Performance Enhancement and Accident Reduction in Complex Systems: Perspectives and a Research Program

This is the first year of the new Track on Complex Systems. No doubt the idea of what a complex system is will be different to different people. For the purpose of this Track, a complex system may be large or small in scale. An important characteristic, however, is that such a system exhibiting a behavior under stress that is difficult to predict. This may be because models are not well understood (i.e. load models in electric power systems, behavioral models in social and economic systems). It may be because the number of variables is so large that it is beyond simulation capabilities of current computers, or because the relation between a large number of variables is so complex that current mathematics or simulation methods are inadequate. This track seeks to explore methods at the frontier of understanding complex system phenomena and the electric power system is a worthy example of such a system.There are five mini-tracks in this Track. The mini-track on Information Management seeks to explore techniques for managing and visualizing large-scale models that may be distributed across multiple operating authorities. Papers that cover both distribution and transmission network applications are scheduled for presentation.Another Mini-track focuses on topics related to the ability of complex systems such as power systems to survive disturbances with minimal impact on performance. Specific topics to be presented are steady state and dynamic security assessment where the impacts of pre-specified contingencies are analyzed and Available Transfer Capability (ATC), which quantifies the ability of the interconnected system to accept increases in power, transfers.Many large complex systems exhibit evidence of self-organized criticality. Issues such as the role of network size and topology along with the influence of network loading and operation on self-organized criticality are of interest. Evidence that large network disturbances are of a self-organized type and mechanisms of self-organized behavior in large networks are to be presented.Hybrid systems can be viewed as systems that allow interactions between discrete events and continuous dynamics. As such, they are natural models for complex interactive networks and systems such as manufacturing, power, communications, and transportation systems. A satisfactory theory for such systems, which draws from several disciplines including control theory, computer science, and applied mathematics, will have an enormous impact on the design, synthesis, and operations of many practical systems. Computational and algorithmic approaches to such problems encounter considerable difficulties. In addition to modeling and analysis of such systems, this mini-track explores novel computational paradigms that are able to accommodate uncertainties in the system at various levels.Finally, there are three sessions in the mini-track on Markets and Economics. The aim of this mini-track is to explore the ability of commercial trading models to effectively represent the complex physical behavior of an electricity industry, an issue that is critical to the success of electricity industry restructuring. Important aspects of this issue include the design of efficient spot markets and ancillary service markets, and mechanisms to incorporate network effects in electricity trading models. Papers will be presented that address these and other aspects of this important problem.

Public health systems have suffered infrastructure losses worldwide. Strengthening public health systems requires not only good policies and programs, but also development of new research methodologies to support public health systems renewal. Our research team considers public health systems to be complex adaptive systems and as such new methods are necessary to generate knowledge about the process of implementing public health programs and services. Within our program of research, we have employed situational analysis as a method for studying complex adaptive systems in four distinct research studies on public health program implementation. The purpose of this paper is to demonstrate the use of situational analysis as a method for studying complex systems and highlight the need for further methodological development.

Genomics, proteomics, and metabolomics have all vastly advanced our understanding of human biology and disease. But the functioning of even a simple system such as a single yeast cell or bacterium is much more complicated than the sum of its genes or proteins or metabolites; it’s the activity of all those components and their relationships to one another that add up to a living organism. Recognizing that complexity, the emerging field of systems biology attempts to harness the power of mathematics, engineering, and computer science to analyze and integrate data from all the “omics” and ultimately create working models of entire biological systems. “Traditionally, scientists—toxicologists included—have relied on a reductionist approach to biology,” says William Suk, director of the NIEHS Center for Risk and Integrated Sciences. Even now, many studies examine complex systems by looking at cellular components in isolation. For instance, a common experiment involves using DNA microarrays to observe the effect of a chemical exposure on thousands of genes at once. This technique can quickly tell a scientist which genes may be vulnerable to that exposure. But a systems biology approach would attempt to model not only the chemical’s effect on gene expression but also how that expression will affect protein function, and in turn how the exposure will affect cell signaling. “There’s nothing wrong with what we’ve been doing,” Suk says. “But systems biology is going to take it to another level.”

<i>Introduction to the Modeling and Analysis of Complex Systems.</i> H. Sayama (Ed.). (2015, Open SUNY Textbooks). Free open access PDF, 498 pp. ISBN 978-1-942341-06-2 (deluxe color edition). ISBN 978-1-942341-08-6 (print edition). ISBN 978-1-942341-09-3 (ebook).

&lt;div class="htmlview paragraph"&gt;The changing, growing, and, hopefully, improving application of the behavioral sciences and related disciplines to the conceptualization, development and use of complex work systems demands adequate conceptualization about human performance. A useful framework for understanding the varieties of human performances in the complex system or formal work organization is presented. The approach here is &lt;u&gt;interactionist&lt;/u&gt;, structuring human performance along the lines of the principal interactions of the human with the system environment and positing a fundamental “Performance Grid.” Multi-dimensional models are also discussed as basic to the systematic development of measurable aspects of human performance.&lt;/div&gt;

The monograph reveals the basics of complexity theory and methods for assessing complexity. The concept of complexity consideration is based on the analysis of complexity as a common attribute in processes and systems. The monograph describes the main methods for assessing different types of complexity. The concept of considering complexity in this monograph is also based on the fact that complexity is a comparative characteristic. It is given on a relative scale of difficulty. Therefore, complexity must be defined on a relative scale of “simplicity-complexity.” This concept motivates the consideration and analysis of the concept of “simplicity” as a complement to the concept of “complexity”. These concepts set the scale of complexity. The monograph provides a comparative analysis of the related concepts of simplicity and complexity. Three methods for assessing complexity are described: expert assessment of complexity, assessment of complexity using mathematical metrics, comparative assessment of complexity based on the theory of comparative analysis. The monograph contains a taxonomy of the main types of complexity. The content of the main types of complexity is revealed in detail: descriptive complexity, system complexity, modeling complexity, computational complexity. algorithmic complexity, deterministic complexity. Specific cognitive difficulties are described in detail. For cognitive complexity, special assessment methods are used. An interpretation of the concept of cognitive filter is given. Complexity is associated with the concept of complex systems. In most monographs on complex systems, the complexity aspect has not been considered or is viewed in a simplified manner. This monograph examines complexity as a characteristic of complex systems and the basis for their classification. Emergence is described as a characteristic of the complexity of systems and complex processes. The monograph contains a taxonomy of complex systems with characteristics of the complexity of different systems. Complex data systems have been explored. An analysis of organizational complex systems is given. Various types of complex ergatic systems have been described. An analysis of complex technical systems is given. Self-developing complex systems are described. autopoiesis of a complex organizational and technical system has been studied as a principle of systems development. Cyber-physical systems are described as an example of the development of complex systems. The monograph is intended for specialists in the field of computer science, systems analysis, artificial intelligence and philosophy of information.

System reliability is important for systems engineers, since it is directly related to a company's reputation, customer satisfaction, and system design costs. Improving system reliability has been an important task for the system engineers and a number of studies have been published to discuss methods for improving system reliability. For this purpose sensitivity analysis and fault diagnosis has been used in various studies, where identification of significant and problematic components plays an important role. Both sensitivity analysis and fault diagnosis require understanding the system structure and component relationships; and Bayesian networks (BN) have been shown to be an effective tool for modeling the systems and quantifying the component interactions. In this study, we use BN for sensitivity analysis and fault diagnosis to improve system reliability. We focus on the complex systems, where the number of components and component interactions can be very large. In this study, we first discuss sensitivity analysis in complex systems using BN, which can be used for identification of significant system components. Sensitivity analysis using BN is concerned with the question of how sensitive system reliability is to possible changes in the nodes in BN. In this paper we demonstrate that BN can be efficiently and effectively used for sensitivity analysis in complex system reliability. This study is the first that considers component reliabilities and uses BN for sensitivity analysis in complex systems. In this paper as a part of our method for sensitivity analysis, an efficient algorithm (SA) is introduced to perform sensitivity analysis in complex systems. Our SA algorithm is based on a graph traversal algorithm that can be effectively used in BN. The SA algorithm traverses the BN through the connected nodes and evaluates the reliabilities to perform sensitivity analysis. Our method helps the systems engineers understand the cause and effect relationships between system components and their reliability and discover the key components that have significant effects on system reliability. Once the key components are identified, system structure can be revised to improve the overall system reliability. Next we discuss fault diagnosis in complex systems and show how fault diagnosis can be used to improve complex system reliability. Due to component aging and environmental factors, the system components in real-life complex systems may fail or not function as expected. Such failures may cause unprecedented changes in the system reliability values and affect the reliability of not only the failed component, but also the overall system. One important issue in complex systems is that, the system engineers must process large amounts of information before making operational decisions. Since BN combine expert knowledge of the system with probabilistic theory for construction of effective diagnosis methodologies, they have been applied to fault diagnosis in various studies. In this paper, we present a new method for fault diagnosis in complex systems. Our method uses the complex system reliability to detect the faulty components. We continuously monitor the overall system reliability value, and our fault diagnosis mechanism is only triggered when significant changes to system reliability are detected. As part of our method, an efficient search algorithm is designed specifically for BN. This algorithm is empowered with popular heuristics. In this paper, we discuss how our method can be efficiently applied to complex systems since our search algorithm needs to check only a small portion of the system's components before detecting the failed one. We believe that our method provides system engineers with invaluable information to diagnose the faulty component and improve reliability in complex systems.

System reliability is important for systems engineers, since it is directly related to a company's reputation, customer satisfaction, and system design costs. Improving system reliability has been an important task for the system engineers and a number of studies have been published to discuss methods for improving system reliability. For this purpose sensitivity analysis and fault diagnosis has been used in various studies, where identification of significant and problematic components plays an important role. Both sensitivity analysis and fault diagnosis require understanding the system structure and component relationships; and Bayesian networks (BN) have been shown to be an effective tool for modeling the systems and quantifying the component interactions. In this study, we use BN for sensitivity analysis and fault diagnosis to improve system reliability. We focus on the complex systems, where the number of components and component interactions can be very large. In this study, we first discuss sensitivity analysis in complex systems using BN, which can be used for identification of significant system components. Sensitivity analysis using BN is concerned with the question of how sensitive system reliability is to possible changes in the nodes in BN. In this paper we demonstrate that BN can be efficiently and effectively used for sensitivity analysis in complex system reliability. This study is the first that considers component reliabilities and uses BN for sensitivity analysis in complex systems. In this paper as a part of our method for sensitivity analysis, an efficient algorithm (SA) is introduced to perform sensitivity analysis in complex systems. Our SA algorithm is based on a graph traversal algorithm that can be effectively used in BN. The SA algorithm traverses the BN through the connected nodes and evaluates the reliabilities to perform sensitivity analysis. Our method helps the systems engineers understand the cause and effect relationships between system components and their reliability and discover the key components that have significant effects on system reliability. Once the key components are identified, system structure can be revised to improve the overall system reliability. Next we discuss fault diagnosis in complex systems and show how fault diagnosis can be used to improve complex system reliability. Due to component aging and environmental factors, the system components in real-life complex systems may fail or not function as expected. Such failures may cause unprecedented changes in the system reliability values and affect the reliability of not only the failed component, but also the overall system. One important issue in complex systems is that, the system engineers must process large amounts of information before making operational decisions. Since BN combine expert knowledge of the system with probabilistic theory for construction of effective diagnosis methodologies, they have been applied to fault diagnosis in various studies. In this paper, we present a new method for fault diagnosis in complex systems. Our method uses the complex system reliability to detect the faulty components. We continuously monitor the overall system reliability value, and our fault diagnosis mechanism is only triggered when significant changes to system reliability are detected. As part of our method, an efficient search algorithm is designed specifically for BN. This algorithm is empowered with popular heuristics. In this paper, we discuss how our method can be efficiently applied to complex systems since our search algorithm needs to check only a small portion of the system's components before detecting the failed one. We believe that our method provides system engineers with invaluable information to diagnose the faulty component and improve reliability in complex systems.

Industry 4.0 in the contemporary operating context carries important sources of complexity. This context generates both traditional risks and emerging risks that must be managed. The management of these risks includes both industrial risks and occupational risks, since they are heavily interlinked. The human factor can be considered the main link between both types of risks. Thus, understanding risks originating from human errors and organizational weaknesses as causes of accidents and other disruptions in complex systems requires elaborating sophisticated modeling approaches. Therefore, the objective of this paper is to propose an organizational and human performance approach to improve the emerging risk management linked to the complex systems, like as Human‐Machine Interactions (HMI) and Human‐Robot Interaction (HRI). To fulfill this objective, we first introduce the concept of emerging risk linked to human factor. Then, we introduce the concept of emerging risk management in the Industry 4.0 context. Under this complex context, we expose the concept considering the current models of risk management. Finally, we discuss how enhancing human and organizational performance can be achieved through risk management in complex systems linked to Industry 4.0. Therefore, we conclude that while Industry 4.0 brings numerous advantages, it must contend with emerging risks and challenges associated with organizational and human factors. These emerging risks include industrial risks as well as occupational risks. Moreover, the human factor aspect of Industry 4.0 is directly linked to industrial emerging and occupational emerging via context of operations. To cope with these new challenges, it is necessary to develop new approaches. One of such approaches is Complex System Governance. This approach is discussed along with the need for adequate organizational and human performance models dealing with, for example, experience from other domains such as nuclear, space, aviation, and petrochemical.

