The emergence of self-organization in complex systems–Preface

Paradisi, Paolo; Kaniadakis, Giorgio; Scarfone, Antonio Maria

doi:10.1016/j.chaos.2015.09.017

Cited by 29 publications

(30 citation statements)

References 30 publications

(61 reference statements)

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“…In order for a complex biological system to survive and evolve there must be interplay between competition and co-operation at different scales. Furthermore, the non-linear dynamics of a complex system must be co-operative for self-organization to occur [ 24 ]. This is a fundamental characteristic of insect & animal colonies and human activities.…”

Section: Complex Systems and Scale-invariancementioning

confidence: 99%

What Is a Complex Innovation System?

Katz

2016

PLoS ONE

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Innovation systems are sometimes referred to as complex systems, something that is intuitively understood but poorly defined. A complex system dynamically evolves in non-linear ways giving it unique properties that distinguish it from other systems. In particular, a common signature of complex systems is scale-invariant emergent properties. A scale-invariant property can be identified because it is solely described by a power law function, f(x) = kxα, where the exponent, α, is a measure of scale-invariance. The focus of this paper is to describe and illustrate that innovation systems have properties of a complex adaptive system. In particular scale-invariant emergent properties indicative of their complex nature that can be quantified and used to inform public policy. The global research system is an example of an innovation system. Peer-reviewed publications containing knowledge are a characteristic output. Citations or references to these articles are an indirect measure of the impact the knowledge has on the research community. Peer-reviewed papers indexed in Scopus and in the Web of Science were used as data sources to produce measures of sizes and impact. These measures are used to illustrate how scale-invariant properties can be identified and quantified. It is demonstrated that the distribution of impact has a reasonable likelihood of being scale-invariant with scaling exponents that tended toward a value of less than 3.0 with the passage of time and decreasing group sizes. Scale-invariant correlations are shown between the evolution of impact and size with time and between field impact and sizes at points in time. The recursive or self-similar nature of scale-invariance suggests that any smaller innovation system within the global research system is likely to be complex with scale-invariant properties too.

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Section: Complex Systems and Scale-invariancementioning

confidence: 99%

What Is a Complex Innovation System?

Katz

2016

PLoS ONE

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“…Even if a general definition of complex system is not universally accepted in the scientific community, some features are recognized to be ubiquitous in the presence of complexity (Paradisi et al, 2015c;Paradisi et al, 2015b;Solé and Bascompte, 2006;Sornette, 2006). Without demanding completeness, we give a brief list of the most important features:…”

Section: The Paradigm Of Fractal Intermittency In Complexitymentioning

confidence: 99%

“…This powerlaw behavior in the WT distribution is a crucial emergent property, characterizing the capacity of the complex system to trigger self-organization. This condition is denoted as fractal intermittency (Paradisi et al, 2012b;Paradisi et al, 2013;Allegrini et al, 2013;Paradisi et al, 2015b;Paradisi and Allegrini, 2015). This complex behavior is also known as Temporal Complexity (Grigolini, 2015;Beig et al, 2015;Turalska et al, 2011;Grigolini and Chialvo, 2013), a term that was introduced to underline the difference of the intermittency-based approach to complexity, focused on the study of the temporal structure of selforganization, with the more extensively investigated approach associated with the estimation of topological and spatial indicators of complexity (e.g., the degree distribution in a complex network, the avalanche size distribution) (Beggs and Plenz, 2003;Plenz and Thiagarjan, 2007;Fraiman et al, 2009;Chialvo, 2010;Grigolini and Chialvo, 2013).…”

Section: The Paradigm Of Fractal Intermittency In Complexitymentioning

confidence: 99%

The Challenge of Brain Complexity - A Brief Discussion about a Fractal Intermittency-based Approach

Paradisi¹,

Righi²,

Barcaro³

2016

Proceedings of the 3rd International Conference on Physiological Computing Systems

