Characteristics Of Complex Systems Research Articles

Co-adaptation and the emergence of structure.

Co-adaptation (or co-evolution), the parallel feedback process by which agents continuously adapt to the changes induced by the adaptive actions of other agents, is a ubiquitous feature of complex adaptive systems, from eco-systems to economies. We wish to understand which general features of complex systems necessarily follow from the (meta)-dynamics of co-adaptation, and which features depend on the details of particular systems. To begin this project, we present a model of co-adaptation (“The Stigmergy Game”) which is designed to be as a priori featureless as possible, in order to help isolate and understand the naked consequences of co-adaptation. In the model, heterogeneous, co-adapting agents, observe, interact with and change the state of an environment. Agents do not, ab initio, directly interact with each other. Agents adapt by choosing among a set of random “strategies,” particular to each agent. Each strategy is a complete specification of an agent's actions and payoffs. A priori, all environmental states are equally likely and all strategies have payoffs that sum to zero, so without co-adaptation agents would on average have zero “wealth”. Nevertheless, the dynamics of co-adaptation generates a structured environment in which only a subset of environmental states appear with high probability (niches) and in which agents accrue positive wealth. Furthermore, although there are no direct agent-agent interactions, there are induced non-trivial inter-agent interactions mediated by the environment. As a function of the population size and the number of possible environmental states, the system can be in one of three dynamical regions. Implications for a basic understanding of complex adaptive systems are discussed.

Trigonometric regressive spectral analysis: an innovative tool for evaluating the autonomic nervous system

Biological rhythms, describing the temporal variation of biological processes, are a characteristic feature of complex systems. The analysis of biological rhythms can provide important insights into the pathophysiology of different diseases, especially, in cardiovascular medicine. In the field of the autonomic nervous system, heart rate variability (HRV) and baroreflex sensitivity (BRS) describe important fluctuations of blood pressure and heart rate which are often analyzed by Fourier transformation. However, these parameters are stochastic with overlaying rhythmical structures. R-R intervals as independent variables of time are not equidistant. That is why the trigonometric regressive spectral (TRS) analysis--reviewed in this paper--was introduced, considering both the statistical and rhythmical features of such time series. The data segments required for TRS analysis can be as short as 20 s allowing for dynamic evaluation of heart rate and blood pressure interaction over longer periods. Beyond HRV, TRS also estimates BRS based on linear regression analyses of coherent heart rate and blood pressure oscillations. An additional advantage is that all oscillations are analyzed by the same (maximal) number of R-R intervals thereby providing a high number of individual BRS values. This ensures a high confidence level of BRS determination which, along with short recording periods, may be of profound clinical relevance. The dynamic assessment of heart rate and blood pressure spectra by TRS allows a more precise evaluation of cardiovascular modulation under different settings as has already been demonstrated in different clinical studies.

Understanding Amsterdam Airport Schiphol through Controversies

The question of how to organize collaborative problem-solving efforts when collective action is necessary has received considerable attention in the scientific literature. More recently, it is even argued that to organize successful collaborative arrangements, the complex system characteristics of the governance system in which such an arrangement is applied needs to be taken into account. This would trigger a bandwagon effect of mutually reinforcing changes and be successful in overcoming deadlocks. In this paper, we will argue that this line of reasoning is far from complete, even resulting in misguiding conclusions. By analyzing the governance of Amsterdam Airport Schiphol, we suggest that—instead of complicating the problem by establishing more self-organizing collaborative bodies, hoping to avoid path dependencies and bring new solutions—there is rather a need to clear ‘the disposition of complexity’ profoundly, towards concrete associative opportunities, and co-evolution beyond political agendas and/or plans. Copyright © 2013 John Wiley & Sons, Ltd.

