Introduction

This chapter assesses whether there is a single natural phenomenon of complexity found in a wide variety of living and nonliving systems and which can be the subject of a single scientific theory. Is there such a thing as 'complexity science' rather than merely branches of different sciences, each of which have to deal with their own examples of complex systems? The chapter synthesises an account of how to think about complexity and complex systems from the examples and analysis of the previous chapters. Roughly speaking, there is no single phenomenon of complexity, but there are a variety of features of complex systems that manifest themselves in different ways in different contexts. Hence, complexity science is not a single scientific theory but a collection of models and theories that can be used to study the different features in common ways across different kinds of systems. The chapter then considers different views about complex systems.

Complex adaptive systems (CAS) have been identified as being hard to comprehend, composed of multiple interacting components acting interdependently with overlapping functions aimed at adapting to external/environmental forces. The current theoretical model utilized the natural functions of teams, viewing teams as a complex adaptive system, to develop the structure of the theory of complex adaptive team systems (CATS). The CATS model was formulated around the components of complexity theory (interactions, nonlinearity, interdependency, heterogeneity, complex systems, emergence, self-organizing, and adaptability) to show its utility across multiple domains (the role of leadership, organizational learning, organizational change, collective cognitive structures, innovation, cross-business-unit collaborations). In theorizing the CATS model, a new level of analysis was implemented, the interactions between agents as a move toward emergence in complex systems. The CATS model ultimately provides a model for organizations/institutions to drive knowledge creation and innovation while operating in today's complexity.

School‐improvement initiatives are implemented with admirable goals, often requiring substantial human and material resources. However, many fail to be sustainable beyond short‐term funding cycles or the enthusiasm of local initiators. Typically, implementation and improvement are viewed linearly and as static end‐products that fail to consider that schools are complex adaptive systems (CAS) that are continuously and recursively challenged with change. To investigate sustainable school‐improvement within CAS, we used PRISMA‐SR guidelines to conduct a scoping review of peer‐reviewed journal articles extracted from six databases using keywords synonymous with ‘ complex or adaptive system ’ , ‘ sustain ’ , ‘ school ’ and ‘ initiative ’ . The search process yielded 1146 articles but only 16 of those articles met our inclusion/exclusion criteria that specified that authors present an integrated view of sustainable implementation in schools conceptualised as CAS. Within the selected 16 articles, key components for sustainability in schools described as CAS included access to adequate funding and resources, engagement and involvement of stakeholders, the ability of stakeholders to adapt to change, formation of collaborative partnerships, and the development of supportive policies and procedures. Some important issues arose, such as the tendency of the ‘whole school approach’ to be reduced to information dissemination activities that do not adequately deal with components of complex systems, and the increasing recognition that older implementation models that seek ‘institutionalisation’ fail to appreciate that sustainability is a process, not an end goal. This scoping review highlights the need to recognise the complexity and adaptivity of school systems for successful and sustained implementation of school‐improvement initiatives.

This book provides an introduction to the role of diversity in complex adaptive systems. A complex system--such as an economy or a tropical ecosystem--consists of interacting adaptive entities that produce dynamic patterns and structures. Diversity plays a different role in a complex system than it does in an equilibrium system, where it often merely produces variation around the mean for performance measures. In complex adaptive systems, diversity makes fundamental contributions to system performance. Scott Page gives a concise primer on how diversity happens, how it is maintained, and how it affects complex systems. He explains how diversity underpins system level robustness, allowing for multiple responses to external shocks and internal adaptations; how it provides the seeds for large events by creating outliers that fuel tipping points; and how it drives novelty and innovation. Page looks at the different kinds of diversity--variations within and across types, and distinct community compositions and interaction structures--and covers the evolution of diversity within complex systems and the factors that determine the amount of maintained diversity within a system. Provides a concise and accessible introduction Shows how diversity underpins robustness and fuels tipping points Covers all types of diversity The essential primer on diversity in complex adaptive systems

This paper will explore the concept of resilience from its roots in ecology to the application of this ecological concept of resilience to social and community resilience in the context of climate change. In this context, resilience is seen as a property of complex adaptive communities rather than of individuals. This paper will explore how this ecological concept of resilience has been taken up both by climate adaptation research and by the Transition Town movement. This ecological concept of resilience is at odds with the individualism of both psychological and economic approaches to resilience in relation to climate change.

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

Complexity of social system cybernetics: risk and uncertainty management in negotiations

Complex systems such as cities, energy grids, or the global climate have many plausible futures. Scenarios, or structured narratives of decision-relevant futures, are a common decision support tool for making the complexity and uncertainties of complex systems humanly interpretable. However, the effectiveness of scenario-based decision support depends in part on the usefulness of the selected scenarios. Here we show an optimization-based approach for generating scenarios that are specifically designed to be diverse, plausible, and comprehensive. We establish the advantages of our method by evaluating it against three previously proposed methods: scenario matrices, generic archetypes, and clustering. Our case study is Schelling’s segregation model, a tractable yet behaviorally rich simulation of a complex system. Our results show the proposed optimization-based approach can generate more diverse, plausible, and comprehensive scenarios than existing approaches. The resulting scenarios may provide a more insightful and robust basis for policy decisions, especially for complex systems with emergent behavior or where substantial uncertainties are present.

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

Fractals and related mathematical models of chaotic phenomena have become active areas in research and applications in many diverse fields. These include weather forecasting, structural analysis, mapping, entertainment, biology, and many others. These fields appear to be a significant part of an emerging interdisciplinary science of complexity and complex systems. The uses of these models in the rapidly developing field of visual computing (i.e., the computational aspects of image sciences: image processing, analysis, and synthesis) are leading to many innovations. This paper will describe a range of activities at TASC and elsewhere illustrating significant new developments, and it describes a trend toward unification in visual computing using these techniques.

This study explored “how strategic improvisation (SI) enables agile and creative responsiveness in dynamic organizations recognized as complex adaptive systems.” We highlight “what” constitutes SI, “why” it is strategic and “how” it allows for agile and creative responsiveness. We advance the conceptualization of SI proposing it as “ an improvised deliberate practice that combines coupling unconventional ideas with emergency actions seeking new strategic patterns to indeterminacy. ” This is the case of firefighters, with unique features, such as operating continuously in experimental conditions, exposure to failure, risk, and unpredictability, despite strict protocols to guide operations. Furthermore, agile and creative responsiveness in a complex system is the rule rather than the exception and, therefore, requires new adaptive managerial practices. Our findings provide the SI framework based on four main dimensions such as (i) resolvability; (ii) experimentation; (iii) interactivity; and (iv) sensemaking, composing a SI mechanism. Practical implications and future perspective were proposed.

Contracts as Systems

Arecent Institute of Medicine (IOM) report describes a chasm in health care quality that we must cross for patients to receive better care in the 21st century. The report calls for a “systems approach,” drawing on the rapid evolution of knowledge about complex adaptive systems.1 Understanding how complex adaptive systems work can give physicians insights to develop and modify health care systems. By describing a practical application of the ideas about complex adaptive systems to newborn care, we aim to help pediatricians prepare to lead in this field. A complex adaptive system is a collection of individual agents who have the freedom to act, but because the agents are interconnected, action by any agent changes the context for other agents in the system. One familiar example is the buyers in a stock market. In the last century, it was usual to see organizations as mechanical systems: in mechanical systems, if we know what each part of a system does, we can predict perfectly how the whole will respond in a given situation. This is obviously not true of the stock market. A complex adaptive system may display sudden unpredictable shifts in behavior caused by interactions among agents. An essential first step in improving the US health care system is to recognize that its member organizations and individuals, with sublevels nested within and interconnected to each other, make up a complex adaptive system. One of the key attributes of a complex adaptive system is that orderly behavior can emerge among many agents who are acting independently but who share a common drive. For instance, ants, driven to survive, create intricate buildings and foraging systems without any planning by a chief executive ant. So do humans. The citizens of New York City share a drive to eat; with no single individual …

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.