Most decisions in life involve ambiguity, where probabilities can not be meaningfully specified, as much as they involve probabilistic uncertainty. In such conditions, the aspiration to utility maximization may be self-deceptive. We propose “robust satisficing” as an alternative to utility maximizing as the normative standard for rational decision making in such circumstances. Instead of seeking to maximize the expected value, or utility, of a decision outcome, robust satisficing aims to maximize the robustness to uncertainty of a satisfactory outcome. That is, (...) robust satisficing asks, “what is a ‘good enough’ outcome,” and then seeks the option that will produce such an outcome under the widest set of circumstances. We explore the conditions under which robust satisficing is a more appropriate norm for decision making than utility maximizing. (shrink)

Surprise and change are the way of the world. Philosophers have known this at least since Thales, and practical men knew it long before. Variety and the continual flux of one thing into another is, for Peirce, a central notion. A very similar conception underlies the information-gap theory of uncertainty and its application to decisions with severely deficient understanding which I have argued for earlier. For Haack, whose treatment of warrant is strongly non-probabilistic, info-gap theory is a natural context. The (...) connection between Peirce, Haack and info-gap theory is explored in this paper. (shrink)

Modern engineering has included the basic sciences and their accompanying mathematical theories among its primary tools. The theory of probability is one of the more recent entries into standard engineering practice in various technological disciplines. Probability and statistics serve useful functions in the solution of many engineering problems. However, not all technological manifestations of uncertainty are amenable to probabilistic representation. In this paper we identify the conceptual limitations of probabilistic and related theories as they occur in a wide range of (...) engineering tasks. We discuss the structure and properties of an alternative, non-probabilistic, method — convex modelling — for quantitatively representing uncertain phenomena. (shrink)