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AbstractThe concept of 'a model' usually evokes the connotation 'model of part of the real world'. That is an almost automatic response. It makes sense especially in relation to the way the concept has been developed and used in natural science. Classical operational research (OR), with its scientific aspirations, and systems engineering, use the concept in the same way and in addition use models as surrogates for the real world, on which experimentation is cheap. In these fields the key feature of a model is representativeness. In soft systems methodology (SSM) models are not of part of the world; they are only relevant to debate about the real world and are used in a cyclic learning process. The paper shows how the different concepts of validation in classical OR and SSM lead to a way of sharply defining the nature of 'soft OR'.

AbstractSystem dynamics models, as causal models, are much like scientific theories. Hence, in evaluating such models, we assume certain norms of scientific inquiry. Most critics hold that the system dynamics approach does not employ formal, objective, quantitative model validation tests. This article argues that this type of criticism presupposes the traditional logical empiricist philosophy of science, which assumes that knowledge is an objective representation of reality and that theory justification can be an objective, formal process. According to the more recent relativist philosophy of science, knowledge is relative to a given society, epoch, and scientific world view. Theory justification is therefore a semiformal, relative social process. We show that relativist philosophy is consistent with the system dynamics paradigm and discuss the practical implications of the two philosophies of science for system dynamics modelers and their critics.

Validation is often defined as the process of determining the degree to which a model is an accurate representation of the real world from the perspective of its intended uses. Validation is crucial as industries and governments depend increasingly on predictions by computer models to justify their decisions. We propose to formulate the validation of a given model as an iterative construction process that mimics the often implicit process occurring in the minds of scientists. We offer a formal representation of the progressive build-up of trust in the model. Thus, we replace static claims on the impossibility of validating a given model by a dynamic process of constructive approximation. This approach is better adapted to the fuzzy, coarse-grained nature of validation. Our procedure factors in the degree of redundancy versus novelty of the experiments used for validation as well as the degree to which the model predicts the observations. We illustrate the methodology first with the maturation of quantum mechanics as the arguably best established physics theory and then with several concrete examples drawn from some of our primary scientific interests: a cellular automaton model for earthquakes, a multifractal random walk model for financial time series, an anomalous diffusion model for solar radiation transport in the cloudy atmosphere, and a computational fluid dynamics code for the Richtmyer–Meshkov instability.