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.

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).

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.

scholarly journals Next Generation Data Infrastructures: Towards an Extendable Model of the Asset Management Data Infrastructure as Complex Adaptive System

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Paul Brous ◽  
Marijn Janssen ◽  
Paulien Herder
Keyword(s):  
Complex Adaptive Systems ◽  
Asset Management ◽  
Adaptive Systems ◽  
New Technologies ◽  
Complex Adaptive System ◽  
Emergent Behavior ◽  
Data Infrastructure ◽  
Management Domain ◽  
Complex Adaptive ◽  
Data Infrastructures

Organizations are increasingly looking to adopt the Internet of Things (IoT) to collect the data required for data-driven decision-making. IoT might yield many benefits for asset management organizations engaged in infrastructure asset management, yet not all organizations are equipped to handle this data. IoT data is collected, stored, and analyzed within data infrastructures and there are many changes over time, resulting in the evolution of the data infrastructure and the need to view data infrastructures as complex adaptive systems (CAS). Such data infrastructures represent information about physical reality, in this case about the underlying physical infrastructure. Physical infrastructures are often described and analyzed in literature as CASs, but their underlying data infrastructures are not yet systematically analyzed, whereas they can also be viewed as CAS. Current asset management data models tend to view the system from a static perspective, posing constraints on the extensibility of the system, and making it difficult to adopt new data sources such as IoT. The objective of the research is therefore to develop an extensible model of asset management data infrastructures which helps organizations implement data infrastructures which are capable of evolution and aids the successful adoption of IoT. Systematic literature review and an IoT case study in the infrastructure management domain are used as research methods. By adopting a CAS lens in the design, the resulting data infrastructure is extendable to deal with evolution of asset management data infrastructures in the face of new technologies and new requirements and to steadily exhibit new forms of emergent behavior. This paper concludes that asset management data infrastructures are inherently multilevel, consisting of subsystems, links, and nodes, all of which are interdependent in several ways.

scholarly journals Organizing Theories for Disasters into a Complex Adaptive System Framework

Urban Science ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 61
Author(s):  
Annetta Burger ◽  
William G. Kennedy ◽  
Andrew Crooks
Keyword(s):  
Human Behavior ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complex Adaptive System ◽  
Disaster Studies ◽  
The Social ◽  
Complex Adaptive ◽  
Interacting Systems ◽  
Individual Actor ◽  
The Individual

Increasingly urbanized populations and climate change have shifted the focus of decision makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: (1) the physical system, consisting of geological, ecological, and human-built systems; (2) the social system, consisting of informal and formal human collective behavior; and (3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just to cities, but to disaster studies and connecting them in the CAS framework provides us with a new lens to study disasters across disciplines. This paper explores the theories and models used in disaster studies, provides a framework to study and explain disasters, and discusses how complex adaptive systems can support theory building in disaster science for promoting more sustainable and resilient cities.

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.

Patient Journey Record Systems (PaJR)

2011 ◽  
pp. 75-92 ◽  
Author(s):  
Carmel M. Martin ◽  
Rakesh Biswas ◽  
Ankur Joshi ◽  
Joachim P. Sturmberg
Keyword(s):  
Health Care ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Explicit Knowledge ◽  
Care Delivery ◽  
Allied Health Professionals ◽  
Patient Journey ◽  
Complex Adaptive ◽  
Care Systems ◽  
The Individual

This chapter argues the need for a paradigm shift to focus health care from a top down fragmented process driven activity to a user-driven journey of the individual whose health is at stake. Currently many person/patients express needs that are often overlooked or not understood in the health system, and the frontline care workers express frustration in relation to care systems that prevent them from optimizing their care delivery. We argue that complex adaptive systems and social constructionist theories provide a link for knowledge translation that ultimately will lead to improved health care and better personal health outcomes/experiences. We propose the Patient Journey Record System (PaJR) as a conceptual framework to transform health care so that it supports and improves the experience of patients and improves the quality of care through adaptable and interconnected provider information and care systems. Information technology, social networking and digital democracy is proposed as a major solution to the need to put the patient and their journey at the centre of health and health care with real time shaping of care to this end. Placing PaJR at the centre of care would enable patients, caregivers, physicians, nurses, allied health professionals and students to contribute to improving care. PaJR should become a ‘discovery tool’ of new knowledge arising from different types of experiences ranging from the implicit knowledge in narratives through to the explicit knowledge that is formalized in the published peer reviewed literature and translated into clinical knowledge.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2011 ◽  
pp. 35-59
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2009 ◽  
pp. 281-300
Author(s):  
Andrew P. Sage ◽  
Cynthia T. Small
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This chapter describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid Knowledge Management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

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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