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Decisions in many disciplines are based on understanding and evidence. More evidence is better than less when it enhances the decision‐maker's understanding. This is achieved by reducing uncertainty confronting the decision‐maker and reducing the potential for misunderstanding and failure. However, some evidence may actually augment uncertainty by revealing prior error or ignorance. True evidence that augments uncertainty is important because it identifies inadequacies of current understanding and may suggest directions for rectifying this. True evidence that reduces uncertainty may simply reconfirm or strengthen prior understanding. Uncertainty‐augmenting evidence, when it is true, can support the expansion of one's previously incomplete understanding. A dilemma arises because both reduction and enhancement of uncertainty can be beneficial, and both are not simultaneously possible on the same issue. That is, uncertainty can be either pernicious or propitious. Info‐gap theory provides a response. The info‐gap robustness function enables protection against pernicious uncertainty by inhibiting failure. The info‐gap opportuneness function enables exploitation of propitious uncertainty by facilitating wonderful windfall outcomes. The dilemma of uncertainty‐augmenting evidence is that robustness and opportuneness are in conflict; a decision that enhances one, worsens the other. This antagonism between robustness and opportuneness—between protecting against pernicious uncertainty and exploiting propitious uncertainty—is characterized in a generic proposition and corollary. These results are illustrated in an example of allocation of limited resources. [ABSTRACT FROM AUTHOR]

Objective: Medical decision-making is often uncertain. The positive predictive value (PPV) and negative predictive value (NPV) are conditional probabilities characterizing diagnostic tests and assessing diagnostic interventions in clinical medicine and epidemiology. The PPV is the probability that a patient has a specified disease, given a positive test result for that disease. The NPV is the probability that a patient does not have the disease, given a negative test result for that disease. Both values depend on disease incidence or prevalence, which may be highly uncertain for unfamiliar diseases, epidemics, etc. Probability distributions for this uncertainty are usually unavailable. We develop a non-probabilistic method for interpreting PPV and NPV with uncertain prevalence. Methods: Uncertainty in PPV and NPV is managed with the non-probabilistic concept of robustness in info-gap theory. Robustness of PPV or NPV estimates is the greatest uncertainty (in prevalence) at which the estimate's error is acceptable. Results: Four properties are demonstrated. Zeroing: best estimates of PPV or NPV have no robustness to uncertain prevalence; best estimates are unreliable for interpreting diagnostic tests. Trade-off: robustness increases as error increases; this trade-off identifies robustly reliable error in PPV or NPV. Preference reversal: sometimes sub-optimal PPV or NPV estimates are more robust to uncertain incidence or prevalence than optimal estimates, motivating reversal of preference from the putative optimum to the sub-optimal estimate. Trade-off between specificity and robustness to uncertainty: the robustness increases as test-specificity decreases. These four properties underlie the interpretation of PPV and NPV. Conclusions: The PPV and NPV assess diagnostic tests, but are sensitive to lack of knowledge that generates non-probabilistic uncertain prevalence and must be supplemented with robustness analysis. When uncertainties abound, as with unfamiliar diseases, assessing robustness is critical to avoiding erroneous decisions. [ABSTRACT FROM AUTHOR]

Nations face two types of challenges—threats and opportunities—both of which are usually very uncertain. Intelligence analysis must support the selection of policy or action in response to both types of challenges. This article focuses on methodology in support of decisionmaking, and claims that different analytical tools are appropriate for threats and for opportunities. Decision support for responding to threats should be based on evaluating the robustness against uncertainty, while guaranteeing specified critical outcomes. In this approach, the quality of the outcome is adequate (the outcome is satisficed) while the robustness to surprise is maximized. This methodology is called robust satisficing. This differs from the standard approach of attempting to achieve an optimal outcome. Decision support for responding to opportunities should be based on exploiting favorable circumstances to facilitate (although not necessarily guarantee) better than anticipated outcomes. In this approach, the analyst supports a decisionmaker who aims at wonderful windfall outcomes at the lowest possible uncertainty. This is called opportune windfalling. We discuss several examples and explore the theoretical properties of, and relation between, these two decision strategies, based on info-gap decision theory. [ABSTRACT FROM AUTHOR]