This paper introduces a choice-based method that for the first time makes it possible to quantitatively measure regret theory, one of the most popular models of decision under uncertainty. Our measurement is parameter-free in the sense that it requires no assumptions about the shape of the functions reflecting utility and regret. The choice of stimuli was such that event-splitting effects could not confound our results. Our findings were largely consistent with the assumptions of regret theory although some deviations were observed. These deviations can be explained by psychological heuristics and referencedependence of preferences.

The central idea of Disappointment theory is that an individual forms an expectation about a risky alternative, and may experience disappointment if the outcome eventually obtained falls short of the expectation. We abandon the hypothesis of a well-defined prior expectation: disappointment feelings may arise from comparing the outcome received with anyof the gamble’s outcomes that the individual failed to get. This leads to a new, general form of Disappointment model. It encompasses Rank Dependent Utility with an explicit one-parameter probability transformation, and Risk-Value models with a generic risk measure including Variance, providing a unifying behavioral foundation for these models. Copyright Springer Science + Business Media, LLC 2006Disappointment theory, Rank Dependent utility, Risk-value models, Mean-variance, Expected Utility violations,

We performed a new test of intransitive choice based on individual measurements of regret theory, the most influential intransitive theory. Our test is tailor-made and, therefore, more likely to detect violations of transitivity than previous tests. In spite of this, we observed only few cycles and we could not reject the hypothesis that they were due to random error. Moreover, there was little evidence that regret affected people's choices. A possible explanation for the poor predictive performance of regret theory is that, unlike other non-expected utility models, it assumes that preferences are separable over states of nature. Our data suggest that to account for the violations of expected utility event-separability has to be relaxed.

We develop a model of Disappointment in which disappointment and elation arise from comparing the outcome received, not with an expected value as in previous models, but rather with the other individual outcomes of the lottery. This approach may better reflect the way individuals are liable to experience disappointment. The model obtained accounts for classic behavioral deviations from the normative theory, offers a richer structure than previous disappointment models, and leads to a Rank-Dependent Utility formulation in a transparent way. Thus, our disappointment model may provide a clear psychological rationale for the subjective transformation of probabilities. Copyright Springer 2006disappointment theory, expected utility violations, probability weighting, rank-dependent utility,

Risk-informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value-of-information-based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.