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Ellsberg thought experiments and empirical confirmation of Ellsberg preferences pose serious challenges to subjective expected utility theory (SEUT). We have recently elaborated a quantum-theoretic framework for human decisions under uncertainty which satisfactorily copes with the Ellsberg paradox and other puzzles of SEUT. We apply here the quantum-theoretic framework to the Ellsberg two-urn example, showing that the paradox can be explained by assuming a state change of the conceptual entity that is the object of the decision (decision-making, or DM, entity) and representing subjective probabilities by quantum probabilities. We also model the empirical data we collected in a DM test on human participants within the theoretic framework above. The obtained results are relevant, as they provide a line to model real life, e.g., financial and medical, decisions that show the same empirical patterns as the two-urn experiment. [ABSTRACT FROM AUTHOR]

As stochastic independence is essential to the mathematical development of probability theory, it seems that any foundational work on probability should be able to account for this property. Bayesian decision theory appears to be wanting in this respect. Savage's postulates on preferences under uncertainty entail a subjective expected utility representation, and this asserts only the existence and uniqueness of a subjective probability measure, regardless of its properties. What is missing is a preference condition corresponding to stochastic independence. To fill this significant gap, the article axiomatizes Bayesian decision theory afresh and proves several representation theorems in this novel framework. [ABSTRACT FROM AUTHOR]

Modern theories of decision-making typically model uncertainty about decision options using the tools of probability theory. This is exemplified by the Savage framework, the most popular framework in decision-making research. There, decision-makers are assumed to choose from among available decision options as if they maximized subjective expected utility, which is given by the utilities of outcomes in different states weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using Bayes' Law. The primary concern of the Savage framework is to ensure that decision-makers' choices are rational. Here, we use concepts from computational complexity theory to expose two major weaknesses of the framework. Firstly, we argue that in most situations, subjective utility maximization is computationally intractable, which means that the Savage axioms are implausible. We discuss empirical evidence supporting this claim. Secondly, we argue that there exist many decision situations in which the nature of uncertainty is such that (random) sampling in combination with Bayes' Law is an ineffective strategy to reduce uncertainty. We discuss several implications of these weaknesses from both an empirical and a normative perspective. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'. [ABSTRACT FROM AUTHOR]

Why would people pay more for a $50 gift certificate than for the opportunity to receive a gift certificate worth either $50 or $100, with equal probability? This article examines three possible mechanisms for this recently documented uncertainty effect (UE): First, awareness of the better outcome may devalue the worse one. Second, the UE may have arisen in the original demonstration of this effect because participants misunderstood the instructions. Third, the UE may be due to direct risk aversion, that is, actual distaste for uncertainty. In Experiment 1 , the UE was observed even though participants in the certainty condition were also aware of the better outcome; this result eliminates the first explanation. Experiment 2 shows that most participants understand the instructions used in the original study and that the UE is not caused by the few who do not. Overall, the experiments demonstrate that the UE is robust, large (prospects are valued at 65% of the value of the worse outcome), and widespread (at least 62% of participants exhibit it). [ABSTRACT FROM AUTHOR]

Real decision makers (DM) partially quantify uncertainty as probability uncertainty intervals. Then alternatives are modeled as fuzzy-rational lotteries. Those can be approximated by standard lotteries with point-estimate probabilities, referred as classical-risky. Ranking the approximating classical-risky lotteries is a problem under risk, solved by expected utility. However, the approximation itself is a problem under strict uncertainty. Here this part of the problem is solved by criteria under strict uncertainty, which if not perfectly rational, are well worked descriptive methods with known properties. The proposed Laplace, Wald, maximax and Hurwiczα expected utility criteria for prescriptive ranking of fuzzy-rational lotteries allow the DM to control the approximation of alternatives with partially quantified uncertainty in consistency with her/his degree of belief and with his optimism-pessimism attitude. That makes them superior to the widespread abandoned-m and normalized mean criteria, which often violate the intrinsic subjective probabilities of the DM. The proposed criteria are generalizations of expected utility criterion under risk and of their standard versions under strict uncertainty. [ABSTRACT FROM AUTHOR]

Many quantitative scales are constructed using cutoffs on a continuum with scores assigned to the cutoffs. This paper develops a framework for using or constructing such scales from a decision-making standpoint. It addresses questions such as: How many distinct thresholds or cutoffs on a scale (i.e., what levels of granularity) are useful for a rational agent? Where should these thresholds be placed given a rational agent’s preferences and risk-orientation? Do scale score assignments have any bearing on decision-making and if so, how should scores be assigned? Given two possible states of nature $$\{A, \sim A\}$$ , an ordered collection of alternatives $$\{R_{0}, R_{1},{\ldots}, R_{K}\}$$ from which one is to be selected depending on the probability that A is the case, a simple expected utility condition stipulates when adjacent alternatives are distinguishable and determines the threshold odds separating them. Threshold odds and utilities are mapped onto scale scores via a simple distance model. The placement of the thresholds reflects relative concern over decisional consequences given A versus consequences given ∼ A. Likewise, it is shown that scale scores reflect risk-aversion or risk-seeking not only with respect to A versus ∼ A but also with respect to the rank of the R j . Connections are drawn between this framework and rank-dependent expected utility (RDEU) theory. Implications are adumbrated for both machine and human decision-making. [ABSTRACT FROM AUTHOR]