1,720,964 research outputs found
Deciding with thresholds: importance measures and value of information
No full textRisk-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 LOCA sequence of the ATR reactor helps us in illustrating the risk analysis insights
Mean-risk analysis with enhanced behavioral content
No full textWe study a mean-risk model derived from a behavioral theory of Disappointment with multiple reference points. One distinguishing feature of the risk measure is that it is based on mutual deviations of outcomes, not deviations from a specific target. We prove necessary and sufficient conditions for strict first and second order stochastic dominance, and show that the model is, in addition, a Convex Risk Measure. The model allows for richer, and behaviorally more plausible, risk preference patterns than competing models with equal degrees of freedom, including Expected Utility (EU), Mean–Variance (M-V), Mean-Gini (M-G), and models based on non-additive probability weighting, such as Dual Theory (DT). In asset allocation, the model allows a decision-maker to abstain from diversifying in a positive expected value risky asset if its performance does not meet a certain threshold, and gradually invest beyond this threshold, which appears more acceptable than the extreme solutions provided by either EU and M-V (always diversify) or DT and M-G (always plunge). In asset trading, the model provides no-trade intervals, like DT and M-G, in some, but not all, situations. An illustrative application to portfolio selection is presented. The model can provide an improved criterion for mean-risk analysis by injecting a new level of behavioral
realism and flexibility, while maintaining key normative properties
Expectations, Disappointment, and Rank-Dependent Probability Weighting
No full textWe 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,
Applying the Benchmarking Procedure: A Decision Criterion of Choice Under Risk
No full textModeling risk in a prescriptively plausible way represents a major issue in decision theory. The benchmarking procedure, being based on the satisficing principle and providing a probabilistic interpretation of expected utility (EU) theory, is prescriptive. Because it is a target-based language, the benchmarking procedure can be applied naturally to finance. In finance, the centrality of risk is widely recognized, but the risk measures that are commonly used to assess risk are too poor as a decision making tool. In this paper we propose a two-stage decision criterion of choice under risk that provides an application of benchmarking to finance through a risk measure. We will analyze some nonexpected utility theories, in particular lottery dependent utility, as potential frameworks for our criterion. Copyright Springer 2006Benchmarking, decision criterion, lottery-dependent utility, risk measure, two-stage procedure,
Disappointment without prior expectation: a unifying perspective on decision under risk
No full textThe 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,
Elicitation of multiattribute value functions through high dimensional model representations: monotonicity and interactions
No full textThis work addresses the early phases of the elicitation of multiattribute value functions proposing a practical method for assessing interactions and monotonicity. We exploit the link between multiattribute value functions and the theory of high dimensional model representations. The resulting elicitation method does not state any a-priori assumption on an individual’s preference structure. We test the approach via an experiment in a riskless context in which subjects are asked to evaluate mobile phone packages that differ on three attributes
On the relationship between safety and decision significance
No full textRisk analysts are often concerned with identifying key safety drivers, i.e., the systems, structures and components (SSCs) that matter the most to safety. SSCs importance is assessed both in the design phase (i.e., before a system is built) and in the implementation phase (i.e., when the system has been built) using the same importance measures. However, in a design phase it would be necessary to appreciate whether the failure/success of a given SSC can cause the overall decision to change from accept to reject (decision significance). This work addresses the search for the conditions under which SSCs which are safety significant are also decision significant. To address this issue, the work proposes the notion of importance measure. We study in detail the relationships among risk importance measures to determine which properties guarantee that the ranking of SSCs does not change before and after the decision is made. An application to a Probabilistic Safety Assessment model developed at NASA illustrates the risk management implications of our work
A Quantitative Measurement of Regret Theory
No full textThis paper introduces a method to measure regret theory, a popular theory of decision under uncertainty. Regret theory allows for violations of transitivity, and it may seem paradoxical to quantitatively measure an intransitive theory. We adopt the trade-off method and show that it is robust to violations of transitivity. Our method makes no assumptions about the shape of the functions reflecting utility and regret. It can be performed at the individual level, taking account of preference heterogeneity. Our data support the main assumption of regret theory, that people are disproportionately averse to large regrets, even when event-splitting effects are controlled for. The findings are robust: similar results were obtained in two measurements using different stimuli. The data support the reliability of the trade-off method: its measurements could be replicated using different stimuli and were not susceptible to strategic responding.regret theory, utility measurement, decision under uncertainty
A tailor-made test of intransitive choice
No full textThis paper reports a new test of intransitive choice using individual measurements of regret- and similarity-based intransitive models of choice under uncertainty. Our test is tailor-made and uses subject-specific stimuli. Despite these features, we observed only a few intransitivities. A possible explanation for the poor predictive performance of intransitive choice models
is that they only allow for interactions between acts. They exclude within-act interactions by retaining the assumption that
preferences are separable over states of nature. Prospect theory, which relaxes separability but retains transitivity, predicted choices better. Our data suggest that descriptively realistic models must allow for within-act interactions but may retain transitivity
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