The fundamental properties, stability and predictive power of distributional preferences

Parsimony is a desirable feature of economic models but almost all human behaviors are characterized by vast individual variation that appears to defy parsimony. How much parsimony do we need to give up to capture the fundamental aspects of a population’s distributional preferences and to maintain high predictive ability? Using a Bayesian nonparametric clustering method that makes the trade-off between parsimony and descriptive accuracy explicit, we show that three preference types—an inequality averse, an altruistic and a predominantly selfish type—capture the essence of behavioral heterogeneity. These types independently emerge in four different data sets and are strikingly stable over time. They predict out-of-sample behavior equally well as a model that permits all individuals to differ and substantially better than a representative agent model and a state-of-the-art machine learning algorithm. Thus, a parsimonious model with three stable types captures key characteristics of distributional preferences and has excellent predictive power

The missing type: where are the inequality averse (students)?

The empirical evidence on the existence of social preferences—or lack thereof—is predominantly based on student samples. Yet, knowledge about whether these findings can be extended to the general population is still scarce. In this paper, we compare the distribution of social preferences in a student and in a representative sample. Using descriptive analysis and a rigorous clustering approach, we show that the distribution of the general population’s social preferences fundamentally differs from the students’ distribution. In the general population, three types emerge: an inequality averse, an altruistic, and a selfish type. In contrast, only the altruistic and the selfish types emerge in the student population. The absence of an inequality averse type in the student population is particularly striking considering the fact that this type comprises about 50 percent of the individuals in the general population sample. Using structural estimation, we show that differences in age and education are likely to explain these results. Younger and more educated individuals—which typically characterize students— not only tend to have lower degrees of other-regardingness but this reduction in other-regardingness basically nullifies behindness aversion among students. Differences in income, however, do not seem to affect social preferences. These findings provide a new cautionary tale that insights from student populations might not extrapolate to the general population

Beliefs about inequality and the nature of support for redistribution

Do beliefs about inequality depend on distributive preferences? What is the joint role of preferences and beliefs about inequality for support for redistribution? We study these questions in a staggered experiment with a representative sample of the Swiss population conducted in the context of a vote on a highly redistributive policy proposal. Our sample comprises a majority of inequality averse subjects, a sizeable group of altruistic subjects, and a minority of predominantly selfish subjects. Irrespective of preference types, individuals vastly overestimate the extent of income inequality. An information intervention successfully corrects these large misperceptions for all types, but essentially does not affect aggregate support for redistribution. These results hide, however, important heterogeneity because the effects of beliefs about inequality for demand for redistribution are preference-dependent: only affluent inequality averse individuals, but not the selfish and altruistic ones, significantly reduce their support for redistribution. These findings cast a new light on the seemingly puzzling result that, in the aggregate, large changes in beliefs about inequality often do not translate into changes in demand for redistribution

Vaccine nationalism counterintuitively erodes public trust in leaders

Global access to resources like vaccines is key for containing the spread of infectious diseases. However, wealthy countries often pursue nationalistic policies, stockpiling doses rather than redistributing them globally. One possible motivation behind vaccine nationalism is a belief among policymakers that citizens will mistrust leaders who prioritize global needs over domestic protection. In seven experiments (total N = 4,215 adults), we demonstrate that such concerns are misplaced: Nationally representative samples across multiple countries with large vaccine surpluses (Australia, Canada, United Kingdom, and United States) trusted redistributive leaders more than nationalistic leaders—even the more nationalistic participants. This preference generalized across different diseases and manifested in both self-reported and behavioral measures of trust. Professional civil servants, however, had the opposite intuition and predicted higher trust in nationalistic leaders, and a nonexpert sample also failed to predict higher trust in redistributive leaders. We discuss how policymakers’ inaccurate intuitions might originate from overestimating others’ self-interest

Contraintes mémoire et solution architecturale pour applications TDSI

- Les systèmes supportant des applications de traitement du signal et de l'image manipulent de plus en plus de données. Cela entraîne une utilisation intensive de la mémoire qui devient le point critique du système ; la mémoire limite les performances et représente une proportion importante de la consommation globale. Dans le cadre du projet RNRT ALIPTA nous développons l'outil de synthèse d'architecture GAUT en nous intéressant à la synthèse de la partie mémoire. Nous évaluerons l'impact de contraintes de mémorisation sur les architectures pour différentes applications en traitement du signal et de l'image

Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks

Brains need to deal with an uncertain world. Often, this requires visiting multiple interpretations of the available information or multiple solutions to an encountered problem. This gives rise to the so-called mixing problem: since all of these "valid" states represent powerful attractors, but between themselves can be very dissimilar, switching between such states can be difficult. We propose that cortical oscillations can be effectively used to overcome this challenge. By acting as an effective temperature, background spiking activity modulates exploration. Rhythmic changes induced by cortical oscillations can then be interpreted as a form of simulated tempering. We provide a rigorous mathematical discussion of this link and study some of its phenomenological implications in computer simulations. This identifies a new computational role of cortical oscillations and connects them to various phenomena in the brain, such as sampling-based probabilistic inference, memory replay, multisensory cue combination and place cell flickering.Comment: 30 pages, 11 figure

20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years

The administration of endocrine therapy for 5 years substantially reduces recurrence rates during and after treatment in women with early-stage, estrogen-receptor (ER)-positive breast cancer. Extending such therapy beyond 5 years offers further protection but has additional side effects. Obtaining data on the absolute risk of subsequent distant recurrence if therapy stops at 5 years could help determine whether to extend treatment