Abstract
Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respectively. Here we develop a rigorous mathematical framework demonstrating that ant colony decision-making and random forest learning are isomorphic under a common formalism of stochastic ensemble intelligence. We show that the mechanisms by which genetically identical ants achieve functional differentiation— through stochastic response to local cues and positive feedback—map precisely onto the bootstrap aggregation and random feature subsampling that decorrelate decision trees. Using tools from Bayesian inference, multi-armed bandit theory, and statistical learning theory, we prove that both systems implement identical variance reduction strategies through decorrelation of identical units. We derive explicit mappings between ant recruitment rates and tree weightings, pheromone trail reinforcement and out-of-bag error estimation, and quorum sensing and prediction averaging. This isomorphism suggests that collective intelligence, whether biological or artificial, emerges from a universal principle: randomized identical agents + diversity-enforcing mechanisms → emergent optimality.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Date
Spring 3-31-2026
Document Type
Article
Department, Program, or Center
Mathematics and Statistics, School of
College
College of Science
Recommended Citation
Fokoue, Ernest; Babbitt, Gregory; and Levental, Yuval, "Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism Between Social Insect Colonies and Ensemble Machine Learning" (2026). Accessed from
https://repository.rit.edu/article/2189
Campus
RIT – Main Campus
Included in
Applied Statistics Commons, Data Science Commons, Statistical Models Commons, Statistical Theory Commons
