scholarly journals The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

Homeopathy ◽  
2019 ◽  
Vol 109 (02) ◽  
pp. 042-050 ◽  
Author(s):  
Iris R. Bell
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
The Body ◽  
Systems Science ◽  
Human Beings ◽  
Small Magnitude ◽  
Non Linear ◽  
Complex Adaptive ◽  
Complex Systems Science ◽  
Over Time

Abstract Background The concepts of complex systems science enhance the understanding of how people develop and recover from disease. Living systems (human beings, animals, and plants) are self-organizing complex adaptive systems (CAS): that is, interconnected networks. CAS maintain life by initiating and carrying out non-linear dynamical changes to optimize survival fitness and function in the context of an ever-changing environment. Aims In Part 1 of this two-part paper, we relate concepts from complex systems science to homeopathic healing. The systemic changes of homeopathic healing involve adaptive patterns of responses to salient signals (similia) for reversing disease patterns and generating emergent multi-symptom healing over time. Methods and Results This narrative review relates homeopathic clinical practice theory to complex systems and network research. Homeopathic medicines communicate individually salient environmental information to the organism, with effects that are multi-system and indirect. The body's defense mechanisms recognize the self-similar information that the correctly chosen simillimum medicine at low dose conveys as a weak external/internal environmental stressor or danger signal (hormetin) to mobilize neural and cellular defenses. The body networks then use endogenous cell to cell signaling and amplify the small magnitude signal information. The results are disproportionately large: that is, non-linear, adaptive, modifications across the inter-connected self-organized biological networks/sub-systems of the body. CAS amplification mechanisms for small or weak signals include stochastic resonance, time-dependent sensitization, and hormesis. Conclusions The body as a complex system has the capacity for self-organization, emergence and self-similarity over global (overall health and wellbeing) and local (organ) levels of organization. These features are key for future research on the systemic healing that evolves over time during individualized homeopathic treatment.

scholarly journals Integrating complex systems science into road safety research and practice, part 1: review of formative concepts

2019 ◽  
Vol 26 (2) ◽  
pp. 177-183
Author(s):  
Rebecca B Naumann ◽  
Jill Kuhlberg ◽  
Laura Sandt ◽  
Stephen Heiny ◽  
Yorghos Apostolopoulos ◽  
...  
Keyword(s):  
Public Health ◽  
Complex Systems ◽  
Injury Prevention ◽  
Road Safety ◽  
Systems Science ◽  
Death Rates ◽  
Research And Practice ◽  
Safety Research ◽  
Complex Systems Science ◽  
Over Time

Many of our most persistent public health problems are complex problems. They arise from a web of factors that interact and change over time and may exhibit resistance to intervention efforts. The domain of systems science provides several tools to help injury prevention researchers and practitioners examine deep, complex and persistent problems and identify opportunities to intervene. Using the increase in pedestrian death rates as an example, we provide (1) an accessible overview of how complex systems science approaches can augment established injury prevention frameworks and (2) a straightforward example of how specific systems science tools can deepen understanding, with a goal of ultimately informing action.

Educating for Cyber (Security)

2021 ◽  
pp. 394-408
Author(s):  
Roger Bradbury
Keyword(s):  
Complex Systems ◽  
Entropy Production ◽  
Maximum Entropy ◽  
Cyber Security ◽  
Social Systems ◽  
Physical Principle ◽  
Systems Science ◽  
Technological Singularity ◽  
Complex Systems Science ◽  
Over Time

This chapter considers the problem of educating for cybersecurity from the perspective of complex systems science. It argues that education is a process that has evolved in human social systems to curate, increase, and transmit the information needed for system survival. Education creates an increase in the negentropy (or useful information) of those systems as they seek to maximize the acquisition and throughput of energy—a physical principle known as maximum entropy production (MaxEP). Civilizations have responded to this principle over time by finding new solutions to the Earth’s MaxEP and becoming more complex in the process. A key part of this complexification is education. And in the present cyber age it is, as in previous ages, a lagging process cobbled together from the structures and processes of previous ages. The current education responses may soon be superseded as a new solution to the Earth’s MaxEP—the technological singularity—looms.

