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Cost–Benefit Analysis (CBA) was developed to assess the net socioeconomic benefits of a wide variety of projects in many fields. In this context, it is relevant to investigate how this method is actually used for project evaluation, and whether its merits and limitations are properly understood by a wider community of economists. In this study, we showcase a debate that took place in Italy in 2019 about an important high-speed rail project, following the publication of a CBA that received much criticism. To learn from this episode, we find it useful to set up a meta-model of CBA that allows the formalisation of a large number of CBA calculations (including potentially ill-founded calculations) and to verify their validity. With this meta model, we review the criticisms formulated during the 2019 CBA debate focusing on two salient topics; whether CBA should include taxation and whether the Rule-of-Half measure of users’ surplus is valid. Our analysis suggests: (1) That the proposed meta-equation can help in structuring the scientific debate regarding CBA and the relevant economic discussion about a given project; (2) with few exceptions, the criticisms formulated regarding the 2019 CBA on these topics were incorrect, mostly incoherent, also from an axiomatic point of view. This indicates that ill-founded methods are at risk of becoming well-accepted in the larger community of economists, with the risk of lowering the general quality of policy recommendations they can formulate. This underlines the need for economists to revise the misguided views of CBA.

Distinguishability and, by extension, observability are key properties of dynamical systems. Establishing these properties is challenging, especially when no analytical model is available and they are to be inferred directly from measurement data. The presence of noise further complicates this analysis, as standard notions of distinguishability are tailored to deterministic systems. In this article, we build on distributional distinguishability, which extends the deterministic notion by comparing distributions of outputs of stochastic systems. We first show that both concepts are equivalent for a class of systems that includes linear systems. We then present a method to assess and quantify distributional distinguishability from output data. Specifically, our quantification measures how much data is required to tell apart two initial states, inducing a continuous spectrum of distinguishability. We propose a statistical test to determine a threshold above which two states can be considered distinguishable with high confidence. We illustrate these tools by computing distinguishability maps over the state space in simulation, then leverage the test to compare sensor configurations on hardware.

ObjectivesIn managing patients with cancer in the COVID-19 era, clinical oncologists and palliative care practitioners had to face new, disrupting and complex medical situations, challenging the quality of the shared decision-making process. During the first lockdown in France, we developed an onco-palliative ethics meeting to enhance the quality of the decision-making process for patients with advanced cancer treated for COVID-19.MethodsA least one of the institutional ethics committee members was present along with oncologists, palliative care teams, psycho-oncologists, radiologists and intensive care specialists. Specific medical parameters were systematically collected to form a standardised framework for the discussions.ResultsThe main raised issues were the definition of new criteria for the implementation of invasive resuscitation techniques, optimal ways to adapt or delay anticancer treatment and best procedures to address terminal respiratory failure and end-of-life care. The main clinical and ethical guidelines that emerged during these debates are presented. The palliative care team played a major role in assessing and reporting patients’ awareness of cancer-related prognosis and their wishes concerning invasive therapies or transfer to intensive care units, enabling an individualised benefit–risk balance assessment. The ethics committee members ensured continuous monitoring during the discussions. Their function was to recall the main ethical principles including dignity, which is conferred on people when there are treated as having equal status.ConclusionsThe onco-palliative ethics meeting provided a powerful avenue for improvement of collegiality and reinforcement of teamwork, which could be a major protection against burnout for healthcare professionals facing an epidemic onslaught.

Le développement des parcours de soins de support se heurte à l’importance des besoins et aux difficultés de financement des moyens nécessaires. Les nouveaux outils numériques de monitoring des symptômes représentent une voie d’avenir, dont le développement est favorisé en France par la mise en place d’un cadre réglementaire adapté. Ces outils permettent le recueil des plaintes symptomatiques par les patients en évitant le prisme déformant des soignants, et l’accès à distance en temps réels à ces données. Les principales études cliniques randomisées évaluant leur utilisation ont clairement montré leur impact favorable sur la qualité de vie et la satisfaction des patients atteints de cancer, en particulier en phase avancée. Il montre également un bénéfice sur la réduction du recours aux urgences, l’augmentation de la dose intensité des traitements oncologiques effectivement reçus et sur la survie globale. Néanmoins des barrières persistent quant à leur déploiement ; les contraintes liées à l’absence de capacités de certains patients (âgés, handicapés, défavorisés) à l’utilisation de ces outils ; l’insuffisance d’impulsion politique des dirigeants hospitaliers ; les nombreuses interrogations persistantes sur les modalités d’implémentation, nécessitant la poursuite des études cliniques dans ce domaine.

Safety constraints and optimality are important but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to maintain formal guarantees, it is also common practice in reinforcement learning (RL) to simply modify reward functions by penalizing failures, with the penalty treated as a mere heuristic. We rigorously examine the relationship of both safety and optimality to penalties, and formalize sufficient conditions for safe value functions (SVFs): value functions that are both optimal for a given task, and enforce safety constraints. We reveal this structure by examining when rewards preserve viability under optimal control, and show that there always exists a finite penalty that induces an SVF. This penalty is not unique, but upper-unbounded: larger penalties do not harm optimality. Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact. This insight suggests practical, theory-guided heuristics to design reward functions for control problems where safety is important.