A Hybrid Interval–Robust Optimization Model for Water Quality Management

In water quality management problems, uncertainties may exist in many system components and pollution-related processes (i.e., random nature of hydrodynamic conditions, variability in physicochemical processes, dynamic interactions between pollutant loading and receiving water bodies, and indeterminacy of available water and treated wastewater). These complexities lead to difficulties in formulating and solving the resulting nonlinear optimization problems. In this study, a hybrid interval-robust optimization (HIRO) method was developed through coupling stochastic robust optimization and interval linear programming. HIRO can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original chemical oxygen demand (COD) discharge constraints, HIRO enhances the robustness of the optimization processes and resulting solutions. This method was applied to planning of industry development in association with river-water pollution concern in New Binhai District of Tianjin, China. Results demonstrated that the proposed optimization model can effectively communicate uncertainties into the optimization process and generate a spectrum of potential inexact solutions supporting local decision makers in managing benefit-effective water quality management schemes. HIRO is helpful for analysis of policy scenarios related to different levels of economic penalties, while also providing insight into the tradeoff between system benefits and environmental requirements.

Structural Analysis on Social Network Constructed from Characters in Literature Texts

Recently we witnessed that the social network analysis focusing on social entities is applied in the social science and web-science, behavioral sciences, as well as in economics, marketing. In this paper we present one method to construct and structure analysis the social network from literary fictions by a simple lexical analysis. And we will show that those literary social graphs, shows the power law distribution of some features, which is the typical characteristics of complex systems. We newly proposed the concept of the kernel of literary social network by which we can classify the abstract level of protagonists appeared in fictions. And we also studied the connectivity of social network based on statement distance distribution of characters. Our study shows that the metric distance among characters written in linear text is very similar to the intrinsic and semantic relationship described by fiction writers, which implies the proposed social network from fictions could be another representation of literary fiction. So we can apply other scientific and quantitative approach by analyzing the concrete social graph model extracted from textual data.

Implicit Interaction: Mathematics on Local Description of Systems

Researches on co-creation systems and that on internal measurement share some basic views on complex systems. These are the researches on features of complex systems which include some duality such as explicit/implicit information or global/local interaction, etc. For experiments and analyses based on the views, we need a theoretical framework describing the duality. In this study, two types of interaction are introduced. One is “explicit interaction” which is defined by conventional usage of functions, and the other is “implicit interaction” which is defined by a generalized determination process of function parameters. These interactions are formalized via duality on vector spaces and generalized inverse matrices. Implicit interaction is defined by the form which is similar to statistical parameter estimation but is not the same. It derives a Cauchy distribution on parameter fluctuation and structural branch on parameters.

The Analysis and Design Study of High Speed Robotic Devices

The problem of multi d.o.f. positioning devices based on compact compliant kinematic mechanisms is to guarantee the desired positional accuracy in static and especially in dynamic modes of operation. The study of accuracy and performance analysis of high speed devices is made in this paper. The influence of differences between stiffness and damping coefficients in actuated directions as well as mutual cross couplings between them are discussed in details and performance characteristics of such complex systems are simulated. As proposed the problem of improving accuracy can be solved by insertion of the compensation member into control system that could be integrated in parallel or serial way. The presented approach enables to verify the dynamical range of operation for small / micro positioning devices performing precise trajectory following tasks.

Complexity of Stakeholder Interaction in Applied Research

Pade-Khene, C., R. Luton, T. Jordaan, S. Hildbrand, C. Gerwel Proches, A. Sitshaluza, J. Dominy, W. Ntshinga, and N. Moloto. 2013. Complexity of stakeholder interaction in applied research. Ecology and Society 18(2): 13. https://doi.org/10.5751/ES-05405-180213

