Individual Preference Stability

Individual preference stability research investigates the consistency and predictability of people's choices over time and across different contexts. Current research focuses on developing computational models, including graph neural networks and machine learning classifiers, to predict and analyze preference dynamics, often incorporating factors like reference points and stakeholder values. This work is significant for improving recommender systems, advancing fair and ethical algorithmic decision-making (e.g., in organ allocation), and providing a deeper understanding of human decision-making processes in strategic interactions. The development of efficient algorithms for identifying stable preference structures is a key area of ongoing investigation.