Severe uncertainty and info-gap decision theory
Article first published online: 16 APR 2013
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 7, pages 601–611, July 2013
How to Cite
Hayes, K. R., Barry, S. C., Hosack, G. R., Peters, G. W. (2013), Severe uncertainty and info-gap decision theory. Methods in Ecology and Evolution, 4: 601–611. doi: 10.1111/2041-210X.12046
- Issue published online: 2 JUL 2013
- Article first published online: 16 APR 2013
- Accepted manuscript online: 11 MAR 2013 01:14PM EST
- Manuscript Accepted: 27 FEB 2013
- Manuscript Received: 7 OCT 2012
- model parameters;
- model structure uncertainty;
- robust decisions;
- severe uncertainty
- Info-gap decision theory (IGDT) seeks to provide a framework for rational decision-making in situations of severe uncertainty. The theory proposes non-probabilistic models of uncertainty and requires relatively small information inputs when compared to alternative theories of uncertainty.
- Info-gap decision theory has been criticised because it is based upon models that do not guarantee good decisions in situations of severe uncertainty, where severe means a ‘very large’ uncertainty space and very poor initial estimates of the unknown elements in this space.
- This paper reviews the use of this method in ecology where it is receiving interest in applied environmental management applications. Paradoxically, ecological applications of IGDT focus almost exclusively on only one source of uncertainty in ecological problems, model parameter uncertainty, and typically ignore other sources, particularly model structure uncertainty and dependence between parameters, that can be just as severe.
- Ecologists and managers contemplating the use of IGDT should carefully consider its strengths and weaknesses, reviewed here, and not turn to it as a default approach in situations of severe uncertainty, irrespective of how this term is defined. We identify four areas of concern for IGDT in practice: sensitivity to initial estimates, localised nature of the analysis, arbitrary error model parameterisation and the ad hoc introduction of notions of plausibility.