Complex System Analysis of Social Networks Extracted from Literary Fictions

Recently we witnessed that the social network analysis focusing on social entities is applied in the social science and web-science, behavioral sciences, as well as in economics, marketing. In this paper we present one method to construct the social network from literary fictions by a simple lexical analysis, not using the complex natural language processing tools. And we will show that those social graphs, saying literary social graph, shows the power law distribution of some features, which is the typical characteristics of complex systems. We showed that the social network extracted from literary data reflects the similar network structure which was semantically designed by authors of fictions. And we newly proposed the concept of the kernel of literary social network by which we can classify the abstract level of protagonists appeared in fictions. Our study shows that the metric distance among characters written in linear text is very similar to the intrinsic and semantic relationship described by fiction writers, which implies the proposed social network from fictions could be another representation of literary fiction. So we can apply other scientific and quantitative approach by analyzing the concrete social graph model extracted from textual data. Extracting useful information from a large textual repository is getting essential in data mining field. One difficulty in this work is how to deal with the various kinds of natural languages. Most work has based on English based texts, but recently some other languages including East-Asian languages have shown interesting result. Due to the recent prevailing SNS (Social Network Service), people try to extract the sentiment from text data shared in on-line users. After obtaining attitude from one individual, we are able to identify the connection structure of elements in a community. The main issues of these sentiment analysis is how to identify the polarity of adjectives based on conjunctions linking them in a large corpus. And another hot research area is mining information over online discussion by observing discussion threads. By mining used words or related replying patterns among discussion thread, we can identify the friendly group or conflicting group. Our basic idea is that we can regard the complicated text (e.g. long literary fictions) as the typical complex system

Network Theory and Terminology

The paper aims to present the relations of network theory and terminology. The model of scale-free networks, which has been recently developed and widely applied since, can be effectively used in terminology research as well. Operation based on the principle of networks is a universal characteristic of complex systems. Networks are governed by general laws. The model of scale-free networks can be viewed as a statistical-probability model, and it can be described with mathematical tools. Its main feature is that ‘everything is connected to everything else’, that is every node is reachable (in a few steps) starting from any other node; this phenomena called ‘the small world phenomenon’. The existence of a linguistic network and the general laws of the operation of networks enable us to place issues of language use in the complex system of relations that reveal the deeper connections between phenomena with the help of networks embedded in each other. The realization of the metaphor that language also has a network structure is the basis of the classification methods of the terminological system, and likewise of the ways of creating terminology databases, which serve the purpose of providing easy and versatile accessibility to specialised knowledge.

Learning by Modeling (LbM): Understanding Complex Systems by Articulating Structures, Behaviors, and Functions

Understanding the behavior of complex systems has become a focal issue for scientists in a wide range of disciplines. Making sense of a complex system should require that a student construct a network of concepts and principles about the learning complex phenomena. This paper describes part of a project about Learning-by-Modeling (LbM). Many features of complex systems make it difficult for students to develop deep understanding. Previous research indicates that involvement with modeling scientific phenomena and complex systems can play a powerful role in science learning. Some researchers argue with this view indicating that models and modeling do not contribute to understanding complexity concepts, since these increases the cognitive load on students. In this study we investigated the effect of different modes of involvement in exploring scientific phenomena using computer simulation tools, on students’ mental model from the perspective of structure, behaviour and function. Quantitative and qualitative methods are used to report about 121 freshmen students that engaged in participatory simulations about complex phenomena, showing emergent, self-organized and decentralized patterns. Results show that LbM plays a major role in students' concept formation about complexity concepts.

Black Holes—Information Models

Estimation of the volume of information in black holes is necessary for generation of restrictions for their formation, development and interconversion. Information is an integral part of the Universe. By its physical essence information is heterogeneity of matter and energy. The universal measure of physical heterogeneity of information is the Shannon in- formation entropy. It is important to note that the Neumann entropy cannot be applied as the universal measure of het- erogeneity because it is equal to zero for structured pure state. Therefore information is inseparably connected with matter and energy. The informatics laws of nature are: the basic law of Zeilinger’s quantum mechanics postulates that the elementary physical system (in particular, fundamental particles: quarks, leptons,…) bears one bit of information, the law of simplicity of complex systems, the law of uncertainty (information) conservation, the law of finiteness of complex systems characteristics, the law of necessary variety by W. Ashby, and the theorem of K. Godel. The law of finiteness of complex systems characteristics and the principle of necessary variety by W. Ashby impose restrictions on the topology and symmetry of the universe. The author’s works testify about the practicality of information laws simultaneously with physical rules for cognition of the Universe. The results presented in this paper show the effectiveness of informational approach to studying the black holes. The article discusses the following questions: The volume of information in the black hole, Emission and absorption of usual substance by a black hole, Formation and development (changing) of black holes, Black hole merger. Black hole is called optimal if information content is minimal at the University region. Optimal black holes can exist when at least the two types of substance are available in the Universe: with non-linear and linear correspondence between information content and mass. Information content of optimal black hole is proportional to squared coefficient correlating information content with mass in usual substance and in inverse proportion to coefficient correlating information content with black hole mass. Concentration of mass in optimal black hole minimizes information content in the system “usual substance—black holes”. Minimal information content of the Universe consisting of optimal black holes only is twice as less as information content available of the Universe of the same mass filled with usual substance only. An information approach along with a physical one allows obtaining new, sometimes more general data in relation to data obtained on the ground of physical rules only.

