The use of circular causality feedback loops 3. A terrific article applying systems thinking to product development using examples such as the iPod, Kindle, and Mini-Cooper. The article explains that a product is more than just the physical entity; it is the experience of researching, shopping, buying, using, and maintaining the product.
For example, the iPod is so successful not only because the physical device is beautiful and functional, but also because the music downloading and listening experiences are pleasurable. Mano presents a variety of examples of self-organization in natural systems, including zebra stripes, leopard spots, sand dune ripples, mud cracks, herding of wildebeests, and honeycomb cell structure.
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He also explains the forces underlying self-organization. In this text, the authors describe and explain a variety of self-organized natural structures such as ant trails, the synchronization of fireflies, the schooling of fish, bee honeycomb patterns, and termite cathedrals. They also discuss emergent properties. This is a mind-expanding article explaining how space and time themselves are self-organizing.
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Smolin explains how self-organization mechanisms create complexity from simple rules and that imbalance in the fundamental forces gravity, electromagnetic, strong nuclear, weak nuclear lead to inhomogeneity and complexity. He argues that the structure of the universe and even its origins are caused by self-organization. The article raises significant questions about the necessity of a prime mover in explaining the structure and existence of the universe. Emergence and S elf- O rganization in C ollective A ctors. This article focuses on self-organization in the natural sciences.
It links self-organization and emergence as well as self-organizational concepts in biology, economics, and sociology. It explains why reductionist thinking does not work for complex systems. However, the set covers a much broader base than just Systems Thinking; it covers most topics associated with systems including ecological modelling, systems theory, cybernetics, applications to society, family therapy, and management. Despite that, there are some excellent papers on reductionism, holism, emergence, self-organization, and complexity. There is no attempt at integration into a common definition or understanding.
Checkland presents a 7-step methodology for dealing with real-world soft systems problems and provides examples including the declining performance of a textile firm, mining equipment problems, executing useful and meaningful surveys, and the decision to land a man on the moon before The authors interviewed senior systems engineers and conclude that the principal mechanisms for developing systems thinking are experiential learning, a supportive environment, and personal characteristics such as personality, curiosity, open-mindedness, and the ability to tolerate uncertainty.
Systems Thinking also provides a language and a scientific technology for understanding and dealing with complexity and change. Systems Thinking has three aspects. These aspects can be used individually or in combination. The Systems Thinking Paradigm consists of a set of principles and theories. The Systems Thinking language uses diagrams to explain non-linear cause and effect relationships.
Systems Thinking modeling tools can be used to create powerful simulation models of organizational situations such as strategy development, process design and re-engineering, and team and organizational learning. It states that systems thinking is also a language involving diagrams, a syntax with precise rules, the translation of perceptions into pictures, and an emphais on closed-loop interdependencies. It advocates several tools including causal loop diagrams, stock-and-flow diagrams, computer simulations, learning laboratories, and group model building.
Maani and Cavana embrace a 4-tiered Iceberg Model and suggest 5 phases of systems thinking and modeling: 1 Structure the problem, 2 Construct Causal Loop diagrams, 3 Model dynamically, 4 Scenario Planning and Modelling, and 5 Implementation and Organizational Learning using Flight Simulators. The book ends with several case studies including the bird flu pandemic, quality in health services, the New Zealand fishing industry, and telecommunications business strategy. Valerdi describes 7 systems thinking competencies: 1.
Ability to define the "universe' appropriately - the system operates in this universe 2. Ability to define the overall system appropriately - defining the right boundaries 3. Ability to see relationships - within the system and between the system and universe 4. Ability to see things holistically - within and across relationships 5. Ability to understand complexity - how relationships yield uncertain, dynamic, nonlinear states and situations 6. Ability to communicate across disciplines - to bring multiple perspectives to bear 7.
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A system comes into existence when the complementary parts are brought together. Each then depends for its very existence on interchanges with the other parts. The organismic analogy proposes, not that all complex systems are organisms, but rather that, like biological organisms, they behave as unified wholes.
