Predictability and Control in Complex Systems

<p>Control of complex systems is one of important files in complex systems, that not only relies on the essence of complex systems which is denoted by the core concept – emergence, but also embodies the elementary concept in control theory. Aiming at giving a clear and self-contained description of emergence, the paper introduces a formal way to completely describe the formation and dynamics of emergence in complex systems. Consequently, this paper indicates the Emergence-Oriented Control methodology that contains three kinds of basic control schemes: the direct control, the system re-structuring and the system calibration. As a universal ontology, the Emergence-Oriented Control provides a powerful tool for identifying and resolving control problems in specific systems.</p>

Linguistic Geometry for autonomous navigation

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.

A series of transformations and adaptations in the Sami use of land for their subsistence over a long period of time has led to the reindeer husbandry of today. In Sweden the Sami rights to land and water are today legally connected to the practising of reindeer husbandry as a livelihood. Together with a few associated commodities, it has become considered is the only lasting Sami land-use. In the Sami culture, a central element is the association of the people with the land and the subsistence on what is provided in nature. Consequently, this association between people and land is today kept alive by the reindeer husbandry practiced as a livelihood, which thereby also produces and manages an essential base for the culture. The developments in the surrounding society are currently reducing the latitude for the reindeer industry at an accelerated rate and thereby also its capacity to handle new situations. In the complicated ecological, economic, social and institutional contexts, where reindeer husbandry is practiced, there is a large risks for sudden and unpredicted disintegrations and collapses at different system levels. The consequences thereof for the long term continuation and adaptability of Sami land use are largely unpredictable. If it leads to a collapse of reindeer industry as mode of land-use, the risks of additional deterioration of the Sami indigenous rights is also apparent and thereby the scope for new solutions as well. This will likely have serious negative consequences for the viability of the Sami culture concurrently with declining live dependences of the land. The situation of reindeer husbandry has similarities with management crises in many other integrated socio-ecological systems, which have led to sustainability failures and unpredicted consequences. These insights seem to be deficient in the treatment of the problems, which reindeer industry is facing. Scientists could probably make a very important contribution by elucidating these circumstances. This requires the adoption of a new approach based on complex systems thinking, where possible changes associated with internal and external conditions can be analysed across system borders, scales and disciplines. A possible conceptual framework for such analyses would be the theories evolved around adaptive transformations in integrated human and natural systems, now becoming a vital part of sustainability science. Fundamental in this framework is the integration of physical, ecological, economic, social and institutional systems via adaptive cycles. These are characterised by four consecutive key processes, which are repeated irregularly over time, namely growth and development, maturation with increasing vulnerability, gradual or sudden disintegration and collapse, and release of resources and values for controlled or uncontrolled reorganisation, growth and development. The connections span over temporal and spatial or social scales, the rate and magnitude of changes in different variables depend to some extent of the scale. Typical for such complex systems are a high degree of dynamics with simultaneous changes in different parts of the system, uncertainty and unpredictability, varying vulnerability over time, multiple stability domains, and inbuilt non-linearities which may cause the system to flip into another trajectories or stability domains when challenges exceed the ability of the system to absorb disturbances without loosing its functions (i.e. resilience). The latter typically happens after decreased resilience which may have many different causes. This may be due to different slow and maybe ignored losses of key resources or abilities to utilise them, slow accumulations of adverse circumstances such as external disturbances, losses of functional diversities, decreased capability to adopt novelties, loss of social capabilities, trust or ability of learning, rigid institutions, etc. It is generally suggested that the management of complex systems should promote the building and maintenance of resilience, creative self-organisations, learning and diversity, rather than strive for decreased variation and stability in the conventional sense. The complex system view is much closer the actual “soul” of reindeer husbandry, which by necessity is characterised by maintaining flexibility, living with uncertainty and continuously adapting to prevailing conditions. This stands in sharp contrast to the “control and command” type of management, which is usually investigated and imposed on the reindeer industry.

Regional complex system simulation optimization through linking governance and environment performance: A case study of water environmental carrying capacity based on the SDES model

Change in a complex system—for example, to its technology, procedures, or information flows— no matter how small, has the potential to create large effects and ripples of disruption. A complex system’s dynamics cannot be fully known, and the effects and disruptions produced by change are difficult to predict. Nonetheless, complex systems can be at least partly understood in terms of patterns in their dynamics, generalizable principles, and mechanisms of control, balance, and adaptation. This panel will focus on complex systems research and what they suggest about how to introduce change into a complex system such that the work system resilience and health are disrupted minimally. Case studies may be discussed, as well; examples of changes to established complex work systems. These changes include introducing remotely piloted aircraft systems (RPAS) into the National Airspace System (NAS), additional automation into air traffic control, and new technology into military air combat training.

