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}.

翻译：社会性昆虫群体与集成机器学习方法分别代表了自然界与计算领域中分散化信息处理的两个最成功范例。本文发展了一个严谨的数学框架，证明蚁群决策与随机森林学习在一种共同的形式化体系——**随机集成智能**——下具有同构性。我们表明，基因相同的蚂蚁通过局部线索的随机响应与正反馈实现功能分化的机制，精确映射到使决策树去相关的自举聚合与随机特征子采样中。利用贝叶斯推断、多臂赌博机理论与统计学习理论的工具，我们证明这两个系统均通过相同单元的去相关来实现相同的方差缩减策略。我们推导出蚂蚁招募速率与树权重、信息素路径强化与袋外误差估计、以及群体感应与预测平均之间的显式映射。这一同构性表明，无论生物或人工的集体智能均源自一个普遍原理：**随机化的相同智能体 + 多样性增强机制 → 涌现最优性**。