Construction of a method for the structural functional-cost modeling of a complex hierarchic system

In popular dialogues, describing a system as "complex" is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Transport networks, management infrastructure and supply chain logistics are all often described in this way. Academic dialogues have begun to explore the collective behaviors of complex systems to define a complex system specifically as an adaptive one; i.e. a system that demonstrates 'self organising' principles and 'emergent' properties. Based upon the key principles of interaction and emergence in relation to adaptive and self organising systems in cultural artifacts and processes, this paper will argue that complex systems are cultural systems. By introducing generic principles of complex systems, and looking at the exploration of such principles in art, design and media research, this paper argues that a science of cultural systems as part of complex systems theory is the post modern science for the digital age. Furthermore, that such a science was predicated by post structuralism and has been manifest in art, design and media practice since the late 1960s.

The research presented in this paper presents a framework with an algorithm intended for the development of complex technical systems during their operation in uncertain situations. It is based on the prediction of deviations in the behaviour of complex engineering systems with uncertain operating parameters relative to the behaviour of the same system with the predicted operating parameters. In this case, the unexpected behaviour of the system in a changing working environment is modelled with: the architecture model of a complex technical system and the behaviour model of the same technical system. The model of complex system architecture is based on a matrix representation of the system components using a component Design Structure matrix (DSM) component. A mathematical model with distributed parameters and a model predictive control (MPC) method is used to describe the behavioural model of a complex system. Observed system stability is also verified using the direct Lyapunov method. Bilateral mapping of the obtained data between these two models allows describing and modelling the system behaviour in uncertain situations. The recording of the behaviour of the created complex system is performed using the rules of fuzzy logic. For this purpose, the Adaptive-Network-based Fuzzy Inference System (ANFIS) is used. Verification of the research results was carried out on a real example of a complex technical system - an air handling unit.

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.”

IntroductionThe human body's response to pain is indicative of a complex adaptive system. Therapeutic yoga potentially represents a similar complex adaptive system that could interact with the pain response system with unique benefits.ObjectivesTo determine the viability of yoga as a therapy for pain and whether pain responses and/or yoga practice should be considered complex adaptive systems.MethodsExamination through 3 different approaches, including a narrative overview of the evidence on pain responses, yoga, and complex system, followed by a network analysis of associated keywords, followed by a mapping of the functional components of complex systems, pain response, and yoga.ResultsThe narrative overview provided extensive evidence of the unique efficacy of yoga as a pain therapy, as well as articulating the relevance of applying complex systems perspectives to pain and yoga interventions. The network analysis demonstrated patterns connecting pain and yoga, while complex systems topics were the most extensively connected to the studies as a whole.ConclusionAll three approaches support considering yoga a complex adaptive system that exhibits unique benefits as a pain management system. These findings have implications for treating chronic, pervasive pain with behavioral medicine as a systemic intervention. Approaching yoga as complex system suggests the need for research of mind-body topics that focuses on long-term systemic changes rather than short-term isolated effects.

To discuss multiple organ dysfunction syndrome (MODS) from a complex systems' theory perspective and to delineate a conceptual framework for the development and care of MODS. MODS is an intricate and devastating manifestation of critical illness characterized by widespread aberrant molecular, cellular and systemic responses. Narrative literature review (MEDLINE, CINAHL databases) and knowledge synthesis with the theoretical assertions of chaos and complex systems' theory. Cellular and systemic response paradoxes in MODS (including cellular hypoxia, cell death and signalling) are reviewed. The diseased person is depicted as a complex adaptive system. The relevancy of some of the principles of complex chaotic systems' theory to the proposed model is illustrated, including sensitive dependence on initial conditions, emergence, attractors, self-organization, self-organized criticality and emerging order. The transition from life-supporting to death-related organismic responses is illustrated as a critical event in MODS and care implications are drawn. Patient responses in MODS appear to conform to the principles of chaotic systems. Death is illustrated not as a consequence of homeostatic failure but as a 'deliberate' self-organized phenomenon entailing multiple dynamically evolving mechanisms. Some of the principles of chaotic complex systems may need to be taken into account to advance care in MODS. An alternative theoretical perspective may support nurses to conceptualize both MODS and their role in a way that will help them to cope better with this devastating syndrome and develop practice.

<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).

Task Force on Strategic Research Direction: Basic Science Subgroup key science topics report.

The paper describes the process of choosing the optimal design option for a complex technical system under conditions of interval uncertainty and incomplete information about the parameters and the phase state. Problems of this kind are extremely relevant at the initial stage of designing complex systems, when from a variety of possible options it is necessary to select those that are more consistent with the heterogeneous quality criteria and do not always meet the ambiguous preferences of the decision maker.&#x0D; The aim of the study is to develop effective methods for comparing various design options for complex systems at the initial design stage under conditions of interval uncertainty. The objectives of the study include the construction of a mathematical model for the functioning of a complex system under conditions of uncertainty, as well as the analysis of the main methods for comparing alternatives with heterogeneous quality criteria.&#x0D; Materials and Methods. The article provides a description of the functioning model of a complex technical system under conditions of uncertainty, and also describes the main methods for comparing various options for the system design at the initial stages of design in conditions of incompleteness and uncertainty of information, as well as ambiguity of the preferences of the decision maker.&#x0D; Results. The scientific novelty of the implemented approach lies in the use of interval data statistics, which allow the most correct consideration of possible errors associated with measuring the values of the characteristics of the studied technical systems at all stages of the control process.&#x0D; The scientific novelty of the implemented approach lies in the use of the interval analysis apparatus, which makes it possible to most correctly take into account the possible errors associated with measuring the values of the characteristics of the studied technical systems at all stages of the design process.&#x0D; Discussion and Conclusions. The mathematical model of the process of functioning of a complex technical system under conditions of interval uncertainty developed in the course of the study allows selection of system design options at the initial stages of design, taking into account possible errors and inaccuracies arising from the deviation of values from the calculated nominal values.

