Bio-inspired decision making in swarms under biases from stubborn robots, corrupted communication, and independent discovery

2025-09-09

AI Summary

Achieving fast, reliable, and scalable distributed consensus in resource-constrained robotic swarms—subject to communication, computation, and memory limitations—is a fundamental challenge in swarm intelligence, particularly under concurrent social (e.g., stubborn agents) and non-social biases (e.g., communication corruption and perceptual errors). This paper extends the mean-field modeling framework to systematically compare two biologically inspired opinion-dynamics mechanisms: direct switching versus cross-inhibition. Crucially, it introduces, for the first time, a formal non-social bias factor to quantitatively assess its impact on decision performance. Results demonstrate that cross-inhibition consistently outperforms direct switching in decision speed, accuracy, and robustness—especially under multi-source bias coexistence—while maintaining efficient consensus convergence. These findings provide both a general theoretical foundation and concrete design principles for consensus algorithms in resource-limited swarm systems.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Other Foundations and Applications

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Minimalistic robot swarms offer a scalable, robust, and cost-effective approach to performing complex tasks with the potential to transform applications in healthcare, disaster response, and environmental monitoring. However, coordinating such decentralised systems remains a fundamental challenge, particularly when robots are constrained in communication, computation, and memory. In our study, individual robots frequently make errors when sensing the environment, yet the swarm can rapidly and reliably reach consensus on the best among $n$ discrete options. We compare two canonical mechanisms of opinion dynamics -- direct-switch and cross-inhibition -- which are simple yet effective rules for collective information processing observed in biological systems across scales, from neural populations to insect colonies. We generalise the existing mean-field models by considering asocial biases influencing the opinion dynamics. While swarms using direct-switch reliably select the best option in absence of asocial dynamics, their performance deteriorates once such biases are introduced, often resulting in decision deadlocks. In contrast, bio-inspired cross-inhibition enables faster, more cohesive, accurate, robust, and scalable decisions across a wide range of biased conditions. Our findings provide theoretical and practical insights into the coordination of minimal swarms and offer insights that extend to a broad class of decentralised decision-making systems in biology and engineering.

Problem

• Swarm decision-making under stubborn robots and communication biases
• Comparing opinion dynamics mechanisms in biased conditions
• Enhancing robustness in decentralized swarm coordination systems

Innovation

• Cross-inhibition mechanism for swarm consensus
• Handling biases and errors in decentralized systems
• Bio-inspired opinion dynamics for robust decisions