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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. Closed loops, or causal feedback loops, in the diagram are very important features of CLDs because they may help identify non-obvious vicious circles and virtuous circles.
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 ...
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 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 ...
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 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.
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 ...
A causal network is a Bayesian network with the requirement that the relationships be causal. The additional semantics of causal networks specify that if a node X is actively caused to be in a given state x (an action written as do( X = x )), then the probability density function changes to that of the network obtained by cutting the links from ...