Abstract Environmental decisions with substantial social and environmental implications are regularly informed by model predictions, incurring inevitable uncertainty. The selection of a set of model predictions to inform a decision is usually based on model performance, measured by goodness‐of‐fit metrics. Yet goodness‐of‐fit metrics have a questionable relationship to a model's value to end users, particularly when validation data are themselves uncertain. For example, decisions based on flow frequency models are not necessarily improved by adopting models with the best overall goodness of fit. We propose an alternative model evaluation approach based on the conditional value of sample information, first defined in 1961, which has found extensive use in sampling design optimization but which has not previously been used for model evaluation. The metric uses observations from a validation set to estimate the expected monetary costs associated with model prediction uncertainties. A model is only considered superior to alternatives if (i) its predictions reduce these costs and (ii) sufficient validation data are available to distinguish its performance from alternative models. By describing prediction uncertainties in monetary terms, the metric facilitates the communication of prediction uncertainty by end users, supporting the inclusion of uncertainty analysis in decision making.
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Model Validation Based on Value-of-Information Theory
Abstract The modeling and simulation community has devoted considerable attention to the question of model validity. Their work has focused on the concept of “accuracy,” loosely defined as the difference between a model-computed result and a real-world result. This paper makes use of an example case that results in a paradox to illustrate weaknesses in an accuracy-focused approach, and proposes in its stead a value-focused approach based on classical decision theory. Instead of advocating the use of a model based on its accuracy, this work advocates using a model if it adds value to the overall application thus relating validation directly to system performance. The approach fills significant gaps in the current theory, notably providing a clearly defined validity metric and a fundamental rationale for the use of this metric.
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- Award ID(s):
- 1923164
- PAR ID:
- 10530966
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8734-9
- Format(s):
- Medium: X
- Location:
- Boston, Massachusetts, USA
- Sponsoring Org:
- National Science Foundation
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