ArticleProceedings of the National Academy of Sciences of the United States of America2025
Computational analysis of 100 K choice dilemmas: Decision attributes, trade-off structures, and model-based prediction.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Computational analysis of 100 K choice dilemmas: Decision attributes, trade-off structures, and model-based prediction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.