During product development and early real-world use new scientific evidence emerges and knowledge about the benefits and risks of medical products evolves. In rapidly changing settings, it is important to understand if stated preferences are robust to available knowledge and changes in attribute specification. During COVID-19 vaccine development, new information emerged about vaccine safety and duration of protection. We examine this issue using two discrete choice experiment (DCE) specifications fielded in France. In December 2020, a representative sample of 6007 adults aged 18-64 years was randomly allocated to either a RESTRICTED specification (n = 1201), which replicated an earlier June 2020 DCE study design, or a more complete REFERENCE specification (n = 4806) that included a booster-related attribute and expanded the serious side-effect risk attribute levels. We test whether the RESTRICTED specification affects the relative importance of common attributes and the consistency of respondents' choices. Relative attribute importance was assessed using partial log-likelihood measures, and choice consistency was examined using heteroskedastic mixed logit and latent class models. We find the RESTRICTED specification does not significantly change the relative importance ranking of the common attributes, but it is associated with higher choice consistency. Our results suggest that DCE results may be robust to attribute omission and level simplification in terms of attribute importance ranking, but that richer and more realistic specifications can increase decision noise. The results suggest the need to balance informational completeness against respondent burden when designing DCEs for evolving health technologies.

Research on cognitive effort has produced a vast scientific literature. In this paper, we propose a roadmap to describe key developments over the past 70 years. We highlight a shift from a research landscape dominated by questioning “What happens when we engage in effort?” to “Why we engage in effort?” The first question generates a research framework that we call the arousal framework, where effort is seen as a state of increased physiological activation. Research within this framework has flourished following the discovery of the ascending reticular activating system and has focused on the role of the locus coeruleus-noradrenargic pathway in modulating the intensity of cognitive control. The second question has been central to research driven by neuroeconomics and advances in neuroimaging and computational neuroscience, leading to what we call the valence framework. In this perspective, it is assumed that effort is inherently aversive, and that individuals engage in effort only if the benefits outweigh the costs. Within this framework, research seeks the neural correlates of expected value of cognitive control, with a spotlight on basal ganglia-prefrontal dopaminergic pathways. We describe exemplar work pertaining to each framework, highlighting their key epistemological features. We argue that future research on cognitive effort should aim to understand the complementarity and interactive nature of valence and arousal, integrating the two questions of “what happens” and “why.” We also argue that understanding effort requires looking more specifically into how individuals execute cognitively demanding tasks, i.e., which solution strategies they adopt.

Most studies assessing animal decision-making under risk rely on probabilities that are typically larger than 10%. To study Decision-Making in uncertain conditions, we explore a novel experimental and modelling approach that aims at measuring the extent to which rats are sensitive - and how they respond - to outcomes that are both rare (probabilities smaller than 1%) and extreme in their consequences (deviations larger than 10 times the standard error). In a four-armed bandit task, stochastic gains (sugar pellets) and losses (time-out punishments) are such that extremely large - but rare - outcomes materialize or not depending on the chosen options. All rats feature both limited diversification, mixing two options out of four, and sensitivity to rare and extreme outcomes despite their infrequent occurrence, by combining options with avoidance of extreme losses (Black Swans) and exposure to extreme gains (Jackpots). Notably, this sensitivity turns out to be one-sided for the main phenotype in our sample: it features a quasi-complete avoidance of Black Swans, so as to escape extreme losses almost completely, which contrasts with an exposure to Jackpots that is partial only. The flip side of observed choices is that they entail smaller gains and larger losses in the frequent domain compared to alternatives. We have introduced sensitivity to Black Swans and Jackpots in a new class of augmented Reinforcement Learning models and we have estimated their parameters using observed choices and outcomes for each rat. Adding such specific sensitivity results in a good fit of the selected model - and simulated behaviors that are close - to behavioral observations, whereas a standard Q-Learning model without sensitivity is rejected for almost all rats. This model reproducing the main phenotype suggests that frequent outcomes are treated separately from rare and extreme ones through different weights in Decision-Making.