The crucial roles of the artificial information systems web in intelligent human organizations

2003 ◽  
Vol 22 (3) ◽  
pp. 115-124
Author(s):  
Liang Thow Yick
Keyword(s):  
Information Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Collective Intelligence ◽  
Physical Structure ◽  
Current Status ◽  
Human Beings ◽  
Intelligent Structure ◽  
Complex Adaptive ◽  
Human Thinking

Human organizations with human beings as interacting agents are complex adaptive systems. Such organizations continuously consume information, make decisions, and evolve with the changing environment. In this respect, all human organizations including businesses must enhance their collective intelligence in order to learn faster and compete more effectively. Thus, adopting an intelligent structure is vital to all businesses as the world moves deeper into the knowledge economy. The paradigmatic shift in thinking, structure, management and operation requires all intelligent human organizations to be designed around intelligence. An intelligent structure encompasses an orgmind, an intangible deep component, as well as a physical component. At the physical structure perspective, being able to identify, design and develop an artificial information systems network that synchronizes well with the orgmind is critical. The connectivity of the organization, and the manner in which it behaves, communicates and collaborates, depend on the effectiveness of its information systems network and its orgmind. The orgmind which is at least the collection of all the interacting human thinking systems must be fully aware of both the internal and external environments. Inevitably, in the new economy, intelligent human organizations must be equipped with a well-integrated intelligent information network which functions similarly to the nervous system in biological beings. This study examines the current status of artificial information systems and their networks in businesses with respect to the above concepts.

scholarly journals The emergence of consensus: a primer

2018 ◽  
Vol 5 (2) ◽  
pp. 172189 ◽  
Author(s):  
Andrea Baronchelli
Keyword(s):  
Game Theory ◽  
Social Networks ◽  
Complex Systems ◽  
Social Systems ◽  
Short Review ◽  
Systems Science ◽  
Social Consensus ◽  
Population Scale ◽  
Complex Systems Science

The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.

Complex Systems Science

2021 ◽  
pp. 243-256
Author(s):  
Matt Kasman ◽  
Nancy Breen ◽  
Ross A. Hammond
Keyword(s):  
Complex Systems ◽  
Systems Science ◽  
Complex Systems Science

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).

How Complex Systems Science Can Revolutionize Population Health Theory

2020 ◽  
pp. 73-88
Author(s):  
Patricia Goodson
Keyword(s):  
Complex Systems ◽  
Population Health ◽  
Policy Development ◽  
Theory Building ◽  
Public Health Workforce ◽  
Systems Science ◽  
Complex Health Problems ◽  
Complex Systems Science ◽  
Health Theory ◽  
And Training

This chapter discusses whether and how complex systems science (CSS) can revolutionize population health theory. First, the chapter defines theory and the practice of theory-building (or theorizing); second, it outlines some of the difficulties found in current population health theorizing; lastly, it characterizes the mechanisms through which CSS can influence, change, and revolutionize current theorizing efforts. The chapter also describes two examples of scholars who used CSS to challenge currently held assumptions and reframe complex health problems. Lastly, the author addresses the implications—of adopting a CSS approach to theorizing—for practice, policy development, and training of the future public health workforce.

Bridging the Divide

2020 ◽  
pp. 3-14
Author(s):  
Yorghos Apostolopoulos
Keyword(s):  
Risk Factor ◽  
Complex Systems ◽  
Population Health ◽  
Health Science ◽  
The Other ◽  
Systems Science ◽  
Shared Understanding ◽  
Key Concepts ◽  
The One ◽  
Complex Systems Science

This chapter contextualizes the volume and describes its organization. It begins by delving into the limitations of the prevailing reductionist paradigm in population health science and the need for a transition from a typically risk factor–based science to a science that recognizes the whole and relationships among parts of pressing population health problems. Next, it walks readers through distinctions between public and population health on the one hand and key concepts of complexity on the other, while offering a shared understanding of population health science and complex systems science. The chapter also lays out the design of and potential audiences for this book.

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.

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