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The purpose of this paper is to build a bridge between climate change adaptation and spatial planning and design. It aims to develop a spatial planning framework in which the properties of climate adaptation and spatial planning are unified.

Adaptive and dynamical approaches in spatial planning literature are studied and climate adaptation properties are defined in a way they can be used in a spatial planning framework. The climate adaptation properties and spatial planning features are aggregated in coherent groups and used to construct the spatial planning framework, which subsequently has been tested to design a climate adaptive region.

The paper concludes that the majority of spatial planning methods do not include adaptive or dynamic strategies derived from complex adaptive systems theory, such as adaptive capacity or vulnerability. If these complex adaptive systems properties are spatially defined and aggregated in a coherent set of spatial groups, they can form a spatial planning framework for climate adaptation. Each of these groups has a specific time dimension and can be linked to a specific spatial planning “layer”. The set of (five) layers form the spatial planning framework, which can be used as a methodology to design a climate adaptive region.

Previous research did not connect the complex issue of climate change with spatial planning. Many frameworks are developed in climate change research but are generally not aiming to meet the needs of spatial planning. This article forms the first attempt to develop a spatial planning framework, in which non‐linear and dynamical processes, such as climate adaptation, is included.

The purpose of this paper is to focus on the evaluation model of the enterprises' technological innovation system, based on the theory of complex adaptive system.

Combined with the status quo and recent studies of Chinese enterprises' technological innovation, the paper discusses the complex‐system features of the technological innovation. The stimulus‐response model is used to establish the two‐level framework for enterprises' technological innovation system. By means of the adaptive fitness function, the economic and social utility of enterprises' technological innovation is measured from two dimensions. Finally, the fuzzy catastrophe model is introduced to evaluate the enterprises' technological innovation.

The enterprises' technological innovation system has attributions of the subject aggregation, the systematic openness, nonlinearity and diversity. Thus, the macro‐micro based technological innovation system from the perspective of complex adaptive system is proposed. The system utility is considered based on the system subjects and system structure, and the calculation framework of the adaptive fitness for the whole system is obtained by considering the emergent property describing the system scale effect and structure effect. In fact, the fuzzy theory can well reflect the influential situation that the interactions between different factors may cause the mutation of the higher level and the interactions between enterprises can lead to the shifts of the system.

The paper proposes the complex adaptive system for the enterprises' technological innovation based on the special macro environment in China. A new framework for the research of technological innovation is provided by analyzing the system inner model. Fuzzy catastrophe model can reduce the evaluation irrationality due to the subjective index weights.

This paper aims to examine complex clinical decision‐making processes in trauma center units of hospitals in terms of the immediate impact of complexity on the medical team involved in the trauma event.

It is proposed to develop a model of decision‐making processes in trauma events that uses a Bayesian classifier model with convolution and deconvolution operators to study real‐time observed trauma data for the decision‐making process under tremendous stress. The objective is to explore and explain physicians' decision‐making processes under stress and time constraints during actual trauma events from the perspective of complexity.

Because physicians have blurred information and cues that are tainted by random environmental noise during injury‐related events, they must de‐blur (de‐convolute) the collected data to find a best approximation of the real data for decision‐making processes.

The data collection and analysis is innovative and the permission to access raw audio and video data from an active trauma center will differentiate this study from similar studies that rely on simulations, self report and case study approaches.

Clinical decision makers in trauma centers are placed in situations that are increasingly complex, making decision‐making and problem‐solving processes multifaceted.

The science of complex adaptive systems, together with human judgment theories, provide important concepts and tools for responding to the challenges of healthcare this century and beyond.

This chapter introduces readers to a complex adaptive systems approach for integrating research on genes, behavior, and social structures/institutions. Until recently, scientists have resorted to reductionism as a decoding and epistemological strategy for understanding human health. The complex bonds among health’s biological, behavioral, and social dimensions, however, cannot be fully grasped with reductionist schemas. Moreover, because reducing and simplifying can lead to incomplete understanding of phenomena, the resulting deficient knowledge has the potential to be harmful.

