Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?

Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give... Robustness is being used increasingly for decision analysis in relation to deep uncertainty and many metrics have been proposed for its quantification. Recent studies have shown that the application of different robustness metrics can result in different rankings of decision alternatives, but there has been little discussion of what potential causes for this might be. To shed some light on this issue, we present a unifying framework for the calculation of robustness metrics, which assists with understanding how robustness metrics work, when they should be used, and why they sometimes disagree. The framework categorizes the suitability of metrics to a decision‐maker based on (1) the decision‐context (i.e., the suitability of using absolute performance or regret), (2) the decision‐maker's preferred level of risk aversion, and (3) the decision‐maker's preference toward maximizing performance, minimizing variance, or some higher‐order moment. This article also introduces a conceptual framework describing when relative robustness values of decision alternatives obtained using different metrics are likely to agree and disagree. This is used as a measure of how “stable” the ranking of decision alternatives is when determined using different robustness metrics. The framework is tested on three case studies, including water supply augmentation in Adelaide, Australia, the operation of a multipurpose regulated lake in Italy, and flood protection for a hypothetical river based on a reach of the river Rhine in the Netherlands. The proposed conceptual framework is confirmed by the case study results, providing insight into the reasons for disagreements between rankings obtained using different robustness metrics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Earth's Future Wiley

Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?

Loading next page...
 
/lp/wiley/robustness-metrics-how-are-they-calculated-when-should-they-be-used-33LAZK9gyQ
Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018 American Geophysical Union
ISSN
2328-4277
eISSN
2328-4277
D.O.I.
10.1002/2017EF000649
Publisher site
See Article on Publisher Site

Abstract

Robustness is being used increasingly for decision analysis in relation to deep uncertainty and many metrics have been proposed for its quantification. Recent studies have shown that the application of different robustness metrics can result in different rankings of decision alternatives, but there has been little discussion of what potential causes for this might be. To shed some light on this issue, we present a unifying framework for the calculation of robustness metrics, which assists with understanding how robustness metrics work, when they should be used, and why they sometimes disagree. The framework categorizes the suitability of metrics to a decision‐maker based on (1) the decision‐context (i.e., the suitability of using absolute performance or regret), (2) the decision‐maker's preferred level of risk aversion, and (3) the decision‐maker's preference toward maximizing performance, minimizing variance, or some higher‐order moment. This article also introduces a conceptual framework describing when relative robustness values of decision alternatives obtained using different metrics are likely to agree and disagree. This is used as a measure of how “stable” the ranking of decision alternatives is when determined using different robustness metrics. The framework is tested on three case studies, including water supply augmentation in Adelaide, Australia, the operation of a multipurpose regulated lake in Italy, and flood protection for a hypothetical river based on a reach of the river Rhine in the Netherlands. The proposed conceptual framework is confirmed by the case study results, providing insight into the reasons for disagreements between rankings obtained using different robustness metrics.

Journal

Earth's FutureWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off