Bayesian advocates of expected utility maximization use sets of probability distributions to represent very different ideas. Strict Bayesians insist that probability judgment is numerically determinate even though the agent can represent such judgments only in imprecise terms. According to Quasi-Bayesians rational agents may make indeterminate subjective probability judgments. Both kinds of Bayesians require that admissible options maximize expected utility according to some probability distribution. Quasi-Bayesians permit the distribution to vary with the context of choice. Maximalists allow for choices that do not maximize expected utility against any distribution. Maximiners mandate what maximalists allow. This paper defends the quasi-Bayesian view against strict Bayesians, on the one hand, and maximalists and maximiners, on the other.

‘Ambiguous beliefs’ are beliefs which are inconsistent with a unique, additive prior. The problem of their update in face of new information has been dealt with in the theoretical literature, and received several contradictory answers. In particular, the ‘maximum likelihood update’ and the ‘full Bayesian update’ have been axiomatized. This experimental study attempts to test the descriptive validity of these two theories by using the Ellsberg experiment framework.

This paper surveys results of a research program investigating human judgments of imprecise probabilities under sample-space ignorance (i.e., ignorance of what the possible outcomes are in a decision). The framework used for comparisons with human judgments is primarily due to Walley (1991, 1996). Five studies are reported which test four of Walley's prescriptions for judgment under sample-space ignorance, as well as assessing the impact of the number of observations and types of events on subjective lower and upper probability estimates. The paper concludes with a synopsis of future directions for empirical research on subjective imprecise probability judgments.

This paper deals with a model of pollution accumulation in which a catastrophic environmental event occurs once the pollution stock exceeds some uncertain critical level. This problem is studied in a context of ‘hard uncertainty’ since we consider that the available knowledge concerning the value taken by the critical pollution threshold contains both randomness and imprecision. Such a general form of knowledge is modelled as a (closed) random interval. This approach is mathematically tractable and amenable to numerical simulations. In this framework we investigate the effect of hard uncertainty on the optimal pollution/consumption trade-off and we compare the results with those obtained both in the certainty case and in the case of ‘soft uncertainty’ (where only randomness prevails).

Our aim in this paper is to clarify the notion of independence for imprecise probabilities. Suppose that two marginal experiments are each described by an imprecise probability model, i.e., by a convex set of probability distributions or an equivalent model such as upper and lower probabilities or previsions. Then there are several ways to define independence of the two experiments and to construct an imprecise probability model for the joint experiment. We survey and compare six definitions of independence. To clarify the meaning of the definitions and the relationships between them, we give simple examples which involve drawing balls from urns. For each concept of independence, we give a mathematical definition, an intuitive or behavioural interpretation, assumptions under which the definition is justified, and an example of an urn model to which the definition is applicable. Each of the independence concepts we study appears to be useful in some kinds of application. The concepts of strong independence and epistemic independence appear to be the most frequently applicable.