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Abstract. For obvious reasons, models for decision-making under severe uncertainty are austere. Simply put, there is precious little to work with under these conditions. This fact highlights the great importance of utilizing in such cases the ingredients of the mathematical model to the fullest extent, which in turn brings under the spotlight the art of mathematical modeling. In this discussion we examine some of the subtle considerations that are called for in the mathematical modeling of decision-making under severe uncertainty in general, and worst-case analysis in particular. As a case study we discuss the lessons learnt on this front from the Info-Gap experience.

PurposeThe purpose of this paper is to clarify a number of important facts about info‐gap decision theory.Design/methodology/approachTheorems are put forward to rebut claims made about info‐gap decision theory in papers published in this journal and elsewhere.FindingsInfo‐gap's robustness model is a simple instance of the most famous model in classical decision theory for the treatment of decision problems subject to severe uncertainty, namely Wald's maximin model. This simple instance is the equivalent of the well‐established model known universally as radius of stability. Info‐gap's robustness model has an inherent local orientation. Therefore, it is in principle unable to address the fundamental difficulties presented by the type of severe uncertainty that is postulated by info‐gap decision theory.Practical implicationsThese findings caution against accepting the assertions made in the info‐gap literature about: info‐gap decision theory's role and place in decision making under severe uncertainty; and its ability to model, analyze, and manage severe uncertainty.Originality/valueThis paper exposes the serious difficulties with claims made in papers published in this journal and elsewhere regarding the place and role of info‐gap decision theory in decision theory and its ability to handle severe uncertainty.

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The recent global financial crisis, natural disasters, and ongoing debate on global warming and climate change are a stark reminder of the huge challenges that severe uncertainty presents in decision and policy making. My objective in this paper is to look at some of the issues that need to be taken into account in the modeling and analysis of decision problems that are subject to severe uncertainty, paying special attention to some of the misconceptions that are being promulgated in this area. I also examine two diametrically opposed approaches to uncertainty. One, that emphasizes that the difficulties encountered in the modeling, analysis, and solution of decision problems in the face of severe uncertainty are in fact insurmountable, and another that claims to provide, against all odds, a reliable strategy for a successful handling of situations subject to severe uncertainty.

In this short discussion, we point out that it is apparently as easy to be fooled by robustness as it is to be fooled by randomness. Our objective is to bring to the attention of applied ecologists that radius-of-stability robustness models are models of local robustness. As such, these models are utterly unsuitable for the treatment/management of a severe uncertainty characterized by a vast uncertainty space and a likelihood-free quantification of the uncertainty. This observation is particularly pertinent to applications of info-gap decision theory in ecology, conservation biology, and environmental management, where the objective is to identify decisions that are robust against a severe uncertainty of this type.

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to illustrate the expressive power of Wald's maximin model and the mathematical modeling effort requisite in its application in decision under severe uncertainty. Design/methodology/approach -Decision making under severe uncertainty is art as well as science. This fact is manifested in the insight and ingenuity that the modeller/analyst is required to inject into the mathematical modeling of decision problems subject to severe uncertainty. The paper elucidates this point in a brief discussion on the mathematical modeling of Wald's maximin paradigm. Findings -The apparent simplicity of the maximin paradigm implies that modeling it successfully requires a considerable mathematical modeling effort. Practical implications -The paper illustrates the importance of mastering the art of mathematical modeling especially in the application of Wald's maximin model. Originality/value -This paper sheds new light on some of the modeling aspects of Wald's maximin paradigm.