Emergent Properties Of Complex Systems Research Articles

Evolution of temporal fluctuation scaling exponent in nonstationary time series using supersymmetric theory of stochastic dynamics.

Temporal fluctuation scaling (TFS) is an emergent property of complex systems that relates the variance (Ξ_{2}) and the mean (M_{1}) from an empirical data set in the form Ξ_{2}∼M_{1}^{α_{TFS}}, where the dispersion (fluctuation) of the data has been described in terms of Ξ_{2}. At present, it has been shown that this law of complex systems has different multidisciplinary applications such as characterizing the market rate based on its exponent, explaining the spatial spread of a pandemic or measuring dispersion in a counting process, among others, if it is known how the average value M_{1} of a representative quantity in a system changes. Then, using the path integral formalism and Parisi-Sourlas method, we propose an extension of path integral formalism to understand the origin of the temporal fluctuation scaling and the evolution of its exponent over time in nonstationary time series. To this end, we first show how the probability of transition between two states of a stochastic variable x(t) can be expressed once it is known its cumulant generating function. Also, we introduce a nonlinear term in a cumulant generating function of the form H^{(n)}(p,t;γ)∼p^{n} to obtain a model where the nth moment of the probability distribution evolves arbitrarily. Subsequently, in order to reproduce the temporal fluctuation scaling, a linear combination of H^{(n)}(p,t;γ) with n∈{1,2} is used. Therefore this allows describing how the mean M_{1}(t) and the variance Ξ_{2}(t) of empirical time series evolve. Thence, an analytical expression is deduced for the evolution of the temporal evolution of the temporal fluctuation scaling exponent α_{TFS}(t). Likewise, the validity of the expression found for α_{TFS}(t) is verified with a toy model based on white noise. Finally, this approach is verified in two stock indices (Dow Jones and Sao Paulo stock index) and two currencies (GBP-USD and EUR-USD) with daily data. It is found that this approach accurately captures the evolution of the mean and variance of these four financial derivatives after contrasting the results with a coefficient of determination that depends on H^{(n)}(p,t;γ). Also, it is shown that the temporal fluctuation scaling exponent is a measure of uncertainty or volatility in financial time series.

System Theoretic Process Analysis for Identification of Sources of Mode Confusion

As systems grow increasingly complex, the likelihood of mode confusion regarding automation and system processes also increases. Mode confusion stems from inaccurate mental models that lead operators to execute inappropriate control actions that result in losses. System Theoretic Process Analysis (STPA) is a holistic risk analysis method that examines the emergent properties of complex systems with interacting components. Such components may include humans, software, organizations, safety culture, and more. By allowing users to clearly understand controllers’ process models, STPA provides a methodology to identify sources of mode confusion and generate requirements to eliminate them. This paper presents an approach to conducting STPA that is tailored to issues of mode confusion in systems with interactions between human and automated controllers. Additionally, an example STPA is applied to the Boeing 777 autopilot system to illustrate how inappropriate feedback and mode confusion can be identified and prevented in complex systems.

Reflections on the fundamental nature of homeopathic potencies: Plasma, Langmuir Adsorption and Vitalism

Background: There is a pressing need to develop methods and approaches that will identify the fundamental nature of homeopathic potencies. Aims: To bring together recent basic research on potencies, especially that using solvatochromic dyes, and to supplement these results with reliable observations made by Hahnemann and his contemporaries from the very beginnings of homeopathy, together with a detailed examination of the process of trituration and succussion coupled to dilution, in order to significantly limit the number of possible explanations as to the identity of potencies. Methodology: A mixture of lab based and literature studies such that as far as possible all verified and substantiated observations about homeopathic potencies have been examined. Results and Discussion: An understanding of the fundamental nature of homeopathic potencies that includes all known and accepted observations (in vitro, in vivo and clinical) is not realistic without embracing hypotheses involving the emergent properties of complex systems and in particular, vitalistic concepts. Using a vitalistic model it is possible to explain a wide range of seemingly unrelated phenomena - such as the polarising effect of potencies on solvatochromic dyes, the ability to use a range of materials such as water, lactose and cellulose as carriers of potencies, the administering of potencies by olfaction, the antidoting effect of camphor on potency action, the non-linear dependence of potency strength on volume as well as succussion level, the oscillatory behaviour of potencies and experimenter/observer/practitioner effects. Conclusion: A hypothesis in which homeopathic potencies can be seen as self-actuating and autonomous plasma generated by trituration and/or succussion and carried according to Langmuir adsorption models fits the known observations about potencies.