To achieve its purpose, this primer will: (1) introduce fundamental notions from complexity science, useful for inquiry and practice integrating research on genes, behavior, and social structures; (2) outline selected methodological strategies employed in studying complex adaptive/dynamic systems; (3) address the question, “Specifically, how can a dynamic systems approach be helpful for integrating research on genes, behavior, and social structures/institutions, to improve the public’s health?”; and (4) provide examples of studies currently deploying a complexity perspective.

The originality/value of this primer rests in its critique of the research status quo and the proposition of an alternative lens for integrating genomic, biomedical, and sociological research to improve the public’s health. The topic of complex adaptive/dynamic systems has begun to flourish within sociology, medicine, and public health, but many researchers lack exposure to the topic’s basic notions and applications.

Complex systems adapt to survive, but little comparative literature exists on various approaches. Adaptive complex systems are generic, this referring to propositions concerning their bounded instability, adaptability and viability. Two classes of adaptive complex system theories exist: hard and soft. Hard complexity theories include Complex Adaptive Systems (CAS) and Viability Theory, and softer theories, which we refer to as Viable Systems Theories (VSTs), that includes Management Cybernetics at one extreme and Humanism at the other. This paper has a dual purpose distributed across two parts. In part 1 the purpose was to identify the conditions for the complementarity of the two classes of theory. In part 2 the two the purpose is to explore (in part using Agency Theory) the two classes of theory and their proposed complexity continuum.

Explanation is provided for the anticipation of behaviour cross-disciplinary fields of theory dealing with adaptive complex systems. A comparative exploration of the theories is undertaken to elicit concepts relevant to a complexity continuum. These explain how agency behaviour can be anticipated under uncertainty. Also included is a philosophical exploration of the complexity continuum, expressing it in terms of a graduated set of philosophical positions that are differentiated in terms of objects and subjects. These are then related to hard and softer theories in the continuum. Agency theory is then introduced as a framework able to comparatively connect the theories on this continuum, from theories of complexity to viable system theories, and how harmony theories can develop.

Anticipation is explained in terms of an agency’s meso-space occupied by a regulatory framework, and it is shown that hard and softer theory are equivalent in this. From a philosophical perspective, the hard-soft continuum is definable in terms of objectivity and subjectivity, but there are equivalences to the external and internal worlds of an agency. A fifth philosophical position of critical realism is shown to be representative of harmony theory in which internal and external worlds can be related. Agency theory is also shown to be able to operate as a harmony paradigm, as it can explore external behaviour of an agent using a hard theory perspective together with an agent’s internal cultural and cognitive-affect causes.

There are very few comparative explorations of the relationship between hard and soft approaches in the field of complexity and even fewer that draw in the notion of harmony. There is also little pragmatic illustration of a harmony paradigm in action within the context of complexity.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

This paper aims to question the utility of addressing food insecurity through food assistance programmes and by separating food security into pillars, and it argues for a systemic innovation and complexity approach. This is achieved by demonstrating that food insecurity is a wicked problem and therefore needs to be addressed holistically.

To establish that food insecurity is a wicked problem, characteristics of food insecurity are aligned to characteristics of wicked problems. The need to address wicked problems holistically through a systemic innovation approach and an understanding of complexity theory is discussed by referring to the literature. How to take such an approach for addressing food insecurity is illustrated by describing the use of an online tool that takes a systemic innovation and complexity approach.

Given food insecurity is a wicked problem and needs to be addressed holistically, the focus when addressing food insecurity should not be on programmes or pillars. Instead, it needs to be on increasing the coherence and building the adaptive capacity of food insecurity solution ecosystems.

This paper provides insights into the nature of food insecurity and how to address food insecurity.

For the first time, this paper aligns characteristics of food insecurity to characteristics of wicked problems and demonstrates how an online tool for systemic innovation can assist food insecurity solution ecosystems to address food insecurity.

The purpose of this paper is to advance supply chain network theory by applying theoretical and empirical developments in complex network literature to the context of supply chains as complex adaptive systems. The authors synthesize these advancements to gain an understanding of the network properties underlying efficient supply chains. To develop a suitable theory of supply chain networks, the authors look to mirror the properties of complex network models with real‐world supply chains.