Contracts as Systems

The 17 Sustainable Development Goals (SDGs) are a complex system comprising 169 targets and about 230 indicators. Urban Health could be considered a complex system since it deals with 15 out of 17 SDGs, excluding the two related to life below water and life on land. According to the World Health Organization, to achieve the SDGs, countries have committed to organize Urban Health initiatives to improve the social, economic, and physical environments promoting health and sustainability globally. Cities will become more inclusive, safer and more sustainable, which are important driving forces to implement the development equation. To set up a framework to point out that Urban Health is a complex system oriented to the achievement of the SDGs. It has been conducted an extensive literature review on databases (PubMed, Embase, Web of Science and Scopus) using keywords in 3 strings combined with Boolean operators: SDGs and Urban Health, Urban Health as Complex System and Urban Health, Environmental Health and Public Health as Complex Systems. In addition, a grey literature review has been carried out. Out of 1005 publications, 21 were eventually included: 14 publications relate to the interdependent relationships between SDGs and Urban Health, with regard to the association between Urban Health and complex system, 3 publication studied the effects and implications of such correlation, 4 focused on Environmental Health and Public Health in relation with the complex system. The selected publications suggested methodologies aimed at setting up an urban health framework to achieve SDGs. There is an initial orientation focused on the study of Urban Health problems aimed at achieving SDGs from the perspective of complex systems. We highlight the need to conduct further studies on a more detailed framework in order to address this type of approach.

Public health evaluation methods have been criticized for being overly reductionist and failing to generate suitable evidence for public health decision-making. A "complex systems approach" has been advocated to account for real world complexity. Qualitative methods may be well suited to understanding change in complex social environments, but guidance on applying a complex systems approach to inform qualitative research remains limited and underdeveloped. This systematic review aims to analyze published examples of process evaluations that utilize qualitative methods that involve a complex systems perspective and proposes a framework for qualitative complex system process evaluations. We conducted a systematic search to identify complex system process evaluations that involve qualitative methods by searching electronic databases from January 1, 2014-September 30, 2019 (Scopus, MEDLINE, Web of Science), citation searching, and expert consultations. Process evaluations were included if they self-identified as taking a systems- or complexity-oriented approach, integrated qualitative methods, reported empirical findings, and evaluated public health interventions. Two reviewers independently assessed each study to identify concepts associated with the systems thinking and complexity science traditions. Twenty-one unique studies were identified evaluating a wide range of public health interventions in, for example, urban planning, sexual health, violence prevention, substance use, and community transformation. Evaluations were conducted in settings such as schools, workplaces, and neighborhoods in 13 different countries (9 high-income and 4 middle-income). All reported some utilization of complex systems concepts in the analysis of qualitative data. In 14 evaluations, the consideration of complex systems influenced intervention design, evaluation planning, or fieldwork. The identified studies used systems concepts to depict and describe a system at one point in time. Only 4 evaluations explicitly utilized a range of complexity concepts to assess changes within the system resulting from, or co-occurring with, intervention implementation over time. Limitations to our approach are including only English-language papers, reliance on study authors reporting their utilization of complex systems concepts, and subjective judgment from the reviewers relating to which concepts featured in each study. This study found no consensus on what bringing a complex systems perspective to public health process evaluations with qualitative methods looks like in practice and that many studies of this nature describe static systems at a single time point. We suggest future studies use a 2-phase framework for qualitative process evaluations that seek to assess changes over time from a complex systems perspective. The first phase involves producing a description of the system and identifying hypotheses about how the system may change in response to the intervention. The second phase involves following the pathway of emergent findings in an adaptive evaluation approach.

As one type of complex system applied in the field of engineering and technology, electromechanical complex system (MCS) is discussed for its properties at an new angle of view in this paper. The development of the intelligent control for electromechanical complex system is analyzed and summarized, and a scheme of intelligent control for above-knee prosthesis is designed based on CMAC neural network model to demonstrate the electromechanical complex system and its control. The conclusion of analysis in the paper shows that electromechanical complex system can be controlled well with intelligent control technology which is a promising way to control electromechanical complex system or other complex systems.

The evaluation of the emergent behaviour in complex systems requires an analytical framework which allows the observation of different phenomena that take place at different levels. In order to observe the dynamics of complex systems, it is necessary to perform simulations so that both local and the emergent behaviour can be observed. To this end, the way in which complex system simulators are built must be examined so that it will be feasible to model large scale scenarios. In this paper, the use of Model Driven Engineering methodology is proposed to deal with this issue. Among other benefits, it is shown that this methodology allows the representation and simulation of a complex system providing support for the analysis. This analysis is supported by a metamodel which describes the system components that are under study. The application of this methodology to the development of large scale simulators is explored through a case study. This case study analyses a complex socio-technical system: a power grid.