The value of sensitivity estimates for determining an optimal set of model parameters has been recognized for over a century. However, the inadequacy of the mathematical foundation of sensitivity has prevented the use of systematic gradient optimization techniques for the analysis of complex water resource systems and hydrologie simulation models. The computer time required to increment each parameter and to measure the change in output is considered excessive for optimization of complex systems. The use of component sensitivity provides an alternative to the method of parameter perturbation that greatly reduces the computational effort. The mathematical foundation of component sensitivity is introduced, and the method of computation is demonstrated for a two‐component system. However, the mathematical foundation of component sensitivity is easily extended to more complex systems.

&lt;p&gt;This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.&lt;/p&gt;

The integration of soft ionization technologies and ultrahigh resolution FTICR mass spectrometry (Fourier transform ion-cyclotron MS) has enabled the examination of molecules, directly from mixtures, with ultrahigh mass resolution and sub-ppm mass accuracy. This ability to observe structure and reactivity aspects of intact (macro)molecules has, in particular, contributed to the molecular-level characterization of complex matrices, for example novel materials and biological and biogeochemical samples. Here, thousands of molecular formulae can be determined in a single measurement directly from mixtures. The exceptional performance of ultrahigh-resolution FTICR mass spectroscopy in the characterization of complex systems suffers much less from the detrimental effects of intrinsic averaging, which is a characteristic of most other methods of organic structural spectroscopy. This novel information-rich and direct perception has the potential to entirely change the way we address the molecular-level analysis of complex materials, systems, and processes in general, thereby opening novel perspectives in data-driven systems biology and “omics” approaches. The inauguration of Europe’s first 12 Tesla FTICR mass spectrometer at the GSF National Research Center for Environment and Health provided the opportunity to hear expert views on novel research dedicated to the molecular-level characterization of complex systems. A dozen presentations organized in four sessions were delivered during the “First International Symposium on Ultrahigh-Resolution Mass Spectrometry for the Molecular Level Analysis of Complex (BioGeo)Systems”, held at the GSF on November 6 and 7, 2006 (www.gsf.de/FTMS2006). In session one, General Foundations of FTICR Mass Spectrometry, Alan G. Marshall (National High Magnetic Field Laboratory, Tallahassee, USA) provided a wide-ranging overview about the currently available opportunities of FTMS in molecular-resolution analysis: High-field Fourier transform ion cyclotron resonance mass spectrometry: a platform for “omics”. Then, Eugene N. Nikolaev (Institute of Biochemical Physics, Moscow, Russian Federation) investigated the complex trajectories of ions in FTMS cells: The new possibilities in ion clouds dynamic simulation using supercomputers. Application to FTICR and quadrupole devices. Arnd Ingendoh (Bruker Daltonics, Bremen, Germany) provided an overview about current and foreseeable trends in modern mass spectrometry: Mass spectrometry and beyond—new areas of applications. Session two, From Biological Chemistry to Chemical Biology, focused on life sciences applications. Jeremy K. Nicholson (Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Imperial College, London, UK) produced a comprehensive assessment about integrated views in “omics” sciences (Metabonomics and top-down systems biology: from personalized healthcare to molecular epidemiology) and Hannelore Daniel (Technical University Munich, Weihenstephan, Germany) complemented this interesting and versatile perspective with The bottom-up approach: from individual genes to the systems biology level of nutrition. Michael Sattler (European Molecular Biology Laboratory, Heidelberg, Germany and GSF Research Center for Environment and Health) illustrated the power of modern NMR methods capable of producing well resolved three-dimensional structures of large biomolecules including description of dynamics and reactivity: NMR to study molecular recognition and dynamics of biomacromolecules. Session three, Novel MS Approaches in Biochemistry and Medicine, illustrated novel MS approaches by Gary Siuzdak (Scripps Center for Mass Spectrometry, La Jolla, USA): Novel mass-based approaches to proteomics and metabolomics and advanced carbohydrate analysis by Jasna Peter-Katalinic (Institute fur Medical Physics and Biophysics, Munster, Germany): Requirements on FTICR mass spectrometry for glycomics in structural biology and medicine. Session four, Repetitive and Non-Repetitive Complex Systems, focused on proteome and complex biogeochemical mixture analysis. Michael Przybylski (Department of Chemistry, University of Konstanz, Germany) elucidated aspects of advanced protein analysis High resolution FTICR mass spectrometry and affinity-proteomics: powerful tool for elucidation of antigen-antibody recognition structures and vaccine development in molecular immunology and complex biogeochemical materials and William T. Cooper (Department of Chemistry and Biochemistry, FSU, Tallahassee, USA) provided insights into the structure, synthesis and degradation of natural organic matter: Insights into organic chemistry of the natural environment; FTICR mass spectrometry of really complex mixtures. Norbert Hertkorn (GSF Institute of Ecological Chemistry, Neuherberg, Germany) described the specific utility and complementarity of NMR and mass spectrometry in the analysis of complex biogeochemical systems: Mapping the molecular compositional space with mass spectrometry, and Philippe Schmitt-Kopplin (GSF Institute of Ecological Chemistry, Neuherberg, Germany) provided a comprehensive assessment of biogeochemical analysis with a variety of current examples: Molecular-level analysis of non-repetitive structures in biogeosystems. To recognize the importance of the technique and provide the reader of the journal Analytical and Bioanalytical Chemistry with a perspective of the current trends in FTICR mass spectrometry, this special issue on “Ultrahigh Resolution Mass Spectrometry” was prepared to include research, perspective, and review articles from experts in the field. This special issue reflects the wide scope of current FTICR mass spectrometry utilization in the analytical characterization of natural products, biomolecules and biogeochemical mixtures, hyphenation of separation and mass spectrometry, and integration of spectroscopy and separation in the molecular-level characterization of complex systems. At this point, we would like to thank all the authors for their high-quality articles, all referees for the rapid evaluations.

