Editorial Comment on the Special Issue of "Information in Dynamical Systems and Complex Systems"

This special issue collects contributions from the participants of the "Information in Dynamical Systems and Complex Systems" workshop, which cover a wide range of important problems and new approaches that lie in the intersection of information theory and dynamical systems. The contributions include theoretical characterization and understanding of the different types of information flow and causality in general stochastic processes, inference and identification of coupling structure and parameters of system dynamics, rigorous coarse-grain modeling of network dynamical systems, and exact statistical testing of fundamental information-theoretic quantities such as the mutual information. The collective efforts reported herein reflect a modern perspective of the intimate connection between dynamical systems and information flow, leading to the promise of better understanding and modeling of natural complex systems and better/optimal design of engineering systems

Modelica - A Language for Physical System Modeling, Visualization and Interaction

Modelica is an object-oriented language for modeling of large, complex and heterogeneous physical systems. It is suited for multi-domain modeling, for example for modeling of mechatronics including cars, aircrafts and industrial robots which typically consist of mechanical, electrical and hydraulic subsystems as well as control systems. General equations are used for modeling of the physical phenomena, No particular variable needs to be solved for manually. A Modelica tool will have enough information to do that automatically. The language has been designed to allow tools to generate efficient code automatically. The modeling effort is thus reduced considerably since model components can be reused and tedious and error-prone manual manipulations are not needed. The principles of object-oriented modeling and the details of the Modelica language as well as several examples are presented

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Bits and Bucks: Modeling complex systems by information flow

This paper presents a general method for modeling and characterizing complex systems in terms of flows of information together with flows of conserved or quasi-conserved quantities such as energy or money. Using mathematical techniques borrowed from statistical mechanics and from physics of computation, a framework is constructed that allows general systems to be modeled in terms of how information, energy, money, etc. flow between subsystems. Physical, chemical, biological, engineering, and commercial systems can all be analyzed within this framework

Network centrality: an introduction

Centrality is a key property of complex networks that influences the behavior of dynamical processes, like synchronization and epidemic spreading, and can bring important information about the organization of complex systems, like our brain and society. There are many metrics to quantify the node centrality in networks. Here, we review the main centrality measures and discuss their main features and limitations. The influence of network centrality on epidemic spreading and synchronization is also pointed out in this chapter. Moreover, we present the application of centrality measures to understand the function of complex systems, including biological and cortical networks. Finally, we discuss some perspectives and challenges to generalize centrality measures for multilayer and temporal networks.Comment: Book Chapter in "From nonlinear dynamics to complex systems: A Mathematical modeling approach" by Springe

Principles of Modeling in Information Communication Systems and Networks

The authors present in this entry chapter the basic rubrics of models, modeling, and simulation, an un- derstanding of which is indispensible for the comprehension of subsequent chapters of this text on the all-important topic of modeling and simulation in Information Communication Systems and Networks (ICSN). A good example is the case of analyzing simulation results of traffic models as a tool for investigat- ing network behavioral pattarns as it affects the transmitted content (Atayero, et al., 2013). The various classifications of models are discussed, for example classification based on the degree of semblance to the original object (i.e. isomorphism). Various fundamental terminologies without the knowledge of which the concepts and models and modeling cannot be properly understood are explained. Model stuctures are highlighted and discussed. The methodological basis of formalizing complex system structures is presented. The concept of componential approach to modeling is presented and the necessary stages of mathematical model formation are examined and explained. The chapter concludes with a presentation of the concept of simulation vis-à-vis information communication systems and networks