{"pk":49904,"title":"Predicting Human Choice Between Textually Described Lotteries","subtitle":null,"abstract":"Predicting human decision-making under risk and uncertainty is a long-standing challenge in cognitive science, economics, and AI. While prior research has focused on numerically described lotteries, real-world decisions often rely on textual descriptions. This study conducts the first large-scale exploration of human decision-making in such tasks using a large dataset of one-shot binary choices between textually described lotteries. We evaluate multiple computational approaches, including fine-tuning Large Language Models (LLMs), leveraging embeddings, and integrating behavioral theories of choice under risk. Our results show that fine-tuned LLMs, specifically  GPT-4o, outperform hybrid models that incorporate behavioral theory, challenging established methods in numerical settings. These findings highlight fundamental differences in how textual and numerical information influence decision-making and underscore the need for new modeling strategies to bridge this gap.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Artificial Intelligence; Decision making; Machine learning; Natural Language Processing; Computational Modeling"}],"section":"Papers with Poster Presentation","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7838t9zr","frozenauthors":[{"first_name":"Eyal","middle_name":"","last_name":"Marantz","name_suffix":"","institution":"Technion - Israel Institute of Technology","department":""},{"first_name":"Ori","middle_name":"","last_name":"Plonsky","name_suffix":"","institution":"Technion - Israel Institute of Technology","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2025-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/49904/galley/37866/download/"}]}