A Unified Stochastic Mechanism Underlying Collective Behavior in Ants, Physical Systems, and Robotic Swarms

2025-11-08

AI Summary

Existing studies lack a unified theoretical framework explaining the common mechanistic origins of stochastic behavior across biological swarms (e.g., ant colonies), physical systems (e.g., gases/liquids), and artificial collectives (e.g., robot swarms). Method: We propose the first unified mobility modeling framework grounded in energy-constrained stochastic decision-making, formalized within a maximum-entropy statistical model. Biological and artificial systems realize goal-directed, distributed cooperation via local, low-complexity rules; physical systems correspond to purposeless entropy-driven evolution. Our approach integrates empirical ant behavioral data, maximum-entropy principles, and distributed control algorithms. Contribution/Results: We empirically validate the mechanism in real ant colonies and reproduce scalable, decentralized, phase-transition-like collective behaviors in robot swarms. Crucially, we uncover a deep unifying principle linking thermodynamically driven, non-purposive evolution with goal-oriented collective intelligence—bridging statistical physics and embodied cognition through energy-limited stochasticity.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Reasoning under Uncertainty: Stochastic Optimization
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Biological swarms, such as ant colonies, achieve collective goals through decentralized and stochastic individual behaviors. Similarly, physical systems composed of gases, liquids, and solids exhibit random particle motion governed by entropy maximization, yet do not achieve collective objectives. Despite this analogy, no unified framework exists to explain the stochastic behavior in both biological and physical systems. Here, we present empirical evidence from extit{Formica polyctena} ants that reveals a shared statistical mechanism underlying both systems: maximization under different energy function constraints. We further demonstrate that robotic swarms governed by this principle can exhibit scalable, decentralized cooperation, mimicking physical phase-like behaviors with minimal individual computation. These findings established a unified stochastic model linking biological, physical, and robotic swarms, offering a scalable principle for designing robust and intelligent swarm robotics.

Problem

• Develops unified stochastic model for biological and physical systems
• Explains collective behavior through energy constraint optimization
• Enables scalable decentralized cooperation in robotic swarm design

Innovation

• Unified stochastic model maximizes energy constraints
• Robotic swarms mimic physical phase behaviors
• Decentralized cooperation with minimal individual computation