The purpose of this research was to develop and deploy a systems-based framework for analysis of complex governance systems using a multimethodology research design. Two research gaps motivated this research: (1) lack of an integrated conceptualization of a system governance construct, (2) an absence of studies that consider both the governed and governing systems as well as the emergent interactions that arise from within complex governance systems. The research focused on three primary questions: (1) What are the distinctive characteristics of governance?; (2) What system-based framework can be developed for analysis of governance in complex systems?, and (3) What results from deployment of the framework in a field setting? The multimethodology research design that guided the effort included three primary phases. First, the literature was synthesized to derive a set of governance elements. This synthesis was accomplished across an extensive and multidisciplinary literature set by a novel method of content document clustering analysis to reveal important elements of governance. Second, a conceptual framework for analysis of system governance was constructed from the confluence of extant governance literature and systems theory. This governance system analysis framework was informed by Bunge's (2003) system perspective to advance the understanding of governance that will be meaningful in a given practice. Finally, a case based application of the analysis framework was conducted to examine implications of the framework from a field perspective. The original research provided contributions to theory, methodology, and practice. From a theoretical perspective, the research contributed to the body of knowledge by providing: (1) a literature derived set of generalizable elements of governance, and (2) the development of a systems-based framework to be used to analyze complex governance systems. From a methodological stand-point, the research advanced an integrated multimethodology research design that featured: (1) a novel content analysis approach for synthesis of diverse literature; (2) the development of an integrated systems analysis method; and (3) a rigorous single-case study application within the engineering management discipline. Lastly, from a practical perspective, the systems framework provided a foundation for derivative approaches to enhance practices related to system governance.

“ . . . beyond methods and tools, the articles in this special issue are proof that complexity science has provided IE an overarching knowledge paradigm that matches the continuously evolving resource, production, and consumption systems that are the object of study in the field.” co-authorship networks, and offer application of complex systems models and analyses. The articles demonstrate the links, relevance, and implications of many (often emerging) fields of study to IE, including network analysis, participatory modeling, nonequilibrium thermodynamics, and agent-based modeling. Together, these articles show that IE itself is a complex adaptive system, where knowledge, frameworks, methods, and tools evolve with and by their applications and use in small and large case studies— multidisciplinary knowledge ecology. In the special issue “Complexity and Industrial Ecology” (Volume 13, Number 2, 2009), Dijkema and Basson (2009, 157) propose that “ . . . complexity theory and its tools has potential to shift the frontier of Industrial Ecology, by enhancing the quality of systems analysis and by underpinning recommendations for redirecting industrial development towards sustainability.” Indeed, in action-oriented IE (Nikolic et al. 2009), we arguably study “complex, layered and dynamic systems that interact with their environment and thereby perpetually affect one another” (Dijkema and Basson 2009, 157). Indeed, we study sociotechnical systems, where the social evolves the technical and vice versa (de Bruijn and Herder 2009). Both their evolution and impact occur at multiple spatial, temporal, and systems scales. Where it has been argued that “sustainability” is an anthropocentric, normative concept (Allenby 2009; Ehrenfeld 2007), from complexity science we may learn that sustainability is an emerging characteristic of the complex adaptive system of our planet earth and any subsystem or part thereof (Nikolic et al. 2009). Taking a complex systems approach and applying complex systems methods can thus deepen and broaden our understanding of resource, production, and consumption systems. In fact,

The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.