This introductory chapter provides an overview and a brief history of complexity science, which is the study of complex systems. All living systems and all intelligent systems are complex systems. Complexity science is relatively new but already indispensable. Many of the most important problems in engineering, medicine, and public policy are now addressed with the ideas and methods of complexity science. However, there is no agreement about the definition of 'complexity' or 'complex system', nor even about whether a definition is possible or needed. The conceptual foundations of complexity science are disputed, and there are many and diverging views among scientists about what complexity and complex systems are. Even the status of complexity as a discipline can be questioned given that it potentially covers almost everything. The origins of complexity science lie in cybernetics and systems theory, both of which began in the 1950s. Complexity science is related to dynamical systems theory, which matured in the 1970s, and to the study of cellular automata, which were invented at the end of the 1940s. By then computer science had become established as a new scientific discipline.

The coordinated development of a complex system refers to the harmonious and coherent evolution of its subsystems. From the perspective of the coordinated development of ecological–economic–social complex systems, this paper analyzes the coordinated development mechanism (CDM) of the mariculture ecological–economic–social (MEES) complex system, constructs a coordinated development evaluation indicator system for the MEES complex system, and adopts the comprehensive evaluation model and the coupling coordination degree (CCD) model to empirically analyze the coordinated development level of the MEES complex system in China from 2009 to 2020. The results show that the comprehensive development level of China’s MEES complex system has improved significantly during this period, with the comprehensive development index increasing from 0.25 in 2009 to 0.76 in 2020, transitioning from a poor to an excellent status. Simultaneously, the CCD of the system increased progressively, experiencing phases of near dissonance, barely coupling coordination, primary coordination, and intermediate coordination, before finally reaching a stage of good coordination. Based on these findings, we further discuss and propose countermeasures to promote the coordinated development of China’s MEES complex system.

This paper reports new results on engineering applications of Linguistic Geometry. This formal theory is intended to discover the inner properties of human expert heuristics, which were successful in a certain class of complex control systems, and apply them to different systems. The Linguistic Geometry relies on the formalization of search heuristics, which allow to decompose complex system into the hierarchy of subsystems, and thus solve intractable problems reducing the search. Currently we investigate heuristics extracted in the form of hierarchical networks of paths. The dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. This paper includes a brief survey of the Linguistic Geometry, and an example of a solution of optimization problem for space robotic vehicles. This example includes actual generation of the hierarchy of languages and demonstrates the drastic reduction of search in comparison with conventional search algorithms. It is well known that despite of the universal proliferation of computers there are many realworld problems where human expert skills in reasoning about complex systems are incomparably higher than the level of modern computing systems. At the same time there are even more areas where advances are required but human problem-solving skills can not be directly applied. For example, there are problems of planning and automatic control of autonomous agents such as space vehicles, stations and robots with cooperative and opposing interests functioning in a complex, hazardous environment. Reasoning about such complex systems should be done automatically, in a timely manner, and often in a real time. Moreover, there are no highly-skilled human experts in these fields ready to substitute for robots (on a virtual model) or transfer their knowledge to them. There is no grand-master in robot control, although, of course, the knowledge of existing experts in this field should not be neglected it is even more valuable. It is very important to study human expert reasoning about similar complex systems in the areas where the results are successful, in order to discover the keys to success, and then apply and adopt these keys to the new, as yet, unsolved problems. The question then is what language tools do we have for the adequate representation of human expert skills? An application of such language to the area of successful results achieved by the human expert should yield a formal, domain independent knowledge ready to be transferred to different areas. Neither natural nor programming languages satisfy our goal. The first are informal and ambiguous, while the second are usually detailed, lower-level tools. Actually, we have to learn how we can formally represent, generate, and investigate a mathematical model based on the abstract images extracted from the expert vision of the problem. There have been many attempts to find the optimal (suboptimal) operation for real-world complex systems. One of the basic ideas is to decrease the dimension of the real-world system following the approach of a human expert in a certain field, by breaking the system into smaller subsystems. These ideas have been implemented for many problems with varying Transactions on Information and Communications Technologies vol 6, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517

The railway system is a complex system because of many constraints, randomness and high security requirements, so it is difficult to establish an accurate mathematical model for it, which brings great challenges to railway scheduling. Parallel intelligence theory can effectively solve the modeling, analysis and control of complex systems, so it has a wide range of applications in the field of intelligent transportation. In this paper, we build an artificial system according to the actual system, and use the surrogate modelling to carry out the calculation experiments, and proposed a parallel scheduling system based on the parallel intelligence method. Select a case to design scenario and perform computational experiment, the scheduling strategy is given and the scheduling strategy is analyzed.