During early life-sensitive periods (i.e., fetal, infancy), the developing stress response system adaptively calibrates to match environmental conditions, whether harsh or supportive. Recent evidence suggests that puberty is another window when the stress system is open to recalibration if environmental conditions have shifted significantly. Whether additional periods of recalibration exist in adulthood remains to be established. The present paper draws parallels between childhood (re)calibration periods and the perinatal period to hypothesize that this phase may be an additional window of stress recalibration in adult life. Specifically, the perinatal period (defined here to include pregnancy, lactation, and early parenthood) is also a developmental switch point characterized by heightened neural plasticity and marked changes in stress system function. After discussing these similarities, lines of empirical evidence needed to substantiate the perinatal stress recalibration hypothesis are proposed, and existing research support is reviewed. Complexities and challenges related to delineating the boundaries of perinatal stress recalibration and empirically testing this hypothesis are discussed, as well as possibilities for future multidisciplinary research. In the theme of this special issue, perinatal stress recalibration may be a mechanism of multilevel, multisystem risk, and resilience, both intra-individually and intergenerationally, with implications for optimizing interventions.

The output of predictive models is routinely recalibrated by reconciling low-level predictions with known quantities defined at higher levels of aggregation. For example, models predicting vote probabilities at the individual level in U.S. elections can be adjusted so that their aggregation matches the observed vote totals in each county, thus producing better-calibrated predictions. In this research note, we provide theoretical grounding for one of the most commonly used recalibration strategies, known colloquially as the “logit shift.” Typically cast as a heuristic adjustment strategy (whereby a constant correction on the logit scale is found, such that aggregated predictions match target totals), we show that the logit shift offers a fast and accurate approximation to a principled, but computationally impractical adjustment strategy: computing the posterior prediction probabilities, conditional on the observed totals. After deriving analytical bounds on the quality of the approximation, we illustrate its accuracy using Monte Carlo simulations. We also discuss scenarios in which the logit shift is less effective at recalibrating predictions: when the target totals are defined only for highly heterogeneous populations, and when the original predictions correctly capture the mean of true individual probabilities, but fail to capture the shape of their distribution.

The accuracy of human forecasters is often reduced because of incompleteinformation and cognitive biases that affect the judges. One approach to improvethe accuracy of the forecasts is to recalibrate them by means of non-lineartransformations that are sensitive to the direction and the magnitude of thebiases. Previous work on recalibration has focused on binary forecasts. Wepropose an extension of this approach by developing an algorithm that uses asingle free parameter to recalibrate complete subjective probabilitydistributions. We illustrate the approach with data from the quarterly Survey ofProfessional Forecasters (SPF) conducted by the European Central Bank (ECB),document the potential benefits of this approach, and show how it can be used inpractical applications.

A routine part of intelligence analysis is judging the probability of alternative hypotheses given available evidence. Intelligence organizations advise analysts to use intelligence-tradecraft methods such as Analysis of Competing Hypotheses (ACH) to improve judgment, but such methods have not been rigorously tested. We compared the evidence evaluation and judgment accuracy of a group of intelligence analysts who were recently trained in ACH and then used it on a probability judgment task to another group of analysts from the same cohort that were neither trained in ACH nor asked to use any specific method. Although the ACH group assessed information usefulness better than the control group, the control group was a little more accurate (and coherent) than the ACH group. Both groups, however, exhibited suboptimal judgment and were susceptible to unpacking effects. Although ACH failed to improve accuracy, we found that recalibration and aggregation methods substantially improved accuracy. Specifically, mean absolute error (MAE) in analysts’ probability judgments decreased by 61% after first coherentizing their judgments (a process that ensures judgments respect the unitarity axiom) and then aggregating their judgments. The findings cast doubt on the efficacy of ACH, and show the promise of statistical methods for boosting judgment quality in intelligence and other organizations that routinely produce expert judgments.