Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?

Integrated assessment models offer a crucial support to decisionmakers in climate policy making. For a full understanding and corroboration of model results, analysts ought to identify the exogenous variables that influence the model results the most (key drivers), appraise the relevance of interactions, and the direction of change associated with the simultaneous variation of uncertain variables. We show that such information can be directly extracted from the data set produced by Monte Carlo simulations. Our discussion is guided by the application to the well‐known DICE model of William Nordhaus. The proposed methodology allows analysts to draw robust insights into the dependence of future atmospheric temperature, global emissions, and carbon costs and taxes on the model's exogenous variables.     

Identifying treatment effects using trimmed means when data are missing not at random

Patients often discontinue from a clinical trial because their health condition is not improving or they cannot tolerate the assigned treatment. Consequently, the observed clinical outcomes in the trial are likely better on average than if every patient had completed the trial. If these differences between trial completers and non-completers cannot be explained by the observed data, then the study outcomes are missing not at random (MNAR). One way to overcome this problem—the trimmed means approach for missing data due to study discontinuation—sets missing values as the worst observed outcome and then trims away a fraction of the distribution from each treatment arm before calculating differences in treatment efficacy (Permutt T, Li F. Trimmed means for symptom trials with dropouts. Pharm Stat. 2017;16(1):20–28). In this paper, we derive sufficient and necessary conditions for when this approach can identify the average population treatment effect. Simulation studies show the trimmed means approach's ability to effectively estimate treatment efficacy when data are MNAR and missingness due to study discontinuation is strongly associated with an unfavorable outcome, but trimmed means fail when data are missing at random. If the reasons for study discontinuation in a clinical trial are known, analysts can improve estimates with a combination of multiple imputation and the trimmed means approach when the assumptions of each hold. We compare the methodology to existing approaches using data from a clinical trial for chronic pain. An R package trim implements the method. When the assumptions are justifiable, using trimmed means can help identify treatment effects notwithstanding MNAR data.     

The scorpions of the urban habitat of Rome (Italy)

A review of the scorpions (three species of the genus Euscorpius, family Chactidae) found in the urban habitat of Rome, Central Italy, together with an analysis of the factors which affect their distribution and abundance in the town, is pointed out. It appears that Euscorpius carpathicus, a species commonly found on limestone terrains in Latium, is frequent, though not particularly abundant, in the urban area of the city especially along the course of the Tiber River. It appears that Euscorpius flavicaudis, a species commonly found on volcanic terrains in Latium and the most common terrains in and around the area of Rome, is the most common species in both the urban and suburban areas of the city. It appears that Euscorpius italicus, a species common in north and northeastern Latium, is represented, in the sample of the scorpions of the city, by a single, possibly aberrant, specimen.     

Choices Under Ambiguity With Familiar And Unfamiliar Outcomes

This paper considers a decision-making process under ambiguity in which the decision-maker is supposed to split outcomes between familiar and unfamiliar ones. She is assumed to behave differently with respect to unfamiliar gains, unfamiliar losses and customary (familiar) outcomes. In particular, she is supposed to be pessimistic on gains, optimistic on losses and ambiguity neutral on the familiar outcomes. A generalization of the usual Choquet Integral is formalized when the decision maker holds capacities and probabilities. A characterization of the decision-maker’s behavior is provided for a specific subset of capacities, in which it is shown that the decision-maker underestimates the unfamiliar outcomes while is linear in probabilities on customary ones.