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A causal loop diagram (CLD) is a causal diagram that visualizes how different variables in a system are causally interrelated. The diagram consists of a set of words and arrows. Causal loop diagrams are accompanied by a narrative which describes the causally closed situation the CLD describes.
A causal loop diagram is a simple map of a system with all its constituent components and their interactions. By capturing interactions and consequently the feedback loops (see figure below), a causal loop diagram reveals the structure of a system. By understanding the structure of a system, it becomes possible to ascertain a system's behavior ...
Causal mapping is the process of constructing, summarising and drawing inferences from a causal map, and more broadly can refer to sets of techniques for doing this. While one group of such methods is actually called “causal mapping”, there are many similar methods which go by a wide variety of names.
A causal loop diagram of growth and underinvestment The growth and underinvestment archetype is one of the common system archetype patterns defined as part of the system dynamics discipline. System dynamics is an approach which strives to understand, describe and optimize nonlinear behaviors of complex systems over time, using tools such as ...
A causal diagram consists of a set of nodes which may or may not be interlinked by arrows. Arrows between nodes denote causal relationships with the arrow pointing from the cause to the effect. There exist several forms of causal diagrams including Ishikawa diagrams, directed acyclic graphs, causal loop diagrams, [10] and why-because graphs (WBGs
Fig. 1: Causal loop diagram. In system dynamics this is described by a circles of causality (Fig. 1) as a system consisting of two feedback loops. One is the balancing feedback loop B1 of the corrective action, the second is the reinforcing feedback loop R2 of the unintended consequences. These influence the problem with a delay and therefore ...
A system archetype is a pattern of behavior of a system.Systems expressed by circles of causality have therefore similar structure.Identifying a system archetype and finding the leverage enables efficient changes in a system.
Causal graphs can be used for communication and for inference. They are complementary to other forms of causal reasoning, for instance using causal equality notation. As communication devices, the graphs provide formal and transparent representation of the causal assumptions that researchers may wish to convey and defend.