Forum
What is DR@W Forum?
DR@W Forum is an interdisciplinary discussion series which focuses on theoretical and empirical research about decision making.
The usual structure of the forum is a 30 - 45 minute introduction of the topic/working paper, with ample additional time for discussion.
The audience prefers discussing work-in-progress topics as opposed to finished papers. We meet on Thursdays between 2:30 and 3:45pm during term time. Contact John Taylor (John.Taylor[at]wbs.ac.uk) if you would like to suggest a speaker for a future event. Notifications of upcoming DR@W Forum events along with other decision research related activities can be obtained by registering with the moderated mailing list - email behaviour_spotlight at newlistserv dot warwick dot ac dot uk to be added to the list.
If you attend DR@W please take some time to fill in our survey It helps us understand who our audience are and how we can widen participation.
DR@W Forum: Kamil Fulawka (MPI, Berlin)
I will present a novel approach to understanding the subjective reasons underlying risky decision-making, enabled by large language models (LLM). Traditional models of risky choice often assume that decision-making relies on a single, stable reason or process, regardless of context. This simplification overlooks the variability in reasoning that may occur depending on the situation. To address these limitations, we developed an LLM-based approach that utilizes free-text reports to reveal the subjective reasons behind decisions, and we implemented a proof-of-concept in three stages. First, we extracted a comprehensive set of nearly 50 decision reasons from formal models, heuristics, and basic motivations. Second, we collected free-text retrospective verbal reports from 86 participants after each of 20 risky choices they made. Third, we employed advanced prompt engineering techniques with a state-of-the-art LLM to identify the reasons mentioned in these reports.
Our results provide strong evidence that decision reasons vary systematically across different choice problems but less across individuals. Furthermore, a simple predictive model based on the identified reasons achieves an out-of-sample accuracy of about 92%, validating the approach. Our results suggest that combining verbal reports and an LLM-based analysis with a large sample and comprehensive set of choice problems can uncover how people make risky decisions, including intricate relationships between decision reasons and types of choice problems.