Meeting the Moment: Leveraging Temporal Inequality for Temporal Targeting to Achieve Water-Quality Load-Reduction Goals

Inequality is an emergent property of complex systems. In catchments, variation in hydroclimatic conditions and biogeochemistry cause streamflow and constituent loads to exhibit strong temporal inequality, with most loads exported during “hot moments”. Achieving water-quality-restoration goals in a cost-effective manner requires targeted implementation of conservation practices in “hot spots” in the landscape and “hot moments” in time. While spatial targeting is commonly included in development of watershed management plans, the need for temporal targeting is often acknowledged, but no common way to address it has been established. Here, we implement a Lorenz Inequality decision-making framework that uses Lorenz Curves and Gini Coefficients to quantify the degree of temporal inequality exhibited by contaminant loads and demonstrate its utility for eight impaired catchments in the Chesapeake Bay watershed. The framework requires a load-reduction goal be set and then links the degree of temporal inequality in annual nutrient loads to the periods of time during which those loads could be targeted. These results are critical in guiding development of site-specific, cost-effective tools that facilitate load-reduction and water-quality goal attainment for individual catchments. The framework provides valuable insight into site-specific potentials for meeting load-reduction goals.

Adaptation, Transformation and Resilience in Healthcare Comment on "Government Actions and Their Relation to Resilience in Healthcare During the COVID-19 Pandemic in New South Wales, Australia and Ontario, Canada".

Adaptive capacity is a critical component of building resilience in healthcare (RiH). Adaptive capacity comprises the ability of a system to cope with and adapt to disturbances. However, "shocks," such as the current coronavirus disease 2019 (COVID-19) pandemic, can potentially exceed critical adaptation thresholds and lead to systemic collapse. To effectively manage healthcare systems during periods of crises, both adaptive and transformative changes are necessary. This commentary discusses adaptation and transformation as two complementary, integral components of resilience and applies them to healthcare. We treat resilience as an emergent property of complex systems that accounts for multiple, often disparately distinct regimes in which multiple processes (eg, adaptation, recovery) are subsumed and operate. We argue that Convergence Mental Health and other transdisciplinary paradigms such as Brain Capital and One Health can facilitate resilience planning and management in healthcare systems.

First-passage times to quantify and compare structural correlations and heterogeneity in complex systems

Virtually all the emergent properties of complex systems are rooted in the non-homogeneous nature of the behaviours of their elements and of the interactions among them. However, heterogeneity and correlations appear simultaneously at multiple relevant scales, making it hard to devise a systematic approach to quantify them. We develop here a scalable and non-parametric framework to characterise the presence of heterogeneity and correlations in a complex system, based on normalised mean first passage times between preassigned classes of nodes. We showcase a variety of concrete applications, including the quantification of polarisation in the UK Brexit referendum and the roll-call votes in the US Congress, the identification of key players in disease spreading, and the comparison of spatial segregation of US cities. These results show that the diffusion structure of a system is indeed a defining aspect of the complexity of its organisation and functioning.

Neurobiology of substance use disorder-recent advances

Changes in neurocircuits, molecular changes and the genetic variations those are responsible for the neuroadaptations mediate the development of addiction. Addictive drugs can trigger epigenetic mechanisms that modulate the expression of genes implicated in neuroplasticity ultimately perturbing the intracellular level of key proteins, and modifying neurotransmitter signaling in various neuronal circuits in the brain. The resultant behavioural dysfunctions in addiction reflect the emergent property of complex systems that are disrupted at multiple, interacting levels.

