Knowra Systems thinking Systems thinking Systems thinking is an approach that explains the behavior of a whole by examining relationships, feedback, boundaries, and change among its interconnected parts.
System : A set of interacting elements organized in relation to a purpose or surrounding environment. Systems thinking begins by deciding what elements and relationships make up the whole.
Causal loop diagram : A diagram representing causal relationships and feedback loops among variables in a system. It makes hypothesized feedback structures explicit before building a formal model.
Systems engineering : An engineering discipline that designs and manages complex systems across their life cycles. It applies whole-system reasoning to requirements, components, integration, and performance.
Ludwig von Bertalanffy : An Austrian biologist who developed general systems theory as a framework for studying organized wholes. His work helped establish a cross-disciplinary language for systems.
Reductionism : An approach that explains complex phenomena by analyzing their constituent parts. Systems thinking complements part-based analysis by emphasizing relationships and whole-system behavior.
Feedback loop : A circular causal sequence in which a change in one variable eventually affects that variable again. Feedback explains how system behavior can amplify or dampen change.
Reinforcing feedback : A feedback loop in which change in a variable produces further change in the same direction. It explains accelerating growth, collapse, and self-reinforcing social patterns.
Soft systems methodology : A participatory approach for exploring complex situations with conflicting interpretations and goals. It adapts systems ideas to messy human problems without assuming one agreed definition.
Norbert Wiener : An American mathematician who founded cybernetics, the study of control and communication in animals and machines. Cybernetics supplied concepts of feedback and control central to systemic analysis.
Linear thinking : Reasoning that treats causes and effects as proportional, sequential, and largely independent. It misses feedback, delays, and interactions that generate nonlinear outcomes.
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