From the Editor

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.

Linguistic Geometry for autonomous navigation

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.

UV laser ablation of polymers is a versatile method to structure polymers with high resolution. The mechanism of ablation is often discussed controversially, but it is necessary to keep in mind that polymers are complex systems with a wide variety of properties that can influence the ablation mechanism. Analyzing the data, it appears that the ablation mechanism is a complex interrelated system, where photochemical and photothermal reactions are very important. The pressure jump, which is associated with the creation of small molecules and originates from both types of reaction, is also important for ablation. The importance of each effect is strongly dependent on the type of polymer, the laser wavelengths, the pulse length, and the substrate. UV laser ablation can also be utilized to deposit directly thin polymer films by PLD, but this is limited to certain polymers. Alternative laser-based techniques (LIFT) utilize the decomposition of a thin layer to transfer complete layers with high spatial resolution. This approach can be used to transfer pixels of sensitive materials to a substrate with a minimal thermal and UV load.

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.

Complexity science offers many theories such as chaos theory and coevolutionary theory. These theories illustrate a large set of real life systems and help decipher their nonlinear and unpredictable behaviours. Categorizing an observed Complex System among these theories depends on the aspect that we intend to study, and it can help better understand the phenomena that occur within the system. This article aims to give an overview on Complex Systems and their modelling. Therefore, we compare these theories based on their main behavioural characteristics, e.g. emergence, adaptability, and dynamism. Then we compare the methods used in the literature to model and simulate Complex Systems, and we propose and discuss simple guidelines to help understand one's Complex System and choose the most adequate model to simulate it.

Multiscale modeling in smart cities: A survey on applications, current trends, and challenges

The application of a systems approach to assessing biological systems provides hope for a coherent understanding of cell dynamics patterns and their relationship to plant life. This action required the reasoning about complex systems. In other sides, there were a lot of researchers who provided the proof about the instructional successions. They involved the multiple external representations which improved the biological learning. The researcher conducted an investigation using one shoot case study design which involved 30 students in proving that the MERs worksheets could affect the student's achievement of reasoning about complex system. The data had been collected based on test of reasoning about complex system and student's identification result who worked through MERs. The result showed that only partially students could achieve reasoning about system complex, but their MERs skill could support their reasoning ability of complex system. This study could bring a new hope to develop the MERs worksheet as a tool to facilitate the reasoning about complex system.

Complex systems are often a derivative of the current trend that dictates increased functionality and performance. Mobile phones that provide internet access and music player, fighter aircraft with fly-by-wire for flight control, and energy grid that also monitors usage and recommends corrective actions are few of the examples of emerging complex systems. There are a large number of causes that could lead to increased complexity of a system, such as (1) increase in number of parts, (2) increase in number of interactions, and (3) integration of multiple technologies. Increase in complexity makes these systems significantly challenging in terms of design and optimization, resulting in large time and resource investment. Therefore, it is typical for complex systems to use non-mature technologies that are expected to mature along the way. However, such infusion introduces significant levels of uncertainty, which historically have led to explosion in development cost. In this paper, we assess the impact of uncertainty on a complex system. Through numerical simulations, we demonstrate that increasing complexity does not necessarily result in vulnerability of a system to uncertainties. These results challenge the conviction held by complex system design community, which assumes complexity and uncertainty have a direct correlation. Our results indicate that selection of system architecture is critical for system robustness. This should provide motivation for further research in the complex system arena.