Causal Loop Diagrams have long been used in standard system dynamics practice for two purposes connected with simulation modeling. They were initially employed after simulation, to summarize and communicate modelbased feedback insights (see, for example, Forrester 1968).
A few years later, they also started to be used prior to simulation analysis, to depict the basic causal mechanisms hypothesized to underlie the reference mode of behavior over time—that is, for articulation of a dynamic hypothesis (Randers 1973; Randers 1980).
The dynamic hypothesis is a cornerstone of good system dynamics modeling practice. It ‘‘explains the dynamics as endogenous consequences of the feedback structure’’ (Sterman 2000), and explicitly states how structure and decision policies generate behavior (Richardson and Pugh 1981). Moreover, ‘‘The inclusion of basic mechanisms from the outset forces the modeler to address a meaningful whole at all stages of model development.’’ (Randers 1973). That is, a dynamic hypothesis is the key to ensuring that the analysis is focused on diagnosing problematic behavior and not on enumerating the unlimited details of a ‘‘system’’.
With the advent of qualitative analysis in the 1980s, the causal-loop diagram started to be used for purposes not necessarily related to simulation modeling, namely, for detailed system description and for stand-alone policy analysis. Wolstenholme (1999) puts the case clearly:
> ‘Causal loop’ qualitative system dynamics enhances linear and ‘laundry list’ thinking by introducing circular causality and providing a medium by which people can externalise mental models and assumptions and enrich these by sharing them. Furthermore, it facilitates inference of modes of behaviour by assisting mental simulation of maps.
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HOMER, Jack and OLIVA, Rogelio, 2001. Maps and models in system dynamics: a response to Coyle. System Dynamics Review. Online. 2001. Vol. 17, no. 4, p. 347–355. [Accessed 17 March 2023]. DOI 10.1002/sdr.224. Geoff Coyle has recently posed the question as to whether or not there may be situations in which computer simulation adds no value beyond that gained from qualitative causal-loop mapping. We argue that simulation nearly always adds value, even in the face of significant uncertainties about data and the formulation of soft variables. This value derives from the fact that simulation models are formally testable, making it possible to draw behavioral and policy inferences reliably through simulation in a way that is rarely possible with maps alone. Even in those cases in which the uncertainties are too great to reach firm conclusions from a model, simulation can provide value by indicating which pieces of information would be required in order to make firm conclusions possible. Though qualitative mapping is useful for describing a problem situation and its possible causes and solutions, the added value of simulation modeling suggests that it should be used for dynamic analysis whenever the stakes are significant and time and budget permit. Copyright © 2001 John Wiley & Sons, Ltd.