BACKGROUND:It is commonly believed that Africa largely evaded the worst of the COVID-19 pandemic, with fewer cases than other continents. However, regional comparisons that ignore differences in testing intensity may misrepresent dynamics. Studying the spread and case-fatality relationship during COVID-19 across WHO regions requires explicitly adjusting for time-varying test volumes. METHODS:We build a weekly panel dataset spanning May 2020 to December 2021 for the WHO regions: Africa, Eastern Mediterranean, South-East Asia, the Americas, Western Pacific, and Europe. Data on tests, confirmed cases, and COVID-19-attributed deaths were sourced from Our World in Data. We apply a novel metric that corrects for fluctuating test volumes to quantify week-to-week acceleration in infections and in mortality. We then compare the frequency, magnitude, and timing of these acceleration episodes across regions. RESULTS:Accounting for testing dynamics, we show that Africa exhibits multiple infection-acceleration episodes whose magnitude and frequency match those in other regions. Mortality accelerations in Africa closely follow infection surges, with an average lag of ten weeks. A positive correlation between infection acceleration in Africa and the Americas further indicates synchrony. These findings hold when using a larger secondary dataset of 140 countries. CONCLUSIONS:Contrary to prevailing assumptions, Africa was not spared from the pandemic's severe dynamics. Infection surges were on par with those elsewhere and were followed by mortality accelerations. These results underscore that accounting for testing variability is essential to accurately assess pandemic progression, and they highlight the urgent need to strengthen surveillance and healthcare capacity across all regions.

Under incomplete contracts, the mutual belief in reciprocity facilitates how traders create value through economic exchange. Creating such beliefs among strangers can be challenging even when they are allowed to communicate, because communication is cheap. In this paper, we first extend the literature showing that a truth-telling oath increases honesty to a sequential trust game with pre-play, fixed-form, and cheap-talk communication. Our results confirm that the oath creates more trust and cooperative behavior thanks to an improvement in communication; but we also show that the oath induces selection into communication — it makes people more wary of using communication, precisely because communication speaks louder under oath. We next designed additional treatments featuring mild and deterrent fines for deception to measure the monetary equivalent of the non-monetary incentives implemented by a truth-telling oath. We find that the oath is behaviorally equivalent to mild fines. The deterrent fine induces the highest level of cooperation. Altogether, these results confirm that allowing for interactions under oath within a trust game with communication creates significantly more economic value than the identical exchange institutions without the oath.

We provide a novel way to correct the effective reproduction number for the time-varying amount of tests, using the acceleration index (Baunez et al., 2021) as a simple measure of viral spread dynamics. Not correcting results in the reproduction number being a biased estimate of viral acceleration and we provide a formal decomposition of the resulting bias, involving the useful notions of test and infectivity intensities. When applied to French data for the COVID-19 pandemic (May 13, 2020-October 26, 2022), our decomposition shows that the reproduction number, when considered alone, characteristically underestimates the resurgence of the pandemic, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all relevant information and captures in real time the sizable time variation featured by viral circulation, it is a more parsimonious indicator to track the dynamics of an infectious disease outbreak in real time, compared to the equivalent alternative which would combine the reproduction number with the test and infectivity intensities.

