Emergent Specialization in Learner Populations: Competition as the Source of Diversity

TL;DR

This paper shows that competition alone can lead to emergent specialization in populations of learners, resulting in diverse behaviors and improved performance across various real-world tasks.

Contribution

The introduction of the NichePopulation algorithm demonstrates how competitive dynamics induce specialization without explicit incentives, outperforming existing methods.

Findings

Learners achieve significant specialization even without niche bonuses.

Diverse populations outperform homogeneous baselines by 26.5%.

Our approach surpasses MARL baselines by 4.3x and is 4x faster.

Abstract

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.