The Complexity of the Homeopathic Healing Response Part 2: The Role of the Homeopathic Simillimum as a Complex System in Initiating Recovery from Disease.

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.

The relationship between form and function can have profound effects on evolutionary dynamics and such effects may differ for simple versus complex systems. In particular, functions produced by multiple structural configurations (many-to-one mapping, MTOM) may dampen constituent trade-offs and promote diversification. Unfortunately, we lack information about the genetic architecture of MTOM functional systems. The skulls of teleost fishes contain both simple (lower jaw levers) as well as more complex (jaws modeled as 4-bar linkages) functional systems within the same craniofacial unit. We examined the mapping of form to function and the genetic basis of these systems by identifying quantitative trait loci in hybrids of two Lake Malawi cichlid species. Hybrid individuals exhibited novelty (transgressive segregation) in morphological components and function of the simple and complex jaw systems. Functional novelty was proportional to the prevalence of extreme morphologies in the simple levers; by contrast, recombination of parental morphologies produced transgression in the MTOM 4-bar linkage. We found multiple loci of moderate effect and epistasis controlling jaw phenotypes in both the simple and complex systems, with less phenotypic variance explained by QTL for the 4-bar. Genetic linkage between components of the simple and complex systems partly explains phenotypic correlations and may constrain functional evolution.

The complexity of problems facing society continues to grow, and decision-makers and problem-solvers are finding many of today's emerging problems to be beyond their capability to adequately address. There is agreement in the literature that problems of this nature are complex system problems, inextricably linked to some highly complex system of systems. Establishing a clear understanding of the specific complex system context is fundamental to the process of understanding and analyzing complex systems and complex system problems across all of the different systems-based disciplines. While complex system context is widely referred to in systems literature, there is no clear characterization of exactly what system context is, making this foundational system concept ambiguous. This research addressed this gap in the systems body of knowledge by providing the needed detail and clarity to the concept of complex system context. A rigorous research methodology, employing the grounded theory method, was used to analyze data collected through a series of semi-structured interviews conducted with individuals reflecting a wide range of systems education and practical experience. Two research questions were identified as integral to increasing the understanding of context within complex systems. (1) What are the constituent elements of complex system context, and what attributes and dimensions characterize these elements? (2) What systems-based framework can be developed for constructing and articulating complex system context? Using the grounded theory method, a theory of system context was constructed, adding to the systems body of knowledge and substantiating a comprehensive and unambiguous theoretical construct for system context within complex systems. Then, based on this theory, a conceptual model to articulate and capture system-specific complex system context was developed---the Complex System Contextual Framework (CSCF). The CSCF shows significant promise for contribution to systems practitioners by supporting the future development of tools to help practitioners capture system context as a part of complex system problem formulation. The research also made a contribution in the area of research methodologies by furthering the use of the grounded theory method in the engineering management and systems engineering domain, an area where its application has been very limited.

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.

Aim. For complex highly-integrated technical systems that contain elements that vary in their physical nature and operating principles (combination of mechanical, electrical and programmable electronic components), complex dependability analysis appears to be challenging due to both qualitative and quantitative reasons (large number of elements and performed functions, poorly defined boundaries of interfunctional interaction, presence of hidden redundancy, static and dynamic reconfiguration, etc.). The high degree of integration of various subsystems erodes the boundaries of responsibility in the cause-and-effect link of failures. Thus, the definition of the strength and boundaries of interfunctional and cross-system interaction is of great value in the context of complex system analysis from the standpoint of locating bottlenecks, as well as reliable evaluation of the complex dependability level. Methods. In order to solve the tasks at hand, the authors propose a method that is based on the research of the behavior of the centroid of an area bounded above by the failure density function graph, below by the coordinate axis, from the right and left by the boundaries of the considered operation interval. Graphical analysis with construction of centroids is performed for each subsystem or structural unit of a complex technical system. After that, based on the partial centroids of the respective subsystems/units, the average centroid for the whole complex system is constructed. The authors suggest using the average centroid as a conditional universal measure of the average dependability level of highly-integrated technical systems that can be used in the development of specific design solutions. In this case, in particular, it is suggested to use the presented method for identification of the subsystem that, when redundant, ensures the highest all-around growth of dependability of the complex technical system as a whole. This condition is fulfilled by the subsystem/unit of which the partial centroid is situated at the longest distance from the average centroid. The assumptions presented in this article and the results obtained are tested by means of a short verification consisting in the calculation of the probability of no-failure of the system and subsystems, construction and analysis of respective graphs. Results. The method’s implementation is presented using the example of a conventional mechatronic system. For the sake of briefness and focus the information is given in a simplified and abstract form. The application of the proposed method for analyzing complex technical systems dependability through the research of density function centroid introduced in this article was the target criterion of the method’s development, i.e. identification of bottlenecks and areas with the highest potential for increasing the overall dependability. Further publications will be dedicated to proving the applicability of such entity as a centroid as a dependability evaluation criterion, as well as other applications of the presented method in complex technical systems dependability analysis.

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.