Overestimation Bias Research Articles

Can financial literacy overconfidence be predicted by narcissistic tendencies?

PurposeIn this study, it is investigated whether narcissistic tendencies can predict financial literacy overconfidence.Design/methodology/approachFinancial literacy overconfidence is analyzed under these three subcategories: overestimation, overplacement and overprecision, according to individual's self-perception of his/her own financial literacy. In order to evaluate narcissistic tendencies, the Turkish version of the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders Axis II (SCID II) Fourth Edition (DSM-IV) is used. To measure the financial literacy overestimation, overplacement and overprecision biases, a multistaged measurement process designed specially on basic and advanced financial literacy is implemented.FindingsThe current study provides strong evidence that narcissistic tendencies are predictors of financial literacy overestimation, overplacement and overprecision biases. These tendencies in men are greater than in women. Evaluated risk factors of being exposed to these biases among people who have narcissistic tendencies are found to be greater than among people who do not have those tendencies. Due to a particular exposure of narcissistic tendencies, the probability of financial literacy overprecision bias has the highest rate among the others, and it is followed by overestimation and overplacement bias.Originality/valueThis is the first study that measures overconfidence under three subcategories of overestimation, overplacement and overprecision, according to financial literacy. For this reason, it is believed that these results provide valuable evidence in favor of the relation between overconfidence in finance and narcissistic tendencies.

Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees

We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations from terrestrial laser scanning (TLS) data acquired within two days of the drone-lidar acquisition. Our analysis detected 51% of the stems >15 cm DBH, and 87% of stems >50 cm DBH. Errors of omission were much more common for smaller trees than for larger ones, and were caused by removal of points prior to segmentation using a low-intensity and morphological filter. Analysis of segmented trees indicates a strong linear relationship between DBH from drone-lidar segmentations and TLS data. The slope of this relationship is 0.93, the intercept is 4.28 cm, and the r2 is 0.98. However, drone lidar and TLS segmentations overestimated DBH for the smallest trees and underestimated DBH for the largest trees in comparison to field data. We evaluate the impact of random error in point locations and variation in footprint size, and demonstrate that random error in point locations is likely to cause an overestimation bias for small-DBH trees. A Random Forest classifier correctly identified broadleaf and needleleaf trees using stem and crown geometric properties with overall accuracy of 85.9%. We used these classifications and DBH estimates from drone-lidar segmentations to apply allometric scaling equations to segmented individual trees. The stand-level aboveground biomass (AGB) estimate using these data is 76% of the value obtained using a traditional field inventory. We demonstrate that 71% of the omitted AGB is due to segmentation errors of omission, and the remaining 29% is due to DBH estimation errors. Our analysis indicates that high-density measurements from low-altitude drone flight can produce DBH estimates for individual trees that are comparable to TLS. These data can be collected rapidly throughout areas large enough to produce landscape-scale estimates. With additional refinement, these estimates could augment or replace manual field inventories, and could support the calibration and validation of current and forthcoming space missions.

Mitigating free riding in social networks: The impact of underestimating others’ ability in financial market

This study investigates overconfident traders who underestimate their contemporaries’ information processing ability, and it examines the impact of these traders on the financial market within the framework of social networks. As a byproduct, we also explore the impact of traders who overestimate their own ability, which is another manifestation of overconfidence. We find that when information acquisition is endogenous, market outcomes are improved by the effects of both underestimation and overestimation biases. The underlying logic is that traders’ overconfidence adds friction to free-riding on contemporaries’ information, thereby mitigating the information crowding-out effect of social communication. The results are robust to the extension of endogenous network formation and varying information acquisition costs.

Moving attention along the mental number line in preschool age: Study of the operational momentum in 3- to 5-year-old children's non-symbolic arithmetic.

People tend to underestimate subtraction and overestimate addition outcomes and to associate subtraction with the left side and addition with the right side. These two phenomena are collectively labeled 'operational momentum' (OM) and thought to have their origins in the same mechanism of 'moving attention along the mental number line'. OM in arithmetic has never been tested in children at the preschool age, which is critical for numerical development. In this study, 3-5years old were tested with non-symbolic addition and subtraction tasks. Their level of understanding of counting principles (CP) was assessed using the give-a-number task. When the second operand's cardinality was 5 or 6 (Experiment 1), the child's reaction time was shorter in addition/subtraction tasks after cuing attention appropriately to the right/left. Adding/subtracting one element (Experiment 2) revealed a more complex developmental pattern. Before acquiring CP, the children showed generalized overestimation bias. Underestimation in addition and overestimation in