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Cybernetics' core theme of circular causality was developed beyond goal-oriented processes to concerns with reflexivity and recursion. This was especially so in the development of second-order cybernetics (or the cybernetics of cybernetics), developed and promoted by Heinz von Foerster, which focused on questions of observation, cognition ...
The perceptual control theory is deeply rooted in biological cybernetics, systems biology and control theory and the related concept of feedback loops. Unlike some models in behavioral and cognitive psychology it sets out from the concept of circular causality.
circular causal loops rather than linear causality, self-organization, observation as part of or directly related to systems, and; reflexivity or interaction between a system and what is known about it. Holistic Symmetry in Modern Science, webtext by Gary Witherspoon, 3 April 2007.
Circular cumulative causation is a theory developed by Swedish economist Gunnar Myrdal who applied it systematically for the first time in 1944 (Myrdal, G. (1944), An American Dilemma: The Negro Problem and Modern Democracy, New York: Harper). It is a multi-causal approach where the core variables and their linkages are delineated.
According to George Richardson's book "Feedback Thought in Social Science and Systems Theory", [2] the first published, formal use of a causal loop diagram to describe a feedback system was Magoroh Maruyama's 1963 article "The Second Cybernetics: Deviation-Amplifying Mutual Causal Processes".
Simple causal reasoning about a feedback system is difficult because the first system influences the second and second system influences the first, leading to a circular argument. This makes reasoning based upon cause and effect tricky, and it is necessary to analyze the system as a whole.
Systems theory is the transdisciplinary [1] study of systems, i.e. cohesive groups of interrelated, interdependent components that can be natural or artificial.Every system has causal boundaries, is influenced by its context, defined by its structure, function and role, and expressed through its relations with other systems.
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. [1] [2] Exploratory causal analysis (ECA), also known as data causality or causal discovery [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions.