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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 ...
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.
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.
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.
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.
Cybernetics is a transdisciplinary approach for exploring regulatory systems with feedback, their structures, constraints, and possibilities. Cybernetics is relevant to the study of systems, such as mechanical, physical, biological, cognitive, and social .
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.
Judea Pearl defines a causal model as an ordered triple ,, , where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U ...