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Natural disasters due to climate change (like floods, hurricanes, heat waves or droughts) combine a risk of large losses and a low probability of occurrence, requiring decisions to be made in uncertain universes. However, the inability of standard decision under uncertainty models to provide rankings when some outcomes are catastrophic impedes rational (public) decision-making. This paper examines the role of emotions in individuals’ choices among alternatives involving catastrophic events, either in real life (flooding) or artificial (laboratory experiment) situations. We report a survey on 599 respondents aimed at determining how people exposed to different levels of flood risk form beliefs and make decisions under uncertainty before and after emotion-generating events. Data on their emotions, the emotions they expect to experience, their personality and psychological determinants, their symptoms before and after emotion-generating events are collected and analyzed. In parallel with this survey, experimental protocols replicate the emotional experience of a catastrophe and measure its impact on behavior and formation of beliefs. Emotions are induced by framing effects and measured through a self-declared worry scale. We collect behavioral data (insurance choice, subjective beliefs, performance) and measure how they are affected by the emotions felt during the decision-making. These protocols test some assumptions in the survey using experimental paradigms from psychophysics that allow us to control the sources of uncertainty experienced by the subjects. Results confirm that emotions connected with the nature of the risk can significantly affect desire to reduce it. The survey provides valuable material for comparative analysis, revealing how actual experience of an anticipated event affects decisions. The experiments show that emotions affect the decision-making process and the forming of probabilistic beliefs.

Metacognitive judgments of performance can be retrospective (such as confidence in past choices) or prospective (such as a prediction of success). Several lines of evidence indicate that these two aspects of metacognition are dissociable, suggesting they rely on distinct cues or cognitive resources. However, because prospective and retrospective judgments are often elicited and studied in separate experimental paradigms, their similarities and differences remain unclear. Here we characterize prospective and retrospective judgments of performance in the same perceptual discrimination task using repeated stimuli of constant difficulty. Using an incentive-compatible mechanism for eliciting subjective probabilities, subjects expressed their confidence in past choices together with their predictions of success in future choices. We found distinct influences on each judgment type: retrospective judgments were strongly influenced by the speed and accuracy of the immediately preceding decision, whereas prospective judgments were influenced by previous confidence over a longer time window. In contrast, global levels of confidence were correlated across judgments, indicative of a domain-general overconfidence that transcends temporal focus.

Les probabilités subjectives ont un rôle central dans la prise de décision. Si les modèles théoriques et les données expérimentales sont relativement silencieux en économie sur la façon dont se forment ces croyances lors du processus décisionnel, il n’est pas de même en sciences cognitives. Nous proposons ici une revue de littérature de l’étude de la métacognition au travers de modèles computationnels de détection du signal. Cette métho - dologie est ensuite importée à la décision non perceptive et nous montrons comment son utilisation ouvre de nouvelles pistes de recherche dans l’étude des croyances subjectives en économie expériment

In this paper, we use an experimental design to compare the performance of elicitation rules for subjective beliefs. Contrary to previous works in which elicited beliefs are compared to an objective benchmark, we consider a purely subjective belief framework (confidence in one’s own performance in a cognitive task and a perceptual task). The performance of different elicitation rules is assessed according to the accuracy of stated beliefs in predicting success. We measure this accuracy using two main factors: calibration and discrimination. For each of them, we propose two statistical indexes and we compare the rules’ performances for each measurement. The matching probability method provides more accurate beliefs in terms of discrimination, while the quadratic scoring rule reduces overconfidence and the free rule, a simple rule with no incentives, which succeeds in eliciting accurate beliefs. Nevertheless, the matching probability appears to be the best mechanism for eliciting beliefs due to its performances in terms of calibration and discrimination, but also its ability to elicit consistent beliefs across measures and across tasks, as well as its empirical and theoretical properties.

We compare three alternative methods for eliciting retrospective confidence in the context of a simple perceptual task: the Simple Confidence Rating (a direct report on a numerical scale), the Quadratic Scoring Rule (a post-wagering procedure), and the Matching Probability (MP; a generalization of the no-loss gambling method). We systematically compare the results obtained with these three rules to the theoretical confidence levels that can be inferred from performance in the perceptual task using Signal Detection Theory (SDT). We find that the MP provides better results in that respect. We conclude that MP is particularly well suited for studies of confidence that use SDT as a theoretical framework.

Whether to keep products segregated (e.g., unbundled) or integrate some or all of them (e.g., bundle) has been a problem of profound interest in areas such as portfolio theory in finance, risk capital allocations in insurance and marketing of consumer products. Such decisions are inherently complex and depend on factors such as the underlying product values and consumer preferences, the latter being frequently described using value functions, also known as utility functions in economics. In this paper, we develop decision rules for multiple products, which we generally call ‘exposure units’ to naturally cover manifold scenarios spanning well beyond ‘products’. Our findings show, e.g. that the celebrated Thaler's principles of mental accounting hold as originally postulated when the values of all exposure units are positive (i.e. all are gains) or all negative (i.e. all are losses). In the case of exposure units with mixed-sign values, decision rules are much more complex and rely on cataloging the Bell number of cases that grow very fast depending on the number of exposure units. Consequently, in the present paper, we provide detailed rules for the integration and segregation decisions in the case up to three exposure units, and partial rules for the arbitrary number of units.

Originally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. The choice of an appropriate dissimilarity measure is a major issue when dealing with sequence analysis for life sequences. Several dissimilarities are available in the literature, but neither of them succeeds to become indisputable. In this paper, instead of deciding upon one dissimilarity measure, we propose to use an optimal convex combination of different dissimilarities. The optimality is automatically determined by the clustering procedure and is defined with respect to the within-class variance.

How to Improve Pupils' Literacy? A Cost-Effectiveness Analysis of the Action Lecture This article presents a cost-effectiveness analysis of an innovative teaching method run in some nursery and primary schools in Paris. This project, named Action Lecture, is designed to improve pupils' abilities and taste on literacy. We report the results of an evaluation of this program. We describe the experimental protocol that was built to estimate the program's impact on several types of indicators (academic abilities, attitude toward reading, school life) by comparing the evolution of treatment schools and control schools. Data were processed following a Differences-in-Differences (DID) method. Then we use the estimation of the impact on academic achievement to conduct a cost-effectiveness analysis and take a reduction of the class size program as a benchmark. The results are positive for the Action Lecture program both in terms of impact's evaluation and in terms of cost-effectiveness ratio.

This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important drawback since it makes a linear assumption on the data. We propose a new methodology, inspired from biology and adapted to career paths, combining optimal matching and self-organizing maps. A complete study on real-life data will illustrate our proposal.