Overconfidence plays a role in a large number of individual decision biases and has been considered a ‘meta-bias’ for this reason. However, since overconfidence is measured behaviorally with respect to particular tasks (in which performance varies across individuals), it is unclear whether people generally vary in terms of their general overconfidence. We investigated this issue using a novel measure: the Generalized Overconfidence Task (GOT). The GOT is a difficult perception test that asks participants to identify objects in fuzzy (‘adversarial’) images. Critically, participants’ estimated performance on the task is not related to their actual performance. Instead, variation in estimated performance, we argue, arises from generalized overconfidence, that is, people indicating a cognitive skill for which they have no basis. In a series of studies (total N = 1,293), the GOT was more predictive when looking at a broad range of behavioral outcomes than two other overestimation tasks (cognitive and numeracy) and did not display substantial overlap with conceptually related measures (Studies 1a and 1b). In Studies 2a and 2b, the GOT showed superior reliability in a test–retest design compared to the other overconfidence measures (i.e., cognitive and numeracy measures), particularly when collecting confidence ratings after each image and an estimated performance score. Finally, the GOT is a strong predictor of a host of behavioral outcomes, including conspiracy beliefs, bullshit receptivity, overclaiming, and the ability to discern news headlines.

Emotion recognition in conversation (ERC) faces two major challenges: biased predictions and poor calibration. Classifiers often disproportionately favor certain emotion categories, such as neutral, due to the structural complexity of classifiers, the subjective nature of emotions, and imbalances in training datasets. This bias results in poorly calibrated predictions where the model’s predicted probabilities do not align with the true likelihood of outcomes. To tackle these problems, we introduce the application of conformal prediction (CP) into ERC tasks. CP is a distribution-free method that generates set-valued predictions to ensure marginal coverage in classification, thus improving the calibration of models. However, inherent biases in emotion recognition models prevent baseline CP from achieving a uniform conditional coverage across all classes. We propose a novel CP variant, class spectrum conformation, which significantly reduces coverage bias in CP methods. The methodologies introduced in this study enhance the reliability of prediction calibration and mitigate bias in complex natural language processing tasks.

This study suggests that there may be considerable difficulties in providing accurate calendar age estimates in the Roman period in Europe, between ca. AD 60 and ca. AD 230, using the radiocarbon calibration datasets that are currently available. Incorporating the potential for systematic offsets between the measured data and the calibration curve using the ΔR approach suggested by Hogg et al. (2019), only marginally mitigates the biases in calendar date estimates observed. At present, it clearly behoves researchers in this period to “caveat emptor” and validate the accuracy of their calibrated radiocarbon dates and chronological models against other sources of dating information.

Variable-Value axiologies avoid Parfit’s Repugnant Conclusion while satisfying some weak instances of the Mere Addition principle. We apply calibration methods to two leading members of the family of Variable-Value views conditional upon: first, a very weak instance of Mere Addition and, second, some plausible empirical assumptions about the size and welfare of the intertemporal world population. We find that such facts calibrate these two Variable-Value views to be nearly totalist, and therefore imply conclusions that should seem repugnant to anyone who opposes Total Utilitarianism only due to the Repugnant Conclusion.

Solvency II requires that firms with Internal Models derive the Solvency Capital Requirement directly from the probability distribution forecast generated by the Internal Model. A number of UK insurance undertakings do this via an aggregation model consisting of proxy models and a copula. Since 2016 there have been a number of industry surveys on the application of these models, with the 2019 Prudential Regulation Authority (“PRA”) led industry wide thematic review identifying a number of areas of enhancement. This concluded that there was currently no uniform best practice. While there have been many competing priorities for insurers since 2019, the Working Party expects that firms will have either already made changes to their proxy modelling approach in light of the PRA survey, or will have plans to do so in the coming years. This paper takes the PRA feedback into account and explores potential approaches to calibration and validation, taking into consideration the different heavy models used within the industry and relative materiality of business lines.

A novel thinned antenna element distribution for cancelling grating lobes (GLs) as well as for reducing phase shifters (PSs) is presented for a two-dimensional phased-array automotive radar application. First, an efficient clustering technique of vertical adjacent elements is employed with array thinning for a PS reduction of 66.7%. In the proposed distribution, several single-element radiators (non-clustered antenna elements) are placed in the vertical direction with specific spacing in a grid of 16 × 12 (192) elements with λ/2 pitch. This disrupts the periodicity of phase-centers after element-clustering and takes a role as steerable GL canceller with capabilities of tracking and nullifying the GL at any scan angle. The proposed distribution enables beam steering up to ±60° in the azimuth plane, as well as ±25° in the elevation plane with cancelled GL and sidelobes. Furthermore, the proposed distribution has been efficiently calibrated with all elements activated by introducing the code division multiple access technique. To the best of the authors’ knowledge, this work represents the first fully calibrated state-of-the-art thinned distribution phased-array including a novel steerable GL canceller to track and nullify GLs.

Wiggle-match dating of tree-ring sequences is particularly promising for achieving high-resolution dating across periods with reversals and plateaus in the calibration curve, such as the entire post-Columbian period of North American history. Here we describe a modified procedure for wiggle-match dating that facilitates precise dating of wooden museum objects while minimizing damage due to destructive sampling. We present two case studies, a dugout canoe and wooden trough, both expected to date to the 18th–19th century. (1) Tree rings were counted and sampled for dating from exposed, rough cross-sections in the wood, with no or minimal surface preparation, to preserve these fragile objects; (2) dating focused on the innermost and outermost portions of the sequences; and (3) due to the crude counting and sampling procedures, the wiggle-match was approximated using a simple ordered Sequence, with gaps defined as Intervals. In both cases, the outermost rings were dated with precision of 30 years or better, demonstrating the potential of wiggle-match dating for post-European Contact canoes and other similar objects.

A laser stripe sensor has two kinds of calibration methods. One is based on the homography model between the laser stripe plane and the image plane, which is called the one-step calibration method. The other is based on the simple triangular method, which is named as the two-step calibration method. However, the geometrical meaning of each element in the one-step calibration method is not clear as that in the two-step calibration method. A novel mathematical derivation is presented to reveal the geometrical meaning of each parameter in the one-step calibration method, and then the comparative study of the one-step calibration method and the two-step calibration method is completed and the intrinsic relationship is derived. What is more, a one-step calibration method is proposed with 7 independent parameters rather than 11 independent parameters. Experiments are conducted to verify the accuracy and robust of the proposed calibration method.

Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally. Since the beta distribution is a common choice for modeling uncertainty about probabilities, here we propose a family of normative Bayesian models for aggregating probability estimates based on beta distributions. We systematically derive and compare different variants, including hierarchical and non-hierarchical as well as asymmetric and symmetric beta fusion models. Using these models, we show how the beta calibration function naturally arises in this normative framework and how it is related to the widely used Linear-in-Log-Odds calibration function. For evaluation, we provide the new Knowledge Test Confidence data set consisting of subjective probability estimates of 85 forecasters on 180 queries. On this and another data set, we show that the hierarchical symmetric beta fusion model performs best of all beta fusion models and outperforms related Bayesian fusion models in terms of mean absolute error.

The IntCal family of radiocarbon (14C) calibration curves is based on research spanning more than three decades. The IntCal group have collated the 14C and calendar age data (mostly derived from primary publications with other types of data and meta-data) and, since 2010, made them available for other sorts of analysis through an open-access database. This has ensured transparency in terms of the data used in the construction of the ratified calibration curves. As the IntCal database expands, work is underway to facilitate best practice for new data submissions, make more of the associated metadata available in a structured form, and help those wishing to process the data with programming languages such as R, Python, and MATLAB. The data and metadata are complex because of the range of different types of archives. A restructured interface, based on the “IntChron” open-access data model, includes tools which allow the data to be plotted and compared without the need for export. The intention is to include complementary information which can be used alongside the main 14C series to provide new insights into the global carbon cycle, as well as facilitating access to the data for other research applications. Overall, this work aims to streamline the generation of new calibration curves.

Stereo vision allows machines to perceive their surroundings, with plane identification serving as a crucial aspect of perception. The accuracy of identification constrains the applicability of stereo systems. Some stereo vision cameras are cost-effective, compact, and user-friendly, resulting in widespread use in engineering applications. However, identification errors limit their effectiveness in quantitative scenarios. While certain calibration methods enhance identification accuracy using camera distortion models, they rely on specific models tailored to a camera’s unique structure. This article presents a calibration method that is not dependent on any particular distortion model, capable of correcting plane position and orientation identified by any algorithm, provided that the identification error is biased. A high-precision mechanical calibration platform is designed to acquire accurate calibration data while using the same detected material in real measurement scenarios. Experimental comparisons confirm the efficacy of plane pose correction on PCL-RANSAC, with the average relative error of distance reduced by 5.4 times and the average absolute error of angle decreasing by 41.2%.

The Minoan eruption of Santorini, Greece, is an important and often-debated chronological marker in contexts of the Eastern Mediterranean region. Among various age estimates of this event, one based on wiggle-matching of radiocarbon (14C) dates from an olive branch found in Santorini by Friedrich et al. (2006) has been widely discussed. Calibrated age estimates based on wiggle-matching of these 14C ages have been changing with improvements in the 14C calibration curve. As also shown earlier, calibration of average 14C age of multiple tree rings dated together should not be done using a single-year calibration curve. Since recent calibration curves include many single-year 14C datasets, a different approach should be considered to calibrate the average 14C age of block of multiple tree rings. Here we have demonstrated the use of multiple moving average (MA) calibration curves for calibrating the sequence of four 14C ages reported for the Santorini olive branch. The resultant calibrated ages for the Minoan Eruption are relatively younger than previous estimates and range from the late-17th century BCE to mid-16th century BCE date.

Temporal and spatial variation in radiocarbon (14C) in the atmosphere has been the subject of investigation from the first pioneering work of Libby and Arnold. However, as the precision of measurements has improved, now by almost two orders of magnitude, what constitutes a significant variation has also changed. Furthermore, it has become possible to test degrees of variation over much longer timescales and with ever wider geographic coverage. As knowledge has improved, the interpretation of 14C measurements has had to be revised. These re-evaluations, and the loss of chronological precision that comes with accurate calibration, have often been seen as an unfortunate drawback in the 14C dating method. However, these problems have stimulated extensive research in global 14C records, statistical methods for dealing with complex 14C data, and measurement methods. This research has provided a wealth of information useful for other scientific challenges, most notably the quantification of the global carbon cycle, but also enabled, in the right circumstances, measurement precision an order of magnitude better than if there had been no variation in atmospheric 14C. Challenges remain but the research undertaken for 14C calibration has, through its ingenuity and innovation, provided rich scientific dividends in both chronology and broader geoscience.

Fossils are more and more used in phylogenetic evolutionary studies either for clade calibration, or as terminals in a dataset including morphological characters. The strength of these methodological advances relies however on the quality and completeness of the fossil record. For crickets (Insecta, Orthoptera, Gryllidea), few ancient (pre-Cenozoic) well-preserved fossils are known, except for isolated wings often classified in purely fossil groups and a few fossils found in Cretaceous amber. Here, we present two remarkable fossils from mid-Cretaceous amber of France, that were imaged using X-ray synchrotron microtomography and exhibit an exquisite preservation allowing description with a precision similar to that of extant taxa. Palaeonemobius occidentalis Laurent and Desutter-Grandcolas, gen. nov., sp. nov. and Picogryllus carentonensis Josse and Desutter-Grandcolas, gen. nov., sp. nov. are the oldest representatives of the Nemobiinae and Podoscirtinae subfamilies of the Trigonidiidae and Oecanthidae families respectively. P. carentonensis Josse and Desutter-Grandcolas, gen. nov., sp. nov. is also the smallest adult male with a full stridulatory apparatus ever documented in crickets (body length 3.3 mm), and the first taxon of the cricket clade for which male genitalia can be partly described. We discuss the significance of Cretaceous fossils of crickets for future evolutionary studies of this clade.

In the November 2016 U.S. presidential election, many state-level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias—and that these methods failed to adjust for important interactions between key variables like educational attainment, race, and geographic region. Finding calibration weights that account for important interactions remains challenging with traditional survey methods: raking typically balances the margins alone, while post-stratification, which exactly balances all interactions, is only feasible for a small number of variables. In this paper, we propose multilevel calibration weighting, which enforces tight balance constraints for marginal balance and looser constraints for higher-order interactions. This incorporates some of the benefits of post-stratification while retaining the guarantees of raking. We then correct for the bias due to the relaxed constraints via a flexible outcome model; we call this approach “double regression with post-stratification.” We use these tools to re-assess a large-scale survey of voter intention in the 2016 U.S. presidential election, finding meaningful gains from the proposed methods. The approach is available in the multical R package.