Our world is filled with hopelessly complicated systems for transportation, finance, biological life and others. These so-called complex systems, natural or human, are systems that are intrinsically challenging to predict due to collective dynamics that are influenced by external environmental factors.
The complexity of these systems is rooted in the intricate interdependence of these constituent elements and interaction with the outside world. Understanding the spread of exogenous disorders is of crucial importance for complex systems. For example, consider the local closure at one end of the power grid and how this can lead to massive cascading failure, moving into a large darkening such as the northeastern darkening in the United States in 2003. Or how a snow storm in the New York City area causes avalanches of air carriers in San Francisco, Los Angeles and the entire west coast. Or fads introduces celebrities who sometimes go viral, propagate and enhance through tweets / retweets, sharing and likes.
For decentralized networked systems that work in dynamic environments, the ability to respond to changing circumstances is paramount. This may be a matter of life and death of rats and maneuvers to avoid the attack of predators. It can also be a matter of optimum efficiency for multi-robot systems that work collectively and subject to changing conditions. It is therefore important to explore and understand the impact of network topology on the collective response system.
With that in mind, Bouffanais and his team at the University of Technology and Design in Singapore (SUTD) considered the archetypal model of distributed decision making. The aim was to study the collective ability of the system to respond to local external perturbations. Their theoretical network research results are confirmed by experiments on the collective behavior of roe debris robots. They found an uninitial relationship between the dynamics of perturbation and the optimal topology of the network. The emergence of a collective response to changes that are slow to change will increase with the degree of interaction, but the opposite is true for quick response. Their study revealed the existence of a number of interactions among the units needed to create an optimal collective response.
Chief Researcher, SUTD Associate Professor Roland Bouffanais, said: "With regard to the explosion in the development of distributed / decentralized systems, this research shows that the dynamic re-linking of the interaction network is crucial to effectively interacting these complex engineering systems at different time scales. "
Details of this work appeared in Scientific progress April 3, 2019
Researchers have found excessive social interaction by reducing the collective response
David Mateo et al., Optimal Network Topology for Responsible Collective Behavior, Scientific progress (2019). DOI: 10.1126 / sciadv.aau0999
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