background preloader

Systems thinking

Systems thinking
Impression of systems thinking about society[1] A system is composed of interrelated parts or components (structures) that cooperate in processes (behavior). Natural systems include biological entities, ocean currents, the climate, the solar system and ecosystems. Designed systems include airplanes, software systems, technologies and machines of all kinds, government agencies and business systems. Systems Thinking has at least some roots in the General System Theory that was advanced by Ludwig von Bertalanffy in the 1940s and furthered by Ross Ashby in the 1950s. The term Systems Thinking is sometimes used as a broad catch-all heading for the process of understanding how systems behave, interact with their environment and influence each other. Systems thinking has been applied to problem solving, by viewing "problems" as parts of an overall system, rather than reacting to specific parts, outcomes or events and potentially contributing to further development of unintended consequences. Related:  ☢️ Theories

BBC Systems Practice - Resources Copyrighted image Credit: The Open University Open2.net fades away... For ten years, give or take, Open2.net was the online home of Open University and BBC programming. Over the last few months, though, we've been moving into OpenLearn, creating one home for all The Open University's free learning content. It means we share a home with the Open University's iTunesU and YouTube channels, and much more besides. You can use the navigation at the top of this page to explore what we have on offer. There's lots to do - you could watch Evan Davis exploring the state of British manufacturing7; explore the frozen planet8; get to know the science and history of the Olympics9 or have a look at our free courses. Most of the content from Open2.net has been brought across; if you've landed here after typing or searching for an Open2.net URL then you're probably looking for something that fitted into one of these categories: Open2 forums We still want you to join in, comment and share your views.

Generalization A generalization (or generalisation) of a concept is an extension of the concept to less-specific criteria. It is a foundational element of logic and human reasoning.[citation needed] Generalizations posit the existence of a domain or set of elements, as well as one or more common characteristics shared by those elements. As such, they are the essential basis of all valid deductive inferences. The process of verification is necessary to determine whether a generalization holds true for any given situation. The concept of generalization has broad application in many related disciplines, sometimes having a specialized context or meaning. Of any two related concepts, such as A and B, A is a "generalization" of B, and B is a special case of A, if and only if every instance of concept B is also an instance of concept A; andthere are instances of concept A which are not instances of concept B. Hypernym and hyponym[edit] Examples[edit] Biological generalization[edit] Geometric generalizations[edit]

Systems Thinking and Systems Tools © Copyright Carter McNamara, MBA, PhD, Authenticity Consulting, LLC. Adapted from the Field Guide to Consulting and Organizational Development and Field Guide to Consulting and Organizational Development with Nonprofits. Three of the biggest breakthroughs in how we understand and successfully guide changes in ourselves, others and organizations are systems theory, systems thinking and systems tools. Sections of This Topic Include Basics -- Definitions - - - What's a System? Also seeRelated Library Topics Also See the Library's Blogs Related to Systems Theory, Chaos Theory and Systems Thinking In addition to the articles on this current page, also see the following blogs that have posts related to Systems Theory, Chaos Theory and Systems Thinking . Library's Business Planning BlogLibrary's Building a Business BlogLibrary's Coaching BlogLibrary's Consulting and Organizational Development BlogLibrary's Leadership BlogLibrary's Strategic Planning BlogLibrary's Supervision Blog What's a System?

Theories of Explanation  Within the philosophy of science there have been competing ideas about what an explanation is. Historically, explanation has been associated with causation: to explain an event or phenomenon is to identify its cause. But with the growth and development of philosophy of science in the 20th century, the concept of explanation began to receive more rigorous and specific analysis. A theory of explanation might treat explanations in either a realist or an epistemic (that is, anti-realist) sense. In contrast to these theoretical and primarily scientific approaches, some philosophers have favored a theory of explanation grounded in the way people actually perform explanation. This article focuses on the way thinking about explanation within the philosophy of science has changed since 1950. Table of Contents 1. Most people, philosophers included, think of explanation in terms of causation. A physical theory is not an explanation. Duhem claimed that: For Hempel, answering the question “Why?” 2.

Background Theory Focal Theory Data Theory Axiom An axiom or postulate is a premise or starting point of reasoning. As classically conceived, an axiom is a premise so evident as to be accepted as true without controversy.[1] The word comes from the Greek axíōma (ἀξίωμα) 'that which is thought worthy or fit' or 'that which commends itself as evident. In mathematics, the term axiom is used in two related but distinguishable senses: "logical axioms" and "non-logical axioms". In both senses, an axiom is any mathematical statement that serves as a starting point from which other statements are logically derived. Etymology[edit] The word "axiom" comes from the Greek word ἀξίωμα (axioma), a verbal noun from the verb ἀξιόειν (axioein), meaning "to deem worthy", but also "to require", which in turn comes from ἄξιος (axios), meaning "being in balance", and hence "having (the same) value (as)", "worthy", "proper". Ancient geometers maintained some distinction between axioms and postulates. Historical development[edit] Early Greeks[edit] Postulates

