1996System Dynamics ReviewRequires access

Why we iterate: scientific modeling in theory and practice

Jack Homer

Open publisher page 160 citations

Abstract

An approach to system dynamics modeling is advocated that adheres to the scientific method, and that may be applied regardless of model scope or size. Scientific modeling is distinguished from other approaches largely by the quality of evaluation and revision performed and by an insistence upon empirical evidence to support hypotheses and formulations. Three case studies drawn from the author's experience are presented. Practical lessons for scientific modeling are given to help guide expectations and maximize effectiveness of the approach. Modelers and clients should clearly understand the level of rigor they wish to pursue and what this means for the degree of confidence that may be placed in model results and insights.

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What this paper is about

An approach to system dynamics modeling is advocated that adheres to the scientific method, and that may be applied regardless of model scope or size. Scientific modeling is distinguished from other approaches largely by the quality of evaluation and revision performed and by an insistence upon empirical evidence to support hypotheses and formulations. Three case studies drawn from the author's experience are presented. Practical lessons for scientific modeling are given to help guide expectations and maximize effectiveness of the approach. Modelers and clients should clearly understand the level of rigor they wish to pursue and what this means for the degree of confidence that may be placed in model results and insights.

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OpenAlex reports 160 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

An approach to system dynamics modeling is advocated that adheres to the scientific method, and that may be applied regardless of model scope or size. Scientific modeling is distinguished from other approaches largely by the quality of evaluation and revision performed and by an insistence upon empirical evidence to support hypotheses and formulations. Three case studies drawn from the author's experience are presented. Practical lessons for scientific modeling are given to help guide expectations and maximize effectiveness of the approach. Modelers and clients should clearly understand the level of rigor they wish to pursue and what this means for the degree of confidence that may be placed in model results and insights.

Key concepts: Scope (computer science), Management science, Computer science, Scientific modelling, Scientific evidence, Quality (philosophy), Empirical evidence, Operations research

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