Mechanism of organization increase in complex systems. Issue 2 (25th July 2014)

• Record Type: Journal Article
• Authors: Georgiev, Georgi Yordanov; Henry, Kaitlin; Bates, Timothy; Gombos, Erin; Casey, Alexander; Daly, Michael; Vinod, Amrit; Lee, Hyunseung
• Is Part Of: Complexity. Volume 21:Issue 2(2015)
• Page Start: 18
• Page End: 28
• Publication Date: 2014-07-25
• Subjects: self‐organization -- complex system -- flow network -- variational principles -- principle of least unit action -- principle of most total action -- positive feedback mechanism -- ordinary differential equations; Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1002/cplx.21574
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : This article proposes a variational approach to describe the evolution of organization of complex systems from first principles, as increased efficiency of physical action. Most simply stated, physical action is the product of the energy and time necessary for motion. When complex systems are modeled as flow networks, this efficiency is defined as a decrease of action for one element to cross between two nodes, or endpoints of motion—a principle of least unit action. We find a connection with another principle, that of most total action, or a tendency for increase of the total action of a system. This increase provides more energy and time for minimization of the constraints to motion to decrease unit action, and therefore, to increase organization. Also, with the decrease of unit action in a system, its capacity for total amount of action increases. We present a model of positive feedback between action efficiency and the total amount of action in a complex system, based on a system of ordinary differential equations, which leads to an exponential growth with time of each and a power law relation between the two. We present an agreement of our model with data for core processing units of computers. This approach can help to describe, measure, manage, design, and predict future behavior of complex systems to achieve the highest rates of self‐organization and robustness. © 2014 Wiley Periodicals, Inc. Complexity 21: 18–28, 2015