Complex Adaptive Systems Thinking Approach to Enterprise Architecture

2014 ◽  
pp. 99-149 ◽  
Author(s):  
Marc Rabaey
Keyword(s):  
Complex Systems ◽  
Systems Thinking ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Enterprise Architecture ◽  
Time Dynamic ◽  
Complex Adaptive ◽  
Unstable Environment ◽  
Systems Thinking Approach ◽  
High Degree

Complex systems interact with an environment where a high degree of uncertainty exists. To reduce uncertainty, enterprises (should) create intelligence. This chapter shows that intelligence has two purposes: first, to increase and to assess (thus to correct) existing knowledge, and second, to support decision making by reducing uncertainty. The chapter discusses complex adaptive systems. Enterprises are not only complex systems; they are also most of the time dynamic because they have to adapt their goals, means, and structure to survive in the fast evolving (and thus unstable) environment. Crucial for enterprises is to know the context/ecology in which they act and operate. The Cynefin framework makes the organization and/or its parts aware of the possible contexts of the organization and/or its parts: simple, complicated, complex, chaotic, or disordered. It is crucial for the success of implementing and using EA that EA is adapted to function in an environment of perpetual change. To realize this, the chapter proposes and elaborates a new concept of EA, namely Complex Adaptive Systems Thinking – Enterprise Architecture (CAST-EA).

Complex Adaptive Systems Thinking Approach to Enterprise Architecture

2016 ◽  
pp. 339-389
Author(s):  
Marc Rabaey
Keyword(s):  
Complex Systems ◽  
Systems Thinking ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Enterprise Architecture ◽  
Time Dynamic ◽  
Complex Adaptive ◽  
Unstable Environment ◽  
Systems Thinking Approach ◽  
High Degree

Complex systems interact with an environment where a high degree of uncertainty exists. To reduce uncertainty, enterprises (should) create intelligence. This chapter shows that intelligence has two purposes: first, to increase and to assess (thus to correct) existing knowledge, and second, to support decision making by reducing uncertainty. The chapter discusses complex adaptive systems. Enterprises are not only complex systems; they are also most of the time dynamic because they have to adapt their goals, means, and structure to survive in the fast evolving (and thus unstable) environment. Crucial for enterprises is to know the context/ecology in which they act and operate. The Cynefin framework makes the organization and/or its parts aware of the possible contexts of the organization and/or its parts: simple, complicated, complex, chaotic, or disordered. It is crucial for the success of implementing and using EA that EA is adapted to function in an environment of perpetual change. To realize this, the chapter proposes and elaborates a new concept of EA, namely Complex Adaptive Systems Thinking – Enterprise Architecture (CAST-EA).

Complex Adaptive Systems Thinking Approach for Intelligence Base in Support of Intellectual Capital Management

2013 ◽  
pp. 122-141 ◽  
Author(s):  
Marc Rabaey
Keyword(s):  
Systems Thinking ◽  
Intellectual Capital ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Organizational Design ◽  
Complex Adaptive System ◽  
Holistic Approach ◽  
Holistic View ◽  
Complex Adaptive ◽  
Systems Thinking Approach

This chapter introduces Complex Adaptive Systems Thinking (CAST) into the domain of Intellectual Capital (IC). CAST is based on the theories of Complex Adaptive System (CAS) and Systems Thinking (ST). It argues that the CAST, combined with Intelligence Base offers a potentially more holistic approach to managing the Intellectual Capital of an organization. Furthermore, the authors extend this IC management with additional dimensions proper to a social entity such as an organization. New organizational design methods are needed and the capability approach is such a method that supports IC in virtual and real organizations. The characteristics of Intellectual Capital are discussed in the iterative process of inquiry and the Cynefin Framework, guaranteeing a holistic view on the organization and its environment.

Conceptual Mapping, Visualisation, and Systems Thinking in Engineering

2012 ◽  
pp. 72-93
Author(s):  
Carol Russell
Keyword(s):  
Systems Thinking ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Engineering Project ◽  
Technical Aspects ◽  
Conceptual Mapping ◽  
Complex Adaptive ◽  
Simplifying Assumptions ◽  
Technical Discussion ◽  
Tacit Assumptions

Diagrams and maps have uses beyond the purely technical representations that engineers routinely use as part of their work. Diagrams can also help to clarify and resolve non-technical aspects of an engineering project, by visualizing hidden assumptions, values, and priorities that might remain tacit and unresolved in a purely technical discussion. This chapter shows how systems thinking and mapping allows soft interpersonal and social aspects of an engineering project to be represented and discussed alongside hard technological activities. Any map or model of a complex and dynamic socio-technical system requires simplifying assumptions. Complex adaptive systems theory provides a conceptual framework for identifying the limitations from different types of simplification. Examples from educational technology and from mining engineering show how various types of conceptual map can help in clarifying, negotiating, and combining different perspectives on technologies in a complex human context – to overcome barriers of specialist language and tacit assumptions.

Modularity and Complex Adaptive Systems

2008 ◽  
pp. 75-104
Author(s):  
David Cornforth ◽  
David G. Green
Keyword(s):  
Fault Tolerance ◽  
Complex Systems ◽  
Long Range ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Single Unit ◽  
Phase Changes ◽  
Critical Phase ◽  
Complex Adaptive ◽  
Different Levels

Modularity is ubiquitous in complex adaptive systems. Modules are clusters of components that interact with their environment as a single unit. They provide the most widespread means of coping with complexity, in both natural and artificial systems. When modules occur at several different levels, they form a hierarchy. The effects of modules and hierarchies can be understood using network theory, which makes predictions about certain properties of systems such as the effects of critical phase changes in connectivity. Modular and hierarchic structures simplify complex systems by reducing long-range connections, thus constraining groups of components to act as a single component. In both plants and animals, the organisation of development includes modules, such as branches and organs. In artificial systems, modularity is used to simplify design, provide fault tolerance, and solve difficult problems by decomposition.

1. Complex systems

2014 ◽  
Author(s):  
John H. Holland
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Tropical Rainforest ◽  
Hierarchical Organization ◽  
Self Organization ◽  
Physical Systems ◽  
Complex Adaptive ◽  
Distinctive Property ◽  
Adaptive Interaction

What is complexity? A complex system, such as a tropical rainforest, is a tangled web of interactions and exhibits a distinctive property called ‘emergence’, roughly described by ‘the action of the whole is more than the sum of the actions of the parts’. This chapter explains that the interactions of interest are non-linear and thus hierarchical organization is closely tied to emergence. Complex systems explains several kinds of telltale behaviour: emergent behaviour, self-organization, chaotic behaviour, ‘fat-tailed behaviour’, and adaptive interaction. The field of complexity studies has split into two subfields that examine two different kinds of emergence: complex physical systems and complex adaptive systems.

scholarly journals The World is More Than Complicated

2011 ◽  
Vol 133 (11) ◽  
pp. 30-35
Author(s):  
Ahmed K. Noor
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Emergent Behavior ◽  
Global Response ◽  
Emergent Complexity ◽  
Engineering Designs ◽  
Complex Adaptive ◽  
Engineered Systems ◽  
The Individual

This article discusses the need of complex systems to be adaptive, and various innovative technologies that are required to engineer these systems. Complex adaptive systems consist of several simultaneously interacting parts or components, which are expected to function in an uncertain, complex environment, and to adapt to unforeseeable contingencies. The defining characteristics of complex adaptive systems are that the components are continually changing, the systems involve many interactions among components, and configurations cannot be fully determined in advance. Studies have shown that complex systems of the future will require a multidisciplinary framework—an approach that has been called emergent (complexity) engineering. Emergent engineering designs a system from the bottom-up by designing the individual components and their interactions that can lead to a desired global response. Although significant effort has been devoted to understanding complexity in natural and engineered systems, the research into complex adaptive systems is fragmented and is largely focused on specific examples. In order to accelerate the development of future diverse complex systems, there is a profound need for developing the new multidisciplinary framework of emergent engineering, along with associated systematic approaches, and generally valid methods and tools for high-fidelity simulations of the collective emergent behavior of these systems.

The complexity continuum, part 2: modelling harmony

Kybernetes ◽  
2019 ◽  
Vol 48 (8) ◽  
pp. 1626-1652 ◽  
Author(s):  
Maurice Yolles
Keyword(s):  
Complex Systems ◽  
Agency Theory ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Critical Realism ◽  
Philosophical Perspective ◽  
Content Type ◽  
Viable Systems ◽  
Complex Adaptive ◽  
Viable System

PurposeComplex 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.Design/methodology/approachExplanation 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.FindingsAnticipation 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.Originality/valueThere 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.

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