When are decentralized infrastructure networks preferable to centralized ones?
Proceedings of the 50th Hawaii International Conference on System Sciences, , , 2017
Abstract: Many infrastructure networks, such as power,
water, and natural gas systems, have similar properties
governing flows. However, these systems have distinctly
different sizes and topological structures. This paper
seeks to understand how these different features
can emerge from relatively simple design principles.
Specifically, we work to understand the conditions
under which it is optimal to build small decentralized
network infrastructures, such as a microgrid, rather
than centralized ones, such as a large high-voltage
power system. While our method is simple it is useful
in explaining why sometimes, but not always, it is
economical to build large, interconnected networks and
in other cases it is preferable to use smaller, distributed
systems. The results indicate that there is not a single
set of infrastructure cost conditions that cause a
transition from centralized networks being optimal, to
decentralized architectures. Instead, as capital costs
increase network sizes decrease gradually, according
to a power-law. And, as the value of reliability
increases, network sizes increase abruptly—there is
a threshold at which large, highly interconnected
networks are preferable to decentralized ones.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).