Quand l'expertise se heurte au pouvoir souverain : la nation américaine face à la prolifération nucléaire, 1945-1953

After the Second World War, the applications of nuclear science raise a crucial threat to international security as far as their civil applications can be turned into military devices. Debates about nuclear proliferation offer a fruitful site to analyze the production of an expertise in bio-security. This paper analyzes in particular how experts have envisioned the role of nation-states and sovereign power in relation to policies aimed at ensuring the security of populations against the risk of nuclear annihilation. It explores what conceptions of sovereignty different experts constructed; what disciplines (political science, natural science, law) were mobilized in this debate; what strategies of institutionalization American experts followed; what publics were targeted by these experts (officials, think tanks, academic publics, media). Experts' conceptions of threats and insurances as well as the institutional paths they build to access policy circles explain which of the experts' policies diffused among policy circles. This approach, which focuses on the production of ideas rather than on the diffusion of administrative norms, complements neo-institutionalist studies of the internationalization of scientific modes of regulation.     

À propos de l’article de François-Xavier de Vaujany, « Les figures de la gestion du changement sociotechnique »

The approach adopted by François Xavier de Vaujany draws from “structurationist” research in English-speaking social sciences, a current that shares with the sociology of technology a vision of the contextual and negotiated nature of the links between technical and organizational phenomena. Various trajectories in the way organizations use new information and communication technology can be compared; and the role, examined that specific mediators play in the appropriation process. This approach provides stimulating input in discussions about the impact of new technology on organizational change.     

Tests of the causal directions of global — Life facet satisfaction relationships

Previous research generally has supported multiple discrepancies theory (MDT) of the processes by which individuals arrive at judgments of satisfaction with various aspects of their lives as well as with life overall. However, the nature of possible causal relationships between overall and life facet satisfaction has been ignored. The purpose of this study was to extend MDT by testing alternative theoretical models which specified Bottom-up, Top-Down, and Bidirectional relationships between overall life satisfaction and satisfaction with 11 life facets in a sample of 1354 U.S. college students. Results most strongly favored the Bidirectional model in which the overall — life facet satisfaction relationship varied across life domains. The need to identify boundary conditions which determine the direction of the overall — life facet satisfaction relationship is discussed.     

Multivariate elliptical models with general parameterization

In this paper we introduce a general elliptical multivariate regression model in which the mean vector and the scale matrix have parameters (or/and covariates) in common. This approach unifies several important elliptical models, such as nonlinear regressions, mixed-effects model with nonlinear fixed effects, errors-in-variables models, and so forth. We discuss maximum likelihood estimation of the model parameters and obtain the information matrix, both observed and expected. Additionally, we derived the generalized leverage as well as the normal curvatures of local influence under some perturbation schemes. An empirical application is presented for illustrative purposes.     

On coupling particle filter trajectories

Particle filters are a powerful and flexible tool for performing inference on state-space models. They involve a collection of samples evolving over time through a combination of sampling and re-sampling steps. The re-sampling step is necessary to ensure that weight degeneracy is avoided. In several situations of statistical interest, it is important to be able to compare the estimates produced by two different particle filters; consequently, being able to efficiently couple two particle filter trajectories is often of paramount importance. In this text, we propose several ways to do so. In particular, we leverage ideas from the optimal transportation literature. In general, though computing the optimal transport map is extremely computationally expensive, to deal with this, we introduce computationally tractable approximations to optimal transport couplings. We demonstrate that our resulting algorithms for coupling two particle filter trajectories often perform orders of magnitude more efficiently than more standard approaches.     

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes

In randomized clinical trials, methods of pairwise comparisons such as the ‘Net Benefit’ or the ‘win ratio’ have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan–Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.     

Stochasticity in Physiologically Based Kinetics Models: Implications for Cancer Risk Assessment

In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs. We adapted the dynamics equations of a physiologically based pharmacokinetic (PBPK) model and used a stochastic simulator for the system of equations that we derived. We then coupled kinetics simulations with a deterministic hockey stick model of carcinogenicity. Stochasticity induced substantial modifications relative to dose-response curve, compared with the deterministic situation. In particular, there was nonlinearity in the response and the stochastic apparent threshold was lower than the deterministic one. The approach that we developed could easily be extended to other biological studies to assess the influence of stochasticity at macroscopic scale for compound dynamics at the cell level.