[2510.16462] Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making

Computer Science > Machine Learning arXiv:2510.16462 (cs) [Submitted on 18 Oct 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making Authors:Emmanuelle Claeys, Elena Kerjean, Jean-Michel Loubes View a PDF of the paper titled Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making, by Emmanuelle Claeys and 2 other authors View PDF HTML (experimental) Abstract:This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' decisions in contextualized foraging tasks. The model accounts for bees' limited memory through a temporal window $\tau$, whose optimal value is around 7 trials, with a slight dependence on weather conditions. Experimental results on real, simulated, and complementary (mice) datasets show that MAYA (particularly with the Wasserstein distance) outperforms imitation baselines and classical statistical models, while providing interpretability of individual learning strategies and enabling the inference of realistic trajectories for prospective ecological applications. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2510.16462 [cs.LG]   (or arXiv:2510.16462v2 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2510.16462 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Emmanuelle Claeys [view email] [v1] Sat, 18 Oct 20...