The development of an integrated model for assessing sustainability of complex systems

Currently, there are numerous indicators (single and composite) for measuring impacts in the three pillars, though current thinking emphasises the need for system thinking rather than the reductionist concept of pillars. Most existing indices/methods measure single aspects of sustainability and the more integrated indicators are aimed at national or global level assessments. A review of existing indicators, methods and models within the context of complex system sustainability showed that no single existing index, method or model was able to assess sustainability of complex systems. Most fail to account for complex system characteristics, such as system dynamics, interconnections and interdependencies of system components, a system’s ability to learn and remember, emergence of novel behaviours, co-evolution, etc. This paper presents the methodology used to develop a new model for assessing sustainability of complex systems based on risk.

Dynamical complexity of human responses: a multivariate data-adaptive framework

Abstract Established complexity measures typically operate at a single scale and thus fail to quantify inherent long-range correlations in real-world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) method has the ability to detect fractal correlations and has been used successfully to assess the complexity of univariate data. However, multivariate observations are common in many real-world scenarios and a simultaneous analysis of their structural complexity is a prerequisite for the understanding of the underlying signal-generating mechanism. For this purpose, based on the notion of multivariate sample entropy, the standard MSE method is extended to the multivariate case, whereby for rigor, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows us to gain better understanding of the complexity of the underlying multivariate real-world process, together with more degrees of freedom and physical interpretation in the analysis. Simulations on both synthetic and real-world biological multivariate data sets support the analysis.

Multifractal information fusion based condition diagnosis for process complex systems

Abnormal conditions are hazardous in process complex systems, and the aim of condition diagnosis is to detect abnormal conditions and thus avoid serious accidents. Comparing with conventional techniques of condition diagnosis without concerning the nonlinearity of complex system, multifractal analysis elaborately reveals scale-invariance or self-similarity properties of time series data, which is one of the intrinsic characteristics of complex systems. Moreover, the monitoring data within multiple feature variables should be investigated by combining multifractal analysis and information fusion techniques, so that significant patterns of the whole system would be discovered. In this article, a condition diagnosis framework is proposed for industrial complex systems, by which nonlinear features are extracted from univariate time series through multifractal analysis using multifractal detrended fluctuation analysis algorithm, and multiple feature variables are investigated through Mahalanobis–Taguchi system as an information fusion method to determine the condition of the whole system. The effectiveness of the approach is illustrated using data from both simulated model and real production system in an industrial enterprise.

On the use of topological features and hierarchical characterization for disambiguating names in collaborative networks

Many features of complex systems can now be unveiled by applying statistical physics methods to treat them as social networks. The power of the analysis may be limited, however, by the presence of ambiguity in names, e.g., caused by homonymy in collaborative networks. In this paper we show that the ability to distinguish between homonymous authors is enhanced when longer-distance connections are considered, rather than looking at only the immediate neighbors of a node in the collaborative network. Optimized results were obtained upon using the 3rd hierarchy in connections. Furthermore, reasonable distinction among authors could also be achieved upon using pattern recognition strategies for the data generated from the topology of the collaborative network. These results were obtained with a network from papers in the arXiv repository, into which homonymy was deliberately introduced to test the methods with a controlled, reliable dataset. In all cases, several methods of supervised and unsupervised machine learning were used, leading to the same overall results. The suitability of using deeper hierarchies and network topology was confirmed with a real database of movie actors, with the additional finding that the distinguishing ability can be further enhanced by combining topology features and long-range connections in the collaborative network.