Background Meta-analyses have shown that preexisting mental disorders may increase serious Coronavirus Disease 2019 (COVID-19) outcomes, especially mortality. However, most studies were conducted during the first months of the pandemic, were inconclusive for several categories of mental disorders, and not fully controlled for potential confounders. Our study objectives were to assess independent associations between various categories of mental disorders and COVID-19-related mortality in a nationwide sample of COVID-19 inpatients discharged over 18 months and the potential role of salvage therapy triage to explain these associations. Methods and findings We analysed a nationwide retrospective cohort of all adult inpatients discharged with symptomatic COVID-19 between February 24, 2020 and August 28, 2021 in mainland France. The primary exposure was preexisting mental disorders assessed from all discharge information recorded over the last 9 years (dementia, depression, anxiety disorders, schizophrenia, alcohol use disorders, opioid use disorders, Down syndrome, other learning disabilities, and other disorder requiring psychiatric ward admission). The main outcomes were all-cause mortality and access to salvage therapy (intensive-care unit admission or life-saving respiratory support) assessed at 120 days after recorded COVID-19 diagnosis at hospital. Independent associations were analysed in multivariate logistic models. Of 465,750 inpatients with symptomatic COVID-19, 153,870 (33.0%) were recorded with a history of mental disorders. Almost all categories of mental disorders were independently associated with higher mortality risks (except opioid use disorders) and lower salvage therapy rates (except opioid use disorders and Down syndrome). After taking into account the mortality risk predicted at baseline from patient vulnerability (including older age and severe somatic comorbidities), excess mortality risks due to caseload surges in hospitals were +5.0% (95% confidence interval (CI), 4.7 to 5.2) in patients without mental disorders (for a predicted risk of 13.3% [95% CI, 13.2 to 13.4] at baseline) and significantly higher in patients with mental disorders (+9.3% [95% CI, 8.9 to 9.8] for a predicted risk of 21.2% [95% CI, 21.0 to 21.4] at baseline). In contrast, salvage therapy rates during caseload surges in hospitals were significantly higher than expected in patients without mental disorders (+4.2% [95% CI, 3.8 to 4.5]) and lower in patients with mental disorders (−4.1% [95% CI, −4.4; −3.7]) for predicted rates similar at baseline (18.8% [95% CI, 18.7-18.9] and 18.0% [95% CI, 17.9-18.2], respectively). The main limitations of our study point to the assessment of COVID-19-related mortality at 120 days and potential coding bias of medical information recorded in hospital claims data, although the main study findings were consistently reproduced in multiple sensitivity analyses. Conclusions COVID-19 patients with mental disorders had lower odds of accessing salvage therapy, suggesting that life-saving measures at French hospitals were disproportionately denied to patients with mental disorders in this exceptional context.

Public good games are at the core of many environmental challenges. In such social dilemmas, a large share of people endorse the norm of reciprocity. A growing literature complements this finding with the observation that many players exhibit a self-serving bias in reciprocation: “weak reciprocators” increase their contributions as a function of the effort level of the other players, but less than proportionally. In this paper, we build upon a growing literature on truth-telling to argue that weak reciprocity might be best conceived not as a preference, but rather as a symptom of an internal trade-off at the player level between (i) the truthful revelation of their private reciprocal preference, and (ii) the economic incentives they face (which foster free-riding). In truth-telling experiments, many players misrepresent private information when this is to their material benefit, but to a significantly lesser extent than what would be expected based on the profit-maximizing strategy. We apply this behavioral insight to strategic situations, and test whether the preference revelation properties of the classic voluntary contribution game can be improved by offering players the possibility to sign a classic truth-telling oath. Our results suggest that the honesty oath helps increase cooperation (by 33% in our experiment). Subjects under oath contribute in a way which is more consistent with (i) the contribution they expect from the other players and (ii) their normative views about the right contribution level. As a result, the distribution of social types elicited under oath differs from the one observed in the baseline: some free-riders, and many weak reciprocators, now behave as pure reciprocators.

We propose a structural econometric model that incorporates altruism towards other household members into the willingness to pay for a public good. The model distinguishes preferences for public good improvements for oneself from preferences for improvements for other household members. We test for three different types of altruism - ‘pure self-interest’, ‘pure altruism’ and ‘public-good-focused non-pure altruism’. Using French contingent valuation data regarding air quality improvements, we find positive and significant degrees of concern for children under the age of 18, which are explained by determinants related to health and subjective air quality assessment. All other forms of pure or air-quality-focused altruism within the family are insignificant, including for children over 18, siblings, spouses, and parents. This result suggests that benefit estimates that do not consider altruism could undervalue improvements in air quality in France.

