Bhatia, S., van Baal, S.T. orcid.org/0000-0001-5351-4361, Wang, F. et al. (1 more author) (2025) Computational analysis of 100 K choice dilemmas: Decision attributes, trade-off structures, and model-based prediction. Proceedings of the National Academy of Sciences, 122 (17). e2406489122. ISSN 0027-8424
Abstract
We present a dataset of over 100 K textual descriptions of real-life choice dilemmas, obtained from social media posts and large-scale survey data. Using large language models (LLMs), we extract hundreds of choice attributes at play in these dilemmas and map them onto a common representational space. This representation allows us to quantify the broader themes and specific trade-offs inherent in life choices and analyze how they vary across different contexts. We also present our dilemmas to human participants and find that our LLM pipeline, when combined with established decision models, accurately predicts people’s choices, outperforming models based on unstructured textual content, demographics, and personality. In this way, our research provides insights into the attributes, outcomes, and goals that underpin life choices, and shows how large-scale LLM-based structure extraction can be used in combination with existing scientific theory to study complex real-world human behavior.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 the Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY-NC-ND 4.0). |
Keywords: | decision-making, computational modeling, large language models, multiattribute choice, naturalistic choice |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Analytics, Technology & Ops Department |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Mar 2025 15:34 |
Last Modified: | 29 May 2025 14:09 |
Status: | Published |
Publisher: | National Academy of Sciences |
Identification Number: | 10.1073/pnas.2406489122 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224454 |