Causal loop diagrams are graphical aids for depicting hypotheses about causality andfeedback mechanisms. Though widely used, causal loop diagramming has significantlimitations. Causal loop diagrams can contain numerous untested assumptions aboutcausality.This paper demonstrates how causal loop diagramming practice can be made morerobust. The proffered technique described in this paper suggests how we might improvethe ways we contemplate cause and effect. Applying the technique, and using the tool,offers new opportunities for testing assumptions about multi-factorial causal influences.The paper suggests that our first attempts at building remedial strategies for complexsystemic problems might be completed without building quantitative stock and flowmodels. The paper demonstrates how causal loop diagrams can be kept free of errorsin logic. It also raises a number of important issues regarding the ways we viewcombinations of potentially confounding causal influences, and identifies the need forfurther research in this area.
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