📄 中文摘要
学习者群体如何在没有明确沟通或多样性激励的情况下，发展出协调且多样化的行为？仅通过竞争，学习者便能自发地将环境划分为不同的专业领域，形成涌现专业化，这与生态位理论相符。我们提出了NichePopulation算法，该算法通过结合简单的竞争机制，使得学习者能够发现并利用环境中的不同“生态位”。NichePopulation算法的核心在于，当多个学习者在同一任务上竞争时，它们会自然地分化，以最小化彼此间的重叠，从而最大化整体群体的表现。这种分化并非预设，而是从竞争动态中自然产生。通过仿真实验，我们观察到，即使在同质的学习者群体中，竞争也能促使它们演化出不同的行为策略，从而覆盖更广泛的问题空间。
📄 English Summary
Emergent Specialization in Learner Populations: Competition as the Source of Diversity
How can populations of learners develop coordinated, diverse behaviors without explicit communication or diversity incentives? 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 interactions among learners. The core idea of NichePopulation is that when multiple learners compete on the same task, they naturally differentiate to minimize overlap, thereby maximizing the overall performance of the group. This differentiation is not pre-programmed but emerges organically from the competitive dynamics. Through simulation experiments, we observe that even in homogeneous learner populations, competition drives the evolution of diverse behavioral strategies, covering a broader problem space. For instance, in multi-armed bandit problems, individual learners within the NichePopulation algorithm spontaneously specialize in optimizing different arms, rather than all individuals attempting to optimize the same arm. This differentiation not only enhances the overall exploration efficiency of the population but also improves robustness to environmental changes. This research reveals competition as a powerful self-organizing mechanism that drives diversity and cooperative behavior in complex systems, offering a new perspective for understanding and designing distributed AI systems with emergent intelligence.