Model calibration as a testing strategy for system dynamics models

By Rogelio Oliva. European Journal of Operational Research, 2003

322 citations recorded by the source corpus.

System dynamics models are becoming increasingly common in the analysis of policy and managerial issues. The usefulness of these models is predicated on their ability to link observable patterns of behavior to micro-level structure and decision-making processes. This paper posits that model calibration––the process of estimating the model parameters (structure) to obtain a match between observed and simulated structures and behaviors––is a stringent test of a hypothesis linking structure to behavior, and proposes a framework to use calibration as a form of model testing. It tackles the issue at three levels: theoretical, methodological, and technical. First, it explores the nature of model testing, and suggests that the modeling process be recast as an experimental approach to gain confidence in the hypothesis articulated in the model. At the methodological level, it proposes heuristics to guide the testing strategy, and to take advantage of the strengths of automated calibration algorithms. Finally, it presents a set of techniques to support the hypothesis testing process. The paper concludes with an example and a summary of the argument for the proposed approach.

Subfields: Artificial Intelligence, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/784ace7ab188af9c01891eb75da0a2db6f5426b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Environmental Engineering

In Management Science and Operations Research

Citations in the corpus

How to do structural validity of a system dynamics type simulation model: The case of an energy policy model (2010, background): “In contrast, the majority of policy models such as system dynamics (SD) type model and agent-based models are built for the analysis of policy, exploration of possible future scenarios, and management purposes (Gass, 1983; Sterman, 1984; Oliva, 2003; Scholl, 2001).”
System dynamics modeling for agricultural and natural resource management issues: Review of some past cases and forecasting future roles (2016, methodology): “, Monte Carlo simulation) and model boundary and extreme condition testing to gauge the model’s respect of physical and economic laws; and (3) analysis of scenarios compared to expert opinions (for model calibration and evaluation techniques, see [33,131,132]).”