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This paper sets out to report on research by the authors into the development and application of four extensions to Ashby's Law of Requisite Variety (LoRV) that increase its utility in the arena of unplanned changes in hegemonic control of designed complex socio‐technical systems/digital eco‐systems in the built environment that are structurally dynamic or emergent.

Research on which the paper is based focused on exploration of classical systems approaches to the design of complex socio‐technical systems in which ownership, power, control and management of structure and benefit generation and distribution are distributed, dynamic and multi‐constituent. Support for development of these four extensions to Ashby's Law comes from observation of four decades of socio‐technical systems development along with critical thinking that combined systems analysis theories with theories and findings from fields of hegemonic analysis, design research, management, management information systems, behaviour in organisations and sociology.

The paper outlines application of four new extensions to LoRV in relation to unplanned changes in distributions of power, ownership, control, benefit generation and benefit distribution in complex socio‐technical systems/digital eco‐systems in the built environment that are emergent or have changing structures. Three of these extensions have been outlined earlier in relation to the design of learning object‐based e‐learning systems. The fourth extension builds on these via application of Coasian analysis. The paper also describes a suite of five guidelines to assist with the design of complex socio‐technical systems derived from the four extensions to Ashby.

The four extensions of Ashby's Law that underpin the design guidelines in this paper are deduced from observation and critical analysis rather than being “proven” empirically. They are derived from observation of the behaviour of real‐world complex systems together with critical analytical thinking that integrated theory and research findings from a range of disciplines where each informs understanding of hegemonic aspects of emergent complex socio‐technical systems involving multiple, changing constituencies, and evolving system structures.

A design method is derived comprising five design guidelines for use in pre‐design and design of complex socio‐technical systems/digital eco‐systems in the built environment.

The paper describes the application of four new extensions to LoRV that extend the analytical role of Ashby's Law in diagnosis of changes in power relations and unintended design outcomes from changes in the generation and control of variety in complex, multi‐layered and hierarchical socio‐technical systems that have multiple stakeholders and constituencies. From these, a suite of five new design guidelines is proposed.

As the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).

The paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.

The digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.

This research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.

In order to deepen the understanding of self-organization, the purpose of the paper is to raise and analyze the state of the art in the area of innovation networks, particularly the characteristics of self-organizing, relying on the theory of complex systems to overcome any shortcomings.

The databases selected for the search were Web of Science and Scopus; the keywords searched in the titles of articles were innovation networks, complex systems, self-organization and self-organizing; the timeline of the search covers the period between 2000 and 2014 due to the presence of important studies in the field of networks starting in the early 2000s; only studies published in English were used; the articles selected were examined by first reading the titles, then the abstracts, and finally the texts in full.

The way the main constructs from the analytical perspective of innovation networks intersect with complex systems explains how self-organization is presented and how it can be allowed to occur within a view of expected benefits for the purposes of these networks.

The originality of the research lies in the questioning of the classical form of organizational management in innovation networks, essentially based on the concentration of hierarchical power.

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.

Smallholder value chains are dynamic, changing over time in sudden, unpredictable ways as they adapt to shocks. Understanding these dynamics and adaptation is essential for these chains to remain competitive in turbulent markets. Many guides to value chain development, though they focus welcome attention on snapshots of current structure and performance, pay limited attention to the dynamic forces affecting these chains or to adaptation. The paper aims to discuss these issues.

This paper develops an expanded conceptual framework to understand value chain performance based on the theory of complex adaptive systems. The framework combines seven common properties of complex systems: time, uncertainty, sensitivity to initial conditions, endogenous shocks, sudden change, interacting agents and adaptation.

The authors outline how the framework can be used to ask new research questions and analyze case studies in order to improve our understanding of the development of smallholder value chains and their capacity for adaptation.

The framework highlights the need for greater attention to value chain dynamics.

The framework offers a new perspective on the dynamics of smallholder value chains.

The purpose of this paper is to highlight how systems thinking contributes to decision making in uncertain contexts that are characteristic of service systems. Based on the assumption that service systems face complex conditions, the paper posits that systems thinking may support the understanding of key issues in service management.

This paper proposes an interpretation of complexity in the context of service systems, which highlights the perspective change that occurs when a systems approach is adopted. The offered conceptual perspective is then brought to an operational level, in spite of the complexity of the decisions driving a viable system, by modelling a service system as a network of agents, resources, processes and decisions through the use of fuzzy logic. The paper reviews service management research streams, and takes a deeper look at the concepts of service systems and complex service systems. The paper then proceeds to discuss how systems thinking contributes to service management by proposing a systems interpretation of complexity.

Service management theories and models may be enhanced by integrating prevailing approaches, based on a quantitative and mechanistic view of service systems dynamics, with systems thinking‐based meta‐models that can be used in better understanding service exchanges. The findings of the paper also show how the integration of an engineering approach can be insightful to the understanding of service systems; adopting a Viable Systems Approach (VSA) as a meta‐model can be useful in fully comprehending market behaviour in uncertain conditions.

The originality of this paper lies in exploring the contribution of systems thinking, in particular of the Viable Systems Approach (VSA), to service management and decision making.

Organizations desire more accurate cost systems as competition increases, and consequently increase cost system complexity, as cost systems with greater complexity are potentially more accurate than simpler systems. However, even complex systems are prone to impactful inaccuracies, for example, due to design or calculation issues, that can adversely affect decision-making and firm performance. The authors investigate whether and the extent to which cost system complexity and competition decrease managers’ attribution of cost-system-driven adverse firm effects to the cost system. The authors find greater cost system complexity (by inspiring greater confidence in the cost system) and higher competition (by providing a plausible external cause) decrease managers’ attribution of cost-system-driven adverse firm effects to the cost system. With both greater cost system complexity and higher competition, managers observing signals of material cost inaccuracies are potentially the least likely to attribute cost-system-driven adverse firm effects to the cost system.

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.

Industries lament the current situation of approaches that have resulted in huge losses in the face of complex risks. The purpose of this study is therefore to review complexity theory in the context of risk management so that it is possible to research better approaches for managing complex risks.

The approach is to review complexity theory and highlight those aspects of complexity theory that have relevance for risk management. Then, the paper ends with a discussion on what direction of research that will be most promising for the aforementioned purpose.

The paper finds that the most challenging aspect is to identify the weak signals, and this implies that the current approaches of estimating probabilities are not going to produce the desired results. Big data may hold a solution in the future, but with legislation such as the General Data Protection Regulation, this seems impossible to implement on ethical grounds. Hence, the most prudent approach is to use a margin of safety as advocated by Graham roughly 70 years ago. Indeed, the approach may be to assume that a disaster will take place and use risk management tools to estimate the impact for a given object.

The literature review is a summary of a much larger work, and in so doing, the resulting simplification may run the risk of missing out on important details. However, with this risk in mind, the review holds rich enough discussion on complexity to be relevant for research about complex risk management.

The current implication for practice is that the paper strongly supports the notion of using a margin of safety as advocated by Graham and his most famous disciple Warren Buffet. This comes from the fact that because context is king, risk management approaches must be applied in their right domain. There is no one right way. In the future, the goal is to develop a quantitative approach that can help the industry in pricing complex risks.

The main contribution of the paper is to bring complexity theory more into the domain of risk management with sufficient details that should allow researchers to get conceptual ideas about what might work or not concerning complex risk management. If nothing else, it would be a significant contribution of the paper if it could help increasing the interest in complexity theory.

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.