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Abstract:In the last years, the complexity paradigm is gaining momentum in many research fields where large multidimensional datasets are made available by the advancements in instrumental technology. A complex system is a multi-component system with a large number of units characterized by cooperative behavior and, consequently, emergence of well-defined self-organized structures, such as communities in a complex network. The self-organizing behavior of the brain neural network is probably the most important prototype of complexity and is studied by means of physiological signals such as the ElectroEncephaloGram (EEG). Physiological signals are typically intermittent, i.e., display non-smooth rapid variations or crucial events (e.g., cusps or abrupt jumps) that occur randomly in time, or whose frequency changes randomly. In this work, we introduce a complexity-based approach to the analysis and modeling of physiological data that is focused on the characterization of intermittent events. Recent findings about self-similar or fractal intermittency in human EEG are reviewed. The definition of brain event is a crucial aspect of this approach that is discussed in the last part of the paper, where we also propose and discuss a first version of a general-purpose event detection algorithm for EEG signals.

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“…Examples are the span of the walk, the first-passage times, survival probabilities, the number of distinct sites visited and, of course, mean and mean-square displacement if they exist. It is very interesting that all these things are also used to characterize complex systems [16].In principle, the CTRW is fundamentally different from the regular random flight or walk models as the probability density of the flight or walk in the long-time (asymptotic) limit scales in a non-Gaussian way [17]. Thus, the CTRW became a foundation of anomalous (dispersive, non-Gaussian) transport and diffusion [10,18,19].…”

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The continuous time random walk, still trendy: fifty-year history, state of art and outlook

2017

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Abstract.In this article we demonstrate the very inspiring role of the continuous-time random walk (CTRW) formalism, the numerous modifications permitted by its flexibility, its various applications, and the promising perspectives in the various fields of knowledge. A short review of significant achievements and possibilities is given. However, this review is still far from completeness. We focused on a pivotal role of CTRWs mainly in anomalous stochastic processes discovered in physics and beyond. This article plays the role of an extended announcement of the Eur. Inspiring properties and achievementsIn their pioneering work published in year 1965 [1], physicists Eliott W. Montroll and George H. Weiss introduced the concept of continuous-time random walk (CTRW) as a way to make the interevent-time continuous and fluctuating. It is characterized by some distribution associated with a stochastic process, giving an insight into the process activity. This distribution, called pausing-or waiting-time one (WTD), permitted the description of both Debye (exponential) and, what is most significant, non-Debye (slowly-decaying) relaxations as well as normal and anomalous transport and diffusion [2,3] -thus the model involves fundamental aspects of the stochastic world -a real, complex world. Notably, ancestors of this concept are presented by Michael Shlesinger in [4].Let us incidentally comment that term "walk" in the name "continuous-time random walk" is commonly used in the generic sense comprising two concepts: namely, both the walk (associated with finite displacement velocity of the process) and flight (associated with an instantaneous displacement of the process). Thus we have to specify in a detailed way with what kind of process we are considering.The CTRW formalism was most conveniently developed by physicists Scher and Lax in terms of recursion relations [5][6][7][8][9][10]. In this context the distinction between Contribution to the Topical Issue "Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook", edited by Ryszard Kutner and Jaume Masoliver. a e-mail: ryszard.kutner@fuw.edu.pl discrete and continuous times [11] and also between separable and non-separable WTDs were introduced [12]. A thorough analysis of the latter, called also the nonindependent CTRW, was performed a decade ago [13] although, in the context of the concentrated lattice gases, it was performed much earlier [14,15]. These analyses took into account dependences over many correlated consecutive particle displacements and waiting (or interevent) times. The Scher and Lax formulation of the CTRW formalism is particularly convenient to study as well anomalous transport and diffusion as the non-Debye relaxation and their anomalous scaling properties (e.g., the nonlinear time growth of the process variance). Examples are the span of the walk, the first-passage times, survival probabilities, the number of distinct sites visited and, of course, mean and mean-square displacement if they exist. It is very interesting...

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The emergence of self-organization in complex systems–Preface

Cited by 29 publications

References 30 publications

What Is a Complex Innovation System?

What Is a Complex Innovation System?

The Challenge of Brain Complexity - A Brief Discussion about a Fractal Intermittency-based Approach

The continuous time random walk, still trendy: fifty-year history, state of art and outlook

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