Distributed Computing Column 77 Consensus Dynamics

Overview. In this edition of the column, we have an exciting contribution from Luca Becchetti, Andrea Clementi, and Emanuele Natale, who provide an in-depth algorithmic perspective on emergent complexity. Roughly, this area aims to characterize non-trivial emergent properties of complex systems, composed of large numbers of relatively simple agents, which can cooperate to express complex global behaviours. Interestingly, over the past two decades, fundamental processes such as consensus or opinion-formation dynamics have been studied independently by different research communities: for instance, in the Distributed Computing community, these dynamics have been studied in the context of population protocols via discrete-time analysis, whereas, in the Control and Optimization community, similar (or even identical) dynamics have been analyzed via continuous-time processes. One very useful feature in this column's contribution is the fact that it provides a unified view of these results, along with the mathematical background to understand and differentiate the underlying results.

A Functional Protection Method for Availability and Cost Risk Management of Complex Research Facilities

While the area of functional safety for industry has evolved over the years by developing well-renowned standards and methods proven in use, such standards are not present in the field of equipment protection for complex research facilities. Research facilities in particular do typically not apply global, systematic approaches for functional equipment protection, but rather rely on system expert judgment for the individual systems, often leaving out the emergent properties of complex systems of systems. With the increasing demands on research facilities to be highly reliable and available, comparable to industrial facilities, a holistic and systematic method for equipment protection is, if not necessary, highly beneficial. This paper describes the development of a risk management method named functional protection, and benchmarks its lifecycle steps toward the IEC 61508 functional safety standard as well as the ISO 31000 risk management standard. The functional protection method is currently applied for the design and construction of the European Spallation Source in Lund, Sweden.

Threshold Based Footprints (for Water)

Thresholds are an emergent property of complex systems and Coupled Natural Human Systems (CNH) because they indicate “tipping points” where a complicated array of social, environmental, and/or economic processes combine to substantially change a system’s state. Because of the elegance of the concept, thresholds have emerged as one of the primary tools by which socio-political systems simplify, define, and especially regulate complex environmental impacts and resource scarcity considerations. This paper derives a general framework for the use of thresholds to calculate scarcity footprints, and presents a volumetric Threshold-based Water Footprint (TWF), comparing it with the Blue Water Footprint (BWF) and the Relevant for Environmental Deficiency (RED) midpoint impact indicator. Specific findings include (a) one requires all users’ BWF to calculate an individual user’s TWF, whereas one can calculate an individual user’s BWF without other users’ data; (b) local maxima appear in the Free from Environmental Deficiency (FED) efficiency of the RED metric due to its nonlinear form; and (c) it is possible to estimate the “effective” threshold that is approximately implied by the RED water use impact metric.

Resilience in ecotoxicology: Toward a multiple equilibrium concept

The term resilience describes stress-response patterns across scientific disciplines. In ecology, advances have been made to clearly define resilience based on underlying mechanistic assumptions. Engineering resilience (rebound) is used to describe the ability of organisms to recover from adverse conditions (disturbances), which is termed the rate of recovery. By contrast, the ecological resilience definition considers a systemic change, that is, when ecosystems reorganize into a new regime following disturbance. Under this new regime, structural and functional aspects change considerably relative to the previous regime, without recovery. In this context, resilience is an emergent property of complex systems. In the present study, we argue that both definitions and uses are appropriate in ecotoxicology, and although the differences are subtle, the implications and uses are profoundly different. We discuss resilience concepts in ecotoxicology, where the prevailing view of resilience is engineering resilience from chemical stress. Ecological resilience may also be useful for describing systemic ecological changes because of chemical stress. We present quantitative methods that allow ecotoxicologists and risk managers to assess whether an ecosystem faces an impending regime shift or whether it has already undergone such a shift. We contend that engineering and ecological resilience help to distinguish ecotoxicological responses to chemical stressors mechanistically and thus have implications for theory, policy, and application. Environ Toxicol Chem 2017;36:2574-2580. © 2017 SETAC.