In a competitive business environment, dishonesty can pay. Self-interested executives and managers can have incentive to shade the truth for personal gain. In response, the business community has considered how to commit these executives and managers to a higher ethical standard. The MBA Oath and the Dutch Bankers Oath are examples of such a commitment device. The question we test herein is whether the oath can be used as an effective form of ethics management for future executives/managers—who for our experiment we recruited from a leading French business school—by actually improving their honesty. Using a classic Sender-Receiver strategic game experiment, we reinforce professional identity by pre-selecting the group to which Receivers belong. This allows us to determine whether taking the oath deters lying among future managers. Our results suggest “yes and no.” We observe that these future executives/managers who took a solemn honesty oath as a Sender were (a) significantly more likely to tell the truth when the lie was detrimental to the Receiver, but (b) were not more likely to tell the truth when the lie was mutually beneficial to both the Sender and Receiver. A joint product of our design is our ability to measure in-group bias in lying behavior in our population of subjects (comparing behavior of subjects in the same and different business schools). The experiment provides clear evidence of a lack of such bias.

Most studies assessing animal decision-making under risk rely on probabilities that are typically larger than 10%. To study Decision-Making in uncertain conditions, we explore a novel experimental and modelling approach that aims at measuring the extent to which rats are sensitive - and how they respond - to outcomes that are both rare (probabilities smaller than 1%) and extreme in their consequences (deviations larger than 10 times the standard error). In a four-armed bandit task, stochastic gains (sugar pellets) and losses (time-out punishments) are such that extremely large - but rare - outcomes materialize or not depending on the chosen options. All rats feature both limited diversification, mixing two options out of four, and sensitivity to rare and extreme outcomes despite their infrequent occurrence, by combining options with avoidance of extreme losses (Black Swans) and exposure to extreme gains (Jackpots). Notably, this sensitivity turns out to be one-sided for the main phenotype in our sample: it features a quasi-complete avoidance of Black Swans, so as to escape extreme losses almost completely, which contrasts with an exposure to Jackpots that is partial only. The flip side of observed choices is that they entail smaller gains and larger losses in the frequent domain compared to alternatives. We have introduced sensitivity to Black Swans and Jackpots in a new class of augmented Reinforcement Learning models and we have estimated their parameters using observed choices and outcomes for each rat. Adding such specific sensitivity results in a good fit of the selected model - and simulated behaviors that are close - to behavioral observations, whereas a standard Q-Learning model without sensitivity is rejected for almost all rats. This model reproducing the main phenotype suggests that frequent outcomes are treated separately from rare and extreme ones through different weights in Decision-Making.

We study time preferences by means of a longitudinal lab experiment involving both monetary and non-monetary rewards (leisure). Our novel design allows to measure whether participants prefer to anticipate or delay gratification, without imposing any structural assumption on the instantaneous utility, intertemporal utility or the discounting functions. We find that most people prefer to anticipate monetary rewards (positive time preferences for money), but they delay non-monetary rewards (negative time preferences for leisure). These results cannot be explained by personal timetables and heterogeneous preferences only. They invite to reconsider the psychological interpretation of the discount factor, and suggest that the assumption that discounting is consistent across domains can lead to non-negligible prediction errors in models involving non-monetary decisions, such as labor supply models.

The COVID-19 pandemic is a major global societal, economic and health threat. The availability of COVID-19 vaccines has raised hopes for a decline in the pandemic. We built upon a stochastic agent-based microsimulation model of the COVID-19 epidemic in France. We examined the potential impact of different vaccination strategies, defined according to the age, medical conditions, and expected vaccination acceptance of the target non-immunized adult population, on disease cumulative incidence, mortality, and number of hospital admissions. Specifically, we examined whether these vaccination strategies would allow to lift all non-pharmacological interventions (NPIs), based on a sufficiently low cumulative mortality and number of hospital admissions. While vaccinating the full adult non-immunized population, if performed immediately, would be highly effective in reducing incidence, mortality and hospital-bed occupancy, and would allow discontinuing all NPIs, this strategy would require a large number of vaccine doses. Vaccinating only adults at higher risk for severe SARS-CoV-2 infection, i.e. those aged over 65 years or with medical conditions, would be insufficient to lift NPIs. Immediately vaccinating only adults aged over 45 years, or only adults aged over 55 years with mandatory vaccination of those aged over 65 years, would enable lifting all NPIs with a substantially lower number of vaccine doses, particularly with the latter vaccination strategy. Benefits of these strategies would be markedly reduced if the vaccination was delayed, was less effective than expected on virus transmission or in preventing COVID-19 among older adults, or was not widely accepted.