Research and application of fluorescence system on norfloxacin-terbium-phen-SDBS

Fluorescence characteristics of complex system of rare earth terbium (Tb3+), norfloxacin, 1,10-phenanthroline(1,10-phen) and surfactant sodium dodecyl benzene sulfonate (SDBS) had been studied by using fluorescence spectroscopy. In a buffer solution (pH=6.3), the fluorescence intensity of the norfloxacin-Tb3+ (NFLX-Tb3+) system had been remarkably enhanced by SDBS and 1,10-Phen. When excited at 335 nm, a good linear relationship between fluorescence intensity of NFLX-Tb3+ system and NFLX concentration was obtained in the range of 8×10−8−4×10−6 mol/L, and the linear equation was F=2×108CNFLX+22.7 with correlation coefficient of 0.9959. In addition, this method was compared with some previous literatures, the proposed method had relatively higher sensitivity and lower detect limit. The method was simple, rapid, sensitive, practical and suitable for direct analysis of pharmaceutical preparation. It was successfully applied to determine the capsules, human serum and urine in real pharmaceutical samples.

Erratum: Temporal dimensions of vulnerability to crime in economic sectors: Theory meets evidence and spawns a new framework

This article presents the results of research into vulnerability to crime in two economic sectors in Belgium. Vulnerability to crime is an integration of diverse temporal factors. Borrowing from Zedner, we address pre-crime and post-crime aspects of vulnerability, arising before and after the criminal event in an economic context. Within this temporal frame, four sub-dimensions of vulnerability to crime are identified: opportunity to commit a crime, inadequate checks and controls within the business or sector, a lack of ability to recover from a crime and lack of adaptation after a crime. Based on inductive interviews with professionals, security staff, law enforcement agents and with criminals in the transport sector and the hotel and catering industry, a study of police files, and inspired by ecologists’ work on resilience, we explore vulnerability to crime as a multifaceted factor undermining the sustainability of businesses. This conceptualisation of vulnerability accepts uncertainty as an inherent feature of complex systems, such as economic sectors, and so as an inherent aspect of the specific forms of crime that arise in such environments.

Network-analysis approaches to deal with causal complexity in a supply network

Large integrated supply networks can exhibit several complex system characteristics. In such systems, researchers tend to misperceive feedback relationships and have difficulty in identifying dynamic causal behaviour, even when they have an understanding of the underlying structural relationships within a system. Supply-network researchers often make use of simulation models, but this is only appropriate if a high degree of knowledge concerning the supply network is available. Many disciplines, including a limited number of supply network researchers, have used network analyses to represent complex systems, and several advanced graph theory techniques exist to support such studies. The aim of this paper is to demonstrate the use of network-analysis approaches in order to analyse supply networks. The research was carried out in four case-study areas within a sugarcane-production-and-processing environment and demonstrates two network-analysis approaches. Semi-structured interviews with stakeholders from different sectors were carried out, and issues (or problems) in the supply network were incorporated into a single coherent network. An energy-transformation approach as well as transitivity produced valuable information. A cause-and-effect network-analysis approach could depict suitable key performance indicators as well as leverage points within the supply network. These methodologies enable researchers to achieve a high degree of understanding in a relatively short time span. The analyses of the supply network occur at a higher degree of abstraction, hence obviating any need to model and understand the intricate detail of the system before any conclusion can be reached.

Multivariate Multiscale Entropy Analysis

Multivariate physical and biological recordings are common and their simultaneous analysis is a prerequisite for the understanding of the complexity of underlying signal generating mechanisms. Traditional entropy measures are maximized for random processes and fail to quantify inherent long-range dependencies in real world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) is a univariate method capable of detecting intrinsic correlations and has been used to measure complexity of single channel physiological signals. To generalize this method for multichannel data, we first introduce multivariate sample entropy (MSampEn) and evaluate it over multiple time scales to perform the multivariate multiscale entropy (MMSE) analysis. This makes it possible to assess structural complexity of multivariate physical or physiological systems, together with more degrees of freedom and enhanced rigor in the analysis. Simulations on both multivariate synthetic data and real world postural sway analysis support the approach.