Adaptive system The term adaptation is used in biology in relation to how living beings adapt to their environments, but with two different meanings. First, the continuous adaptation of an organism to its environment, so as to maintain itself in a viable state, through sensory feedback mechanisms. Second, the development (through evolutionary steps) of an adaptation (an anatomic structure, physiological process or behavior characteristic) that increases the probability of an organism reproducing itself (although sometimes not directly).[citation needed] Generally speaking, an adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts. Some artificial systems can be adaptive as well; for instance, robots employ control systems that utilize feedback loops to sense new conditions in their environment and adapt accordingly. The Law of Adaptation[edit] Let .

Butterfly effect In chaos theory, the butterfly effect is the sensitive dependency on initial conditions in which a small change at one place in a deterministic nonlinear system can result in large differences in a later state. The name of the effect, coined by Edward Lorenz, is derived from the theoretical example of a hurricane's formation being contingent on whether or not a distant butterfly had flapped its wings several weeks earlier. Although the butterfly effect may appear to be an unlikely behavior, it is exhibited by very simple systems. For example, a ball placed at the crest of a hill may roll into any surrounding valley depending on, among other things, slight differences in its initial position. History[edit] Chaos theory and the sensitive dependence on initial conditions was described in the literature in a particular case of the three-body problem by Henri Poincaré in 1890.[1] He later proposed that such phenomena could be common, for example, in meteorology. Illustration[edit] , then

MINDSPACE Behavioural Economics Update: Professor Cass Sunstein, co-author of Nudge: Improving Decisions about Health, Wealth, and Happiness, will speak at the Institute for Government on 22 March 2013. Background New insights from science and behaviour change could lead to significantly improved outcomes, and at a lower cost, than the way many conventional policy tools are used. MINDSPACE: Influencing behaviour through public policy was published by the Institute for Government and the Cabinet Office on 2 March 2010. The report explores how behaviour change theory can help meet current policy challenges, such as how to: reduce crime tackle obesity ensure environmental sustainability. Today's policy makers are in the business of influencing behaviour - they need to understand the effects their policies may be having. Blogs MINDSPACE grows up – behavioural economics in government Reaction "brilliant" - Sir Gus O'Donnell, Cabinet Secretary "this is the best report of its kind - it is reflective and practical at the same time.

Grand theory Grand theory is a term invented by the American sociologist C. Wright Mills in The Sociological Imagination[1] to refer to the form of highly abstract theorizing in which the formal organization and arrangement of concepts takes priority over understanding the social world. In his view, grand theory was more or less separated from the concrete concerns of everyday life and its variety in time and space. The main target of Mills was Talcott Parsons, also an American sociologist and the architect of structural functionalism, against whom he insisted that there is no grand theory in the sense of one universal scheme to understand the unity of social structures, according to Gregory.[2] In Parsons view "grand theory" integrated not only sociological concepts, but also psychological, economic, political, and religious or philosophical components. According to Gregory[2] there are two critical responses to this (reformulated) grand theory. See also[edit] References[edit]

Neoclassical economics Neoclassical economics dominates microeconomics, and together with Keynesian economics forms the neoclassical synthesis which dominates mainstream economics today.[2] Although neoclassical economics has gained widespread acceptance by contemporary economists, there have been many critiques of neoclassical economics, often incorporated into newer versions of neoclassical theory. Overview[edit] The term was originally introduced by Thorstein Veblen in his 1900 article 'Preconceptions of Economic Science', in which he related marginalists in the tradition of Alfred Marshall et al. to those in the Austrian School.[3][4] "No attempt will here be made even to pass a verdict on the relative claims of the recognized two or three main "schools" of theory, beyond the somewhat obvious finding that, for the purpose in hand, the so-called Austrian school is scarcely distinguishable from the neo-classical, unless it be in the different distribution of emphasis. Three central assumptions[edit]

Rational Choice Theory (RCT) Rationality is widely used as an assumption of the behavior of individuals in microeconomic models and analyses and appears in almost all economics textbook treatments of human decision-making. It is also central to some of modern political science,[2] sociology,[3] and philosophy. A particular version of rationality is instrumental rationality, which involves seeking the most cost-effective means to achieve a specific goal without reflecting on the worthiness of that goal. Gary Becker was an early proponent of applying rational actor models more widely.[4] Becker won the 1992 Nobel Memorial Prize in Economic Sciences for his studies of discrimination, crime, and human capital.[5] Definition and scope[edit] The concept of rationality used in rational choice theory is different from the colloquial and most philosophical use of the word. Rational choice theorists do not claim that the theory describes the choice process, but rather that it predicts the outcome and pattern of choices.

Related: