# Synchronization of complex human networks

## TL;DR

This paper investigates synchronization in complex human networks, specifically among violin players, revealing unique dynamics and control strategies that differ from traditional models, with implications for various real-world systems.

## Contribution

The study demonstrates that human networks exhibit distinct synchronization behaviors not captured by existing models, introducing new control mechanisms and strategies for complex network dynamics.

## Key findings

- Humans can alter their periodicity by a factor of three for stable synchronization.
- Players can selectively ignore signals to delete frustrating connections.
- Traditional models like Kuramoto do not apply to human networks.

## Abstract

The synchronization of human networks is essential for our civilization, and understanding the motivations, behavior, and basic parameters that govern the dynamics of human networks is important in many aspects of our lives. Human ensembles have been investigated in recent years, but with very limited control over the network parameters and in noisy environments. In particular, research has focused predominantly on all-to-all coupling, whereas current social networks and human interactions are often based on complex coupling configurations, such as nearest-neighbor coupling and small-world networks. Because the synchronization of any ensemble is governed by its network parameters, studying different types of human networks while controlling the coupling and the delay is essential for understanding the dynamics of different types of human networks. We studied the synchronization between professional violin players in complex networks with full control over the network connectivity, coupling strength of each connection, and delay. We found that the usual models for coupled networks, such as the Kuramoto model, cannot be applied to human networks. We found that the players can change their periodicity by a factor of three to find a stable solution to the coupled network, or they can delete connections by ignoring frustrating signals. These additional degrees of freedom enable new strategies and yield better solutions than are possible within current models. Our results may influence numerous fields, including traffic management, epidemic control, and stock market dynamics.