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Separating the wheat from the chaff: The effective use of mathematical models as decision tools
Author(s) -
Glaser David,
Bridges Todd S
Publication year - 2007
Publication title -
integrated environmental assessment and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 57
eISSN - 1551-3793
pISSN - 1551-3777
DOI - 10.1002/ieam.5630030313
Subject(s) - distrust , computer science , process (computing) , calibration , decision model , range (aeronautics) , mathematical model , a priori and a posteriori , management science , operations research , risk analysis (engineering) , data mining , machine learning , engineering , mathematics , law , aerospace engineering , operating system , philosophy , epistemology , medicine , statistics , political science
The purpose of this paper is to discuss the effective use of quantitative modeling in environmental decision making, with a particular focus on problems of contaminated sediment and surface water. The intended audience includes both model developers and model users. Our goal is to facilitate more effective communication among model developers and those using the information produced by models to aid decision making. We provide a series of observations or conclusions we have reached in our experience that are as follows. A model is a tool for evaluating alternate hypotheses; a model itself is not a hypothesis. All decisions are actually based upon models, either explicitly or implicitly. Models are used to address diagnostic and prognostic questions. Models can provide value added when applied throughout the lifetime of a project. Uncertainty, and therefore the need for models, is greater in systems near background. Models can provide useful information even when based on relatively small data sets. The utility of a model depends on the strength of the constraints placed upon it. The calibration process can be only partially specified a priori. Model calibration and evaluation require multiple lines of evidence. Uncertainty analysis is both qualitative and quantitative. Validation is provided by the application of the model under a wide range of conditions. Communication of the strength of model constraints is critical to model acceptance. We conclude that while models are often used in the evaluation of contaminated sediment problems, distrust in the use of models remains strong. The assessment of uncertainty is the factor most limiting acceptability.