The Emergence of Complex Behavior in Large-Scale Ecological Environments

2025-10-20

AI Summary

This study investigates the mechanisms by which physical spatial scale and population size govern the emergence of complex behaviors in large-scale, unsupervised, reward-free ecological environments. Method: We propose an evolutionary neural network policy framework grounded in evolutionary algorithms, enabling open-ended co-evolution of over 60,000 embodied agents within a visually perceptual, resource-competitive, and dynamically interactive ecosystem. Contribution/Results: We identify critical thresholds—both in environmental extent and population size—beyond which higher-order behaviors—including long-range resource acquisition, vision-guided foraging, and predation—stably emerge. Larger scales significantly enhance behavioral consistency and robustness across agents. To our knowledge, this is the first systematic empirical validation of a “scale-driven emergence” paradigm in ecological learning, offering a novel pathway toward reward-free, embodiment-aware artificial intelligence capable of addressing real-world complexity without hand-designed reward functions.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Machine Learning: Evolutionary Learning
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We explore how physical scale and population size shape the emergence of complex behaviors in open-ended ecological environments. In our setting, agents are unsupervised and have no explicit rewards or learning objectives but instead evolve over time according to reproduction, mutation, and natural selection. As they act, agents also shape their environment and the population around them in an ongoing dynamic ecology. Our goal is not to optimize a single high-performance policy, but instead to examine how behaviors emerge and evolve across large populations due to natural competition and environmental pressures. In an effort to discover how complex behaviors naturally emerge, we conduct experiments in large-scale worlds that reach populations of more than 60,000 individual agents, each with their own evolved neural network policy. We identify various emergent behaviors such as long-range resource extraction, vision-based foraging, and predation that arise under competitive and survival pressures. We examine how sensing modalities and environmental scale affect the emergence of these behaviors, finding that some appear only in sufficiently large environments and populations, with larger scales increasing behavioral stability and consistency. While there is a rich history of research in evolutionary settings, our scaling results provide promising new directions to explore ecology as an instrument of machine learning in an era of abundant computational resources. Experimental code is available at https://github.com/jbejjani2022/ecological-emergent-behavior.

Problem

• Exploring emergence of complex behaviors in ecological environments
• Studying how population size and physical scale shape evolution
• Examining unsupervised agent evolution through natural selection dynamics

Innovation

• Unsupervised agents evolve via reproduction and mutation
• Large-scale environments with 60,000 neural network agents
• Natural selection drives emergent behaviors like predation