The Next Decade of Big Data in Ecosystem Science

Ecosystem scientists will increasingly be called on to inform forecasts and define uncertainty about how changing planet conditions affect human well-being. We should be prepared to leverage the best tools available, including big data. Use of the term ‘big data’ implies an approach that includes capacity to aggregate, search, cross-reference, and mine large volumes of data to generate new understanding that can inform decision-making about emergent properties of complex systems. Although big-data approaches are not a panacea, there are large-scale environmental questions for which big data are well suited, even necessary. Ecosystems are complex biophysical systems that are not easily defined by any one data type, location, or time. Understanding complex ecosystem properties is data intensive along axes of volume (size of data), velocity (frequency of data), and variety (diversity of data types). Ecosystem scientists have employed impressive technology for generating high-frequency, large-volume data streams. Yet important challenges remain in both theoretical and infrastructural development to support visualization and analysis of large and diverse data. The way forward includes greater support for network science approaches, and for development of big-data infrastructure that includes capacity for visualization and analysis of integrated data products. Likewise, a new paradigm of cross-disciplinary training and professional evaluation is needed to increase the human capital to fully exploit big-data analytics in a way that is sustainable and adaptable to emerging disciplinary needs.

Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems.

Cell Division in the Light of Modeling

Theoretical modeling is central to elucidating underlying principles of emergent properties of complex systems. In cell and developmental biology, the last 15 years have witnessed a convergence of empirical and modeling approaches for fresh perspectives. The role of cell division in coordinating size, shape, and fate in particular illustrates the ever-growing impact of modeling.

Application of maturity assessment tools in the innovation process: converting system's emergent properties into technological knowledge

This paper aims at establishing a theoretical construction between the concept of learning-by-using and the concepts of Technological Readiness Level (TRL) and System Readiness Level (SRL). The concept of learning-by-using reveals that the technical change that takes place in complex systems is given by the sum of small improvements in many different technological disciplines integrated in a specific configuration of this system. This kind of learning results from the iterative combination of scientific and technological knowledge, which is generated by the extensive use of products and their associated production processes. A stock of this combined knowledge might be required to cope with emergent properties of complex systems. The pattern of complex systems evolution involves the balance of technological and scientific frontiers as well as the fulfillment of customer expectation. Every innovation involves systemic uncertainty, which is positively correlated to the magnitude of the change introduced into the complex system. Maturity level of technological solutions allows organizations to assess pragmatically strategic risk exposure of implementing complex system innovation. The concept of SRL represents a proficuous tool to unveil emergent properties, which consider both the TRL of individual elements and how they are integrated into a complex system.

Adaptive Policies for Reducing Inequalities in the Social Determinants of Health.

Inequalities in the social determinants of health (SDH), which drive avoidable health disparities between different individuals or groups, is a major concern for a number of international organisations, including the World Health Organization (WHO). Despite this, the pathways to changing inequalities in the SDH remain elusive. The methodologies and concepts within system science are now viewed as important domains of knowledge, ideas and skills for tackling issues of inequality, which are increasingly understood as emergent properties of complex systems. In this paper, we introduce and expand the concept of adaptive policies to reduce inequalities in the distribution of the SDH. The concept of adaptive policy for health equity was developed through reviewing the literature on learning and adaptive policies. Using a series of illustrative examples from education and poverty alleviation, which have their basis in real world policies, we demonstrate how an adaptive policy approach is more suited to the management of the emergent properties of inequalities in the SDH than traditional policy approaches. This is because they are better placed to handle future uncertainties. Our intention is that these examples are illustrative, rather than prescriptive, and serve to create a conversation regarding appropriate adaptive policies for progressing policy action on the SDH.

Syntactic Networks as an Endophenotype of Developmental Language Disorders: An Evo-Devo Approach to Clinical Linguistics

Usually, developmental language disorders are defined either symptomatically (based on a constellation of linguistic deficits appearing recurrently within a population) or etiologically (on the basis of a common underlying deficit), or both. On paper, each of these clinical categories is expected to be distinguished from other close entities at several levels of analysis (phenotypic, cognitive, neurobiological, genetic, etc.). Nonetheless, this is not typically the case: Comorbidity, variability, and heterogeneity are in fact a common outcome of the clinical practice. Ultimately, different disorders may share the same underlying deficit (e.g., phonological dysfunction in dyslexia and SLI); conversely, different deficits may give rise to the same disorder (e.g., both visual problems and phonological deficits may contribute to dyslexia) (Benitez-Burraco 2013). If we want to achieve a better—and earlier—diagnosis of these conditions, we should improve the tools we employ at present. A promising approach is one relying on the endophenotypes of disorders. Endophenotypes may be defined as cognitive, neuroanatomical, neurophysiological, endocrine, or biochemical quantifiable components of the space between genes and diseases (Gould & Gottesman 2006). Endophenotypes refer to more specific (and more physiological) aspects of the body function, therefore they allow us to gain a more accurate diagnosis of its dysfunction (Gottesman & Gould 2003). Here we would like to advance a putative endophenotype of language disorders that combines four factors: (1) linguistic analysis (syntactic computation), (2) information management (communicative strategies), (3) recent evo-devo insights in the nature of phenotypic variation, and (4) network approaches to emergent properties of complex systems (surely, language it is; Deacon 2005). To begin with, we would like to note that, although the set of pathological conditions already described by clinical linguists is ample, it is not unlimited either. In other words, variation is constrained or canalized, even in pathological states. At the same time, we observe that language is both sensitive to damage (e.g., some aspects of language processing are perturbed in nearly all disorders, like the proper use of inflectional cues in verbal and nominal morphology) and resistant to perturbation (e.g., a nearly functional language faculty may emerge at the term of growth in spite of severe underlying deficits).

Sociomaterial Enactment Drive of Business/IT Alignment: From Small Data to Big Impact

Abstract Business/IT alignment is an information systems research field with a long existence and a high number of researchers and represents a central direction on the thinking about the relation between business and the information systems. It aims to achieve a paradigm, one in which there is a high degree of visibility and availability of information, about the information systems sociomateriality. Complex-networks constitute an approach to the study of emergent properties of complex systems that strongly focuses and relies on models and measures, through which build the system interdependence. Several contributions of complex-networks are: topology always affects the function; separated from the domain; quantification of element's relationships; visibility and capture of emergent properties. This work expects to contribute for the appropriate use of complex-networks models and measure in the drive of the information systems alignment. This work considers an exploratory case research strategy. It uses an exploratory case developed in the field of information systems that directed its deployment to sustain the business development and evolution. This paper illustrates a profiling framework that introduces a global and elementary use of the sociomaterial enactment. From the analysis of the exploratory case, the paper infers drivers of the information systems Business/IT alignment.

Sustainability science: accounting for nonlinear dynamics in policy and social–ecological systems

Resilience is an emergent property of complex systems. Understanding resilience is critical for sustainability science, as linked social–ecological systems and the policy process that governs them have the capacity for nonlinear dynamics. The possibility of nonlinear change in these systems means that there is an inherent degree of uncertainty in social–ecological systems and the policy process. Abrupt, nonlinear change often results in social and/or ecological surprises that create tremendous challenges for environmental management. Thus, it is necessary to improve environmental management via a suite of mechanisms that have the capacity for adaptation. This paper suggests how we can move closer to achieving this goal through an overarching focus, including reformed and new law, adaptive management and adaptive governance, scenario planning, and leading indicators.

Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.