Each has a life-cycle, each exhibits growth, stability and death - often sudden, collapsing death. Viewing, or even considering, parts on their own is irrational. Systems and their problems have to be viewed as a whole. Holism observes the tendency of the natural world to create 'wholes,' and that a whole may be more than the sum of its parts This article focuses on a suggested approach for developing models to gain an understanding of the underlying structure s which give rise to observed patterns of behavior.
Bellinger proposes that such an approach consists of the following steps: 1. Define the Situation 2. Is Systems Thinking Appropriate?
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Develop Patterns of Behavior 4. Evolve the Underlying Structure 5. Simulate the Underlying Structure 6. Identify the Leverage Points 7.
Develop an Alternate Structure 8. Simulate the Alternate Structure 9. It goes on to outline and critique 10 applied systems approaches: hard systems thinking, system dynamics, organizational cybernetics, complexity theory, strategic assumption surfacing and testing, interactive planning, soft systems methodology, critical systems heuristics, team syntegrity, and post-modern systems thinking. This approach is unclear to us and does not represent an integrated perspective. The book is excellent, however, in describing and critiquing several popular systems approaches.
They next compared systems thinking definitions from 7 different authors and demonstrated that each definition fails their Systems Test; however they do identify the following commonalities among the definitions: interconnections, the understanding of dynamic behavior, systems structure as a cause of that behavior, and the idea of seeing systems as wholes rather than parts. These skills work together as a system. Thinking systemically also requires several shifts in perception, which lead in turn to different ways to teach, and different ways to organize society. However, there appear to be common themes that are repeated in many of the sources.
This section will attempt to identify and integrate those common themes into a coherent definition. The Systems Thinking Perspective Most sources agree that systems thinking is the opposite of linear thinking, and that it focuses on the relationships among system components, as opposed to the components themselves.
It is holistic integrative thinking instead of analytic dissective thinking. The scientific method prevalent in the last 2 centuries has taught us that we must break up complex situations into smaller and smaller pieces to understand them: dissective thinking. While this has great benefits, it also has the great disadvantage of ignoring the relationships among system components; those relationships often dominate systems behavior. Systems thinking requires that we study systems holistically.
This holistic thinking involves both spatial and temporal elements, as shown in Figure 2. Figure 2.http://soilstones.com/wp-content/2020-03-20/2885.php
Culture and reflexivity: systemic journeys with a British Chinese family
Systems Thinking versus Traditional Views The space element is often easier to grasp than the time element. But systems thinking requires that we ask: What circumstances and attitudes led to this point? What actions and behavior patterns led to this point? What are the likely attitudes, actions, and patterns going forward?
What are the probable reactions of my: allies, enemies, competitors, neutral 3 rd parties, and the environment? Systems Thinking thus requires a vision of the future as well as an understanding of the past. Systems thinking acknowledges that systems are dynamic, and has evolved from the field of General Systems Theory Bertalanffy.
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Systems are constantly subject to various forces and feedback mechanisms, some of which are stabilizing and some of which are reinforcing or de-stabilizing. This behavior is often counter-intuitive. System dynamics and system dynamics modeling are used to help understand the behavior of systems over time, to identify the driving variables so that system behavior may be positively impacted, and to help predict future states. It is important to note that systems thinking does not supplant either statistical or reductionist analytic thinking; it complements them, as shown in Figure 3: Figure 3.
All three approaches provide different but complementary perspectives on gaining more insight into and understanding of the behavior of a system. Systems Thinking requires that we recognize that in human-designed systems, repeated events or patterns derive from systemic structures which, in turn, derive from mental models.
This is clearly depicted in the Iceberg Model Figure 4 , which is a core element of systems thinking: Figure 4. The Iceberg Model The Iceberg Model argues that events and patterns which we can observe are caused by systemic structures and mental models, which are often hidden. Systemic structures are the organizational hierarchy; social hierarchy; interrelationships; rules and procedures; authorities and approval levels; process flows and routes; incentives, compensation, goals, and metrics; attitudes; reactions and the incentives and fears that cause them; corporate culture; feedback loops and delays in the system dynamics; and underlying forces that exist in an organization.
Behaviors derive from these structures, which are in turn established due to mental models or paradigms.