Topic: Data-driven methods

The Society for Industrial and Applied Mathematics convenes researchers developing and applying dynamical-systems methods across biology, chemistry, physics, climate science, social science, industry and data science. Themes include computational, experimental, theoretical and data-driven methods; AI-informed systems; fluid and climate dynamics; materials; networks; pattern formation; population dynamics; stochastic systems and tipping points. Minisymposium proposals are due 26 October 2026, followed by contributed lecture, poster and minisymposium-presentation abstracts on 23 November 2026.

Traditional work in the study of human reward-based learning involves designing an experimental task---often inspired by Reinforcement Learning (RL) theory---and fits a small set of computational models---often inspired by RL algorithms---to that dataset. For example, researchers often model human behavior on bandit tasks using variants of Q-learning. While this approach has been highly productive, leading to landmark discoveries such as the dopamine reward prediction error hypothesis, it also has limitations. This talk focuses on the lack of generalizability of such models: Even if they close