It can be assumed that higher SARS-CoV-2 infection risk is associated with higher COVID-19 vaccination intentions, although evidence is scarce. In this large and representative survey of 6007 adults aged 18–64 years and residing in France, 8.1% (95% CI, 7.5–8.8) reported a prior SARS-CoV-2 infection in December 2020, with regional variations according to an East–West gradient (p < 0.0001). In participants without prior SARS-CoV-2 infection, COVID-19 vaccine hesitancy was substantial, including 41.3% (95% CI, 39.8–42.8) outright refusal of COVID-19 vaccination. Taking into account five characteristics of the first approved vaccines (efficacy, duration of immunity, safety, country of the vaccine manufacturer, and place of administration) as well as the initial setting of the mass vaccination campaign in France, COVID-19 vaccine acceptance would reach 43.6% (95% CI, 43.0–44.1) at best among working-age adults without prior SARS-CoV-2 infection. COVID-19 vaccine acceptance was primarily driven by vaccine characteristics, sociodemographic and attitudinal factors. Considering the region of residency as a proxy of the likelihood of getting infected, our study findings do not support the assumption that SARS-CoV-2 infection risk is associated with COVID-19 vaccine acceptance.

Even though much has been learned about the new pathogen SARS-CoV-2 since the beginning of the COVID-19 pandemic, a lot of uncertainty remains. In this paper we argue that what is important to know under uncertainty is whether harm accelerates and whether health policies achieve deceleration of harm. For this, we need to see cases in relation to diagnostic effort and not to look at indicators based on cases only, such as a number of widely used epidemiological indicators, including the reproduction number, do. To do so overlooks a crucial dimension, namely the fact that the best we can know about cases will depend on some welldefined strategy of diagnostic effort, such as testing in the case of COVID-19. We will present a newly developed indicator to observe harm, the acceleration index, which is essentially an elasticity of cases in relation to tests. We will discuss what efficiency of testing means and propose that the corresponding health policy goal should be to find ever fewer cases with an ever-greater diagnostic effort. Easy and low-threshold testing will also be a means to give back people’s sovereignty to lead their life in an “open” as opposed to “locked-down” society.

An acceleration index is proposed as a novel indicator to track the dynamics of COVID-19 in real-time. Using data on cases and tests in France for the period between the first and second lock-downs-May 13 to October 25, 2020-our acceleration index shows that the pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic acceleration was stronger than national average for the [59-68] and especially the 69 and older age groups since early September, the latter being associated with the strongest acceleration index, as of October 25. In contrast, acceleration among the [19-28] age group was the lowest and is about half that of the [69-78]. In addition, we propose an algorithm to allocate tests among French "départements" (roughly counties), based on both the acceleration index and the feedback effect of testing. Our acceleration-based allocation differs from the actual distribution over French territories, which is population-based. We argue that both our acceleration index and our allocation algorithm are useful tools to guide public health policies as France might possibly enter a third lock-down period with indeterminate duration.

Does giving taxpayers a voice over the destination of tax revenues lead to more honest income declarations? Previous experiments have shown that giving participants the opportunity to select the organization that receives their tax funds tends to increase tax compliance. The aim of this paper is to assess whether this increase in compliance is induced by the sole fact of giving subjects a choice—a "direct democracy effect". To that aim, we ask participants to a tax evasion game to choose, in a collective or individual choice setting, between two very similar organizations which provide the same social (ecological) benefits. We elicit compliance for both organizations before the choice is made so as to control for the counter-factual compliance decision. We find that democracy does not increase compliance, and even observe a slight negative effect—in particular for women. Our results confirm the existence of a commitment effect of democracy, leading to favor more the selected organization when it was actively chosen. The commitment effect of democracy is however not enough to overcome the decrease in the level of compliance. Thanks to response times data, we show that prior choice on similar options as compared to a purely random selection weakens the preference for honesty. One important field application of our results is that democracy in tax spending must offer real choices to tax payers to improve compliance.