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What is a robust decision support quantitative model?

by Daniel J. Power
Editor, DSSResources.COM

Some quantitative models provide very different results when assumptions are violated and when model parameters are poorly estimated or incorrect. These models are hyper-sensitive and they are not robust. Robustness implies that the complexity of a quantitative model has only a small impact on the quality of the solution it produces. Simple models can yield robust results and solutions.

'Robust' is a characteristic describing a model's ability to effectively perform while its variables or assumptions are altered. A robust quantitative model can provide useful solutions under a variety of conditions.

Investopedia explains "Robustness can relate to both economic and statistical concepts. For statistics, a test is claimed as robust if it still provides insight to a problem despite having its assumptions altered or violated. In economics, robustness is attributed to financial markets that continue to perform despite alterations in market conditions. In general, being robust means a system can handle variability and remain effective." Read more: http://www.investopedia.com/terms/r/robust.asp#ixzz3XdQrN1NB

"Robust Decision Making is a decision support tool that is used in situations of deep uncertainty, i.e. in the absence of probabilistic information on scenarios and outcomes." from https://weadapt.org/knowledge-base/adaptation-training/module-robust-decision-making

References

Decision Modelling and Information Systems: The Information Value Chain By Nikitas-Spiros Koutsoukis, Gautam Mitra

Last update: 2015-04-17 10:26
Author: Daniel Power

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