How can populations of learners develop coordinated, diverse behaviors without explicit communication or diversity incentives? We demonstrate that competition alone is sufficient to induce emergent specialization -- learners spontaneously partition into specialists for different environmental regimes through competitive dynamics, consistent with ecological niche theory. We introduce the NichePopulation algorithm, a simple mechanism combining competitive exclusion with niche affinity tracking. Validated across six real-world domains (cryptocurrency trading, commodity prices, weather forecasting, solar irradiance, urban traffic, and air quality), our approach achieves a mean Specialization Index of 0.75 with effect sizes of Cohen's d > 20. Key findings: (1) At lambda=0 (no niche bonus), learners still achieve SI > 0.30, proving specialization is genuinely emergent; (2) Diverse populations outperform homogeneous baselines by +26.5% through method-level division of labor; (3) Our approach outperforms MARL baselines (QMIX, MAPPO, IQL) by 4.3x while being 4x faster.

翻译：学习者群体如何在缺乏显式通信或多样性激励的情况下，发展出协调且多样化的行为？我们证明，仅凭竞争就足以引发涌现专业化——学习者通过竞争动态自发地划分为适应不同环境机制的专业个体，这与生态位理论相一致。我们提出了NichePopulation算法，这是一种结合竞争排斥与生态位亲和度追踪的简单机制。在六个现实领域（加密货币交易、商品价格、天气预报、太阳辐照度、城市交通和空气质量）的验证表明，我们的方法实现了平均专业化指数0.75，效应量Cohen's d > 20。关键发现包括：（1）当λ=0（无生态位奖励）时，学习者仍能达到SI > 0.30，证明专业化是真正涌现的；（2）通过方法层面的劳动分工，多样化群体比同质基线性能提升+26.5%；（3）我们的方法在性能上超越多智能体强化学习基线（QMIX、MAPPO、IQL）4.3倍，同时速度提升4倍。