 53) and online sample (N= 300) which allows for analyses that examine the effect of these beliefs. Along with this novel task, demographic information and data for several personality and clinical questionnaires were collected. By incorporating measures that capture differences in probability beliefs, this dataset enables researchers to examine how ambiguity-related decision-making varies beyond max-min assumptions, providing a richer understanding of how individuals form and act upon beliefs about probabilities. These data offer opportunities to explore how individual tendencies—such as risk preferences, cognitive styles, and clinical traits–interact with beliefs about uncertainty, advancing both theoretical and applied perspectives on decision-making under ambiguity.

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