Modeling Nonequilibrium Phase Transitions and Critical Behavior in Complex Systems

We comment on some recent, yet unpublished results concerning instabilities in complex systems and their applications. In particular, we briefly describe main observations during extensive computer simulations of two lattice nonequilibrium models. One exhibits robust and efficient processes of pattern recognition under synaptic coherent activity; the second example exhibits interesting critical behavior and simulates nucleation and spinodal decomposition processes in driven fluids.Comment: 6 pages, 4 figure

Timing of Transients : Quantifying Reaching Times and Transient Behavior in Complex Systems

The authors thank the anonymous referees for their detailed and constructive feedback. This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. This work was conducted in the framework of PIK’s flagship project on coevolutionary pathways (copan). The authors thank CoNDyNet (FKZ 03SF0472A) for their cooperation. The authors gratefully acknowledge the European Regional Development Fund (ERDF), the German Federal Ministry of Education and Research and the Land Brandenburg for supporting this project by providing resources on the high performance computer system at the Potsdam Institute for Climate Impact Research. The authors thank the developers of the used software: Python[47], Numerical Python[48] and Scientific Python[49]. The authors thank Sabine Auer, Karsten Bolts, Catrin Ciemer, Jonathan Donges, Reik Donner, Jasper Franke, Frank Hellmann, Jakob Kolb, Chiranjit Mitra, Finn Muller-Hansen, Jan Nitzbon, Anton Plietzsch Stefan Ruschel, Tiago Pereira da Silva, Francisco A. Rodrigues, Paul Schultz, and Lyubov Tupikina for helpful discussions and comments.Peer reviewedPublisher PD

Bounding network spectra for network design

The identification of the limiting factors in the dynamical behavior of complex systems is an important interdisciplinary problem which often can be traced to the spectral properties of an underlying network. By deriving a general relation between the eigenvalues of weighted and unweighted networks, here I show that for a wide class of networks the dynamical behavior is tightly bounded by few network parameters. This result provides rigorous conditions for the design of networks with predefined dynamical properties and for the structural control of physical processes in complex systems. The results are illustrated using synchronization phenomena as a model process.Comment: 17 pages, 4 figure

Understanding Supply Chain Complexity with Performance Measurement

Abstract: Despite the great number of complex systems existing in the real world, complexity is currently a poorly explored topic. In organizational settings, managers regularly apply to complex contexts classical approaches developed for simple systems, just because they do not know how to take into account companies' internal and external complexity. Nevertheless, before developing new managerial models, a deep knowledge about drivers and effects of complexity is needed. After defining the characteristics making supply chains complex systems, this paper discusses performance measurement as a methodology to analyze the effects of complexity on supply chain behavior. The results of a survey highlight that manufacturing companies usually evaluate isolated aspects of their supply chains, without considering the relationships between different performance indicators or dimensions. This work suggests System Dynamics as a valuable approach to understand the cause and effect connections among metrics and system elements affecting their values, thus clarifying the structure leading to a complex behavior. This research is the first step of a larger project aimed at providing companies with innovative tools to understand and manage supply chain complexit

Control Principles of Complex Networks

A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: It requires an accurate map of the network that governs the interactions between the system's components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in nonlinear dynamics and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these here we review recent advances on the controllability and the control of complex networks, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components. We match the pertinent mathematical results with empirical findings and applications. We show that uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.Comment: 55 pages, 41 figures, Submitted to Reviews of Modern Physic