蚂蚁群体与随机森林在决策机制上存在数学同构，揭示了集体智能的通用原理。
Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism Between Social Insect Colonies and Ensemble Machine Learning
- 用随机集成智能框架证明蚁群与随机森林机制等价。
- 两者均通过去相关实现方差降低，提升决策准确性。
- 适合对群体智能、机器学习交叉研究感兴趣的读者。
社会性昆虫群体和集成机器学习方法是自然界与计算领域中去中心化信息处理的两个最成功范例。本文构建了一个严格的数学框架，证明蚁群决策与随机森林学习在随机集成智能的统一形式下具有同构性。我们发现，基因相同的蚂蚁通过随机响应局部线索和正反馈实现功能分化，这精确对应于决策树的自助采样和随机特征子采样以实现去相关。借助贝叶斯推断、多臂赌博机理论和统计学习理论工具，我们证明两者均通过去相关相同单元实现相同的方差缩减策略。我们推导出蚂蚁招募率与树权重之间的显式映射，信息素路径强化与袋外误差估计的对应关系，以及阈值感应与预测平均化的等价性。该同构性表明，无论是生物还是人工系统，集体智能的涌现都源于一个普适原则：随机相同的个体 + 多样性促进机制 → 出现最优结果。
原文摘要 · Abstract (English)
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}.
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