[Paper Review] How Robots in a Large Group Make Decisions as a Whole? From Biological Inspiration to the Design of Distributed Algorithms

Gabriele Valentini|arXiv (Cornell University)|Feb 10, 2020

Slime Mold and Myxomycetes Research79 references4 citations

TL;DR

This paper presents a modular framework for designing distributed decision-making algorithms inspired by biological collectives, such as social insects and flocks, using local interactions and shared information to achieve robust, scalable, and adaptive group decisions without central control. The key contribution is a mathematically grounded design approach that ensures performance guarantees through predictive models.

ABSTRACT

Nature provides us with abundant examples of how large numbers of individuals can make decisions without the coordination of a central authority. Social insects, birds, fishes, and many other living collectives, rely on simple interaction mechanisms to do so. They individually gather information from the environment; small bits of a much larger picture that are then shared locally among the members of the collective and processed together to output a commonly agreed choice. Throughout evolution, Nature found solutions to collective decision-making problems that are intriguing to engineers for their robustness to malfunctioning or lost individuals, their flexibility in face of dynamic environments, and their ability to scale with large numbers of members. In the last decades, whereas biologists amassed large amounts of experimental evidence, engineers took inspiration from these and other examples to design distributed algorithms that, while maintaining the same properties of their natural counterparts, come with guarantees on their performance in the form of predictive mathematical models. In this paper, we review the fundamental processes that lead to a collective decision. We discuss examples of collective decisions in biological systems and show how similar processes can be engineered to design artificial ones. During this journey, we review a framework to design distributed decision-making algorithms that are modular, can be instantiated and extended in different ways, and are supported by a suit of predictive mathematical models.

Motivation & Objective

• To understand the fundamental mechanisms by which large groups of individuals make collective decisions without centralized control, drawing from biological systems.
• To address the challenge of designing artificial collective decision-making systems that are robust, scalable, and adaptive to dynamic environments.
• To develop a modular framework for distributed algorithms that can be instantiated and extended across different applications.
• To provide predictive mathematical models that guarantee performance and stability of the engineered collective systems.
• To bridge the gap between biological observation and engineered implementation in decentralized decision-making systems.

Proposed method

• The framework draws on biological examples—such as ant foraging, bird flocks, and fish schools—where individuals use local interactions and environmental cues to reach group consensus.
• It employs decentralized information sharing through local interactions, where each agent updates its state based on limited, local observations.
• The design emphasizes modularity, allowing components such as information gathering, sharing, and decision thresholds to be independently configured and extended.
• Mathematical models are used to predict system behavior, including convergence speed, accuracy, and robustness to failures or noise.
• The approach supports scalability by ensuring performance does not degrade with increasing group size.
• The framework enables formal analysis of collective decision outcomes using stochastic processes and consensus dynamics.

Experimental results

Research questions

• RQ1 How do biological collectives achieve reliable group decisions through simple, local interactions without centralized control?
• RQ2 What design principles can be extracted from natural systems to engineer scalable and robust artificial collective decision-making algorithms?
• RQ3 How can distributed algorithms be structured to maintain performance guarantees while remaining modular and extensible?
• RQ4 What mathematical models can predict the behavior and convergence of such artificial collectives under varying conditions?
• RQ5 How do these engineered systems maintain robustness and adaptability in dynamic or faulty environments?

Key findings

• The framework successfully translates biological decision-making mechanisms into a design blueprint for artificial collectives.
• Local interaction rules based on environmental cues and shared information enable robust and scalable group decisions.
• The modular architecture allows for flexible instantiation across diverse robotic or computational systems.
• Predictive mathematical models provide formal guarantees on decision accuracy, convergence speed, and fault tolerance.
• The system maintains high performance even when individuals fail or are lost, demonstrating resilience.
• The approach enables scalable decision-making that does not degrade with increasing group size.