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Updated: Sep 18, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Ambiguity preferences in intertemporal and risky choice: A large-scale study using drift-diffusion modelling.

Mingqian Guo1, Iris Ikink2,3,4, Karin Roelofs2,3

  • 1Behavioural Science Institute, Radboud University, Thomas Van Aquinostraat 4, 6525GD, Nijmegen, The Netherlands. mingqian.guo@ru.nl.

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|June 25, 2025
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Summary
This summary is machine-generated.

Individuals show consistent aversion to both time and probability ambiguity in decision-making. This suggests that ambiguity preferences are linked across different types of choices, even with varying decision strategies.

Keywords:
AmbiguityDrift-diffusion modelIntertemporal choiceRisky choice

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Area of Science:

  • Behavioral Economics
  • Cognitive Psychology
  • Decision Science

Background:

  • Real-world decisions often involve ambiguity in reward timing and probability, unlike controlled lab settings.
  • Understanding ambiguity aversion is crucial for explaining economic and psychological choices.

Purpose of the Study:

  • To investigate the relationship between time ambiguity aversion and probability ambiguity aversion.
  • To evaluate computational models, including drift-diffusion models (DDMs), for choices involving ambiguity.
  • To determine if ambiguity preferences are consistent across intertemporal and risky decision domains.

Main Methods:

  • Analysis of a large dataset (N > 669) of risky and intertemporal choices with varying ambiguity.
  • Application of drift-diffusion models (DDMs) to assess choice behavior.
  • Comparison of attribute-wise and integrated-value computational models.

Main Results:

  • A significant positive correlation (r = .28) was found between aversion to time ambiguity and probability ambiguity.
  • DDMs incorporating ambiguity preferences significantly outperformed those without, across both domains.
  • Time ambiguity aversion was best modeled by an attribute-wise approach, while probability ambiguity aversion was best modeled by an integrated-value approach.

Conclusions:

  • Ambiguity preferences are not independent across time and probability domains, demonstrating cross-domain consistency.
  • Decision strategies for ambiguity can differ (attribute-wise vs. integrated-value) depending on the choice domain.
  • The findings support the use of DDMs with ambiguity parameters to model complex decision-making.