The authors review complex network literature drawn from multiple disciplines in top scientific journals. From this interdisciplinary review a series of propositions are developed around supply chain complexity and adaptive phenomena.

This paper proposes that the structure of efficient supply chains follows a “scale‐free” network. This proposal emerges from arguments that the key properties of efficient supply chains are a short characteristic path length, a high clustering coefficient and a power law connectivity distribution.

The authors' discussion centres on applying advances found in recent complex network literature. Hence, the need is noted to empirically validate the series of propositions developed in this paper in a supply chain context.

If efficient supply chains resemble a scale‐free network, then managers can derive a number of implications. For example, supply chain resilience is derived by the presence of hub firms. To reduce the vulnerability of supply chains to cascading failures, it is recognized that managers could build in redundancy, undertake a multi‐sourcing strategy or intermediation between hub firms.

This paper advances supply chain network theory. It offers a novel understanding of supply chains as complex adaptive systems and, in particular, that efficient and resilient supply chain systems resemble a scale‐free network. In addition, it provides a series of propositions that allow modelling and empirical research to proceed.

The purpose of this research is to explore core contributions from two different approaches to complexity management in organisations aiming to improve their sustainability,: the Viable Systems Model (VSM), and the Complex Adaptive Systems (CAS). It is proposed to perform this by summarising the main insights each approach offers to understanding organisational transformations aiming to improve sustainability; and by presenting examples of applied research on each case and reflecting on the learning emerging from them.

An action science approach was followed: the conceptual framework used in each case was first presented, which then illustrates its application through a case study; at the first one the VSM framework supports an organisational transformation towards sustainability in a community; the second one is a quantitative case study of intended greening of two firms in the supermarket industry, taken from a CAS perspective. The learning from each case study on how they support/explain organisational learning in transformations towards more sustainable organisations was illustrated.

It wase found that the VSM and the CAS approaches offer internally consistent and complementary insights to address issues of self‐organisation and adaptive management for sustainability improvement: while CAS explains empowerment of bottom‐up learning processes in organisations, VSM enables a learning context where self‐organised networks can co‐evolve for improved sustainability.

The main aspects of both theories and examples of their explanatory power to support learning in practical applications in organisations were introduced. The initial findings indicate that it will be worth studying in greater depth the contributions to organisational learning from both conceptual models and more widely comparing their applications and insights.

The paper offers some guidance to both researchers and practitioners interested in using complex systems theories in action research‐oriented projects, regarding the usability and applicability of both approaches.

It is considered that, by better understanding organisational ability to adapt and self‐regulate on crucial issues for sustainability, it may help to develop one path through the ongoing socio‐ecological crisis. While much has been written about sustainability initiatives and governance from conventional perspectives, much less is known about how a complex systems framework may help to address one's pressing sustainability needs. These issues from two innovative complexity approaches as well as the value of using them in action research were illustrated.

Quality management constructs related to management leadership and workforce involvement have consistently shown strong correlation with firm success for years. However, there is an increasing body of research based on complexity theory (CT) suggesting that constructs such as these should be viewed as variables in a complex system with inter-dependencies, interactions, and potentially nonlinear relationships. Despite the significant body of conceptual research related to CT, there is a lack of methodological research into these potentially nonlinear effects. The purpose of this paper is to demonstrate the theoretical and practical importance of non-linear terms in a multivariate polynomial model as they become more significant predictors of firm success in collaborative environments and less significant in more rigidly controlled work environments.

Multivariate polynomial regression methods are used to examine the significance and effect sizes of interaction and quadratic terms in operations scenarios expected to have varying degrees of complex and complex adaptive behaviors.

The results find that in highly collaborative work environments, non-linear and interaction effects become more significant predictors of success than the linear terms in the model. In more rigid, less collaborative work environments, these effects are not present or significantly reduced in effect size.

This study shows that analytical methods sensitive to detecting and measuring nonlinearities in relationships such as multivariate polynomial regression models enhance our theoretical understanding of the relationships between constructs when the theory predicts that complex and complex adaptive behaviors are present and important.

This study demonstrates that complex adaptive behaviors between management and the workforce exist in certain environments and provide greater understanding of factor relationships relating to firm success than more traditional linear analytical methods.