Resilience and Controllability of Dynamic Collective Behaviors

Abstract

The network paradigm is used to gain insight into the structural root causes of the resilience of consensus in dynamic collective behaviors, and to analyze the controllability of the swarm dynamics. Here we devise the dynamic signaling network which is the information transfer channel underpinning the swarm dynamics of the directed interagent connectivity based on a topological neighborhood of interactions. The study of the connectedness of the swarm signaling network reveals the profound relationship between group size and number of interacting neighbors, which is found to be in good agreement with field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl. Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic collective behaviors enabling us to uncover that the swarm signaling network is a homogeneous clustered small-world network, thus facilitating emergent outcomes if connectedness is maintained. Resilience of the emergent consensus is tested by introducing exogenous environmental noise, which ultimately stresses how deeply intertwined are the swarm dynamics in the physical and network spaces. The availability of the signaling network allows us to analytically establish for the first time the number of driver agents necessary to fully control the swarm dynamics.

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Cited by in corpus (11)

• Network resilience
• Swarm-Enabling Technology for Multi-Robot Systems
• Optimal Network Topology for Effective Collective Response
• Consensus reaching in swarms ruled by a hybrid metric-topological distance
• Controllability of a swarm of topologically interacting autonomous agents
• Human collective visualization transparency
• Controllability of a Class of Swarm Signaling Networks
• A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
• Excess of Social Behavior Reduces the Capacity to Respond to Perturbations
• Swarming collapse under limited information flow between individuals
• Information Flow in Finite Flocks with Topological Interactions