Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism Between Social Insect Colonies and Ensemble Machine Learning

TL;DR

This paper establishes a rigorous mathematical connection between social insect colonies and ensemble machine learning, showing both systems operate via similar stochastic, diversity, and variance reduction mechanisms leading to collective intelligence.

Contribution

It introduces a formal framework demonstrating the isomorphism between ant colony decision-making and random forest learning, unifying biological and artificial ensemble intelligence under a common theory.

Findings

Ant colony decision-making maps onto bootstrap aggregation in machine learning.

Pheromone trail reinforcement corresponds to out-of-bag error estimation.

Decorrelation mechanisms in both systems lead to variance reduction and optimality.

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 \textbf{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: \textbf{randomized identical agents + diversity-enforcing mechanisms $\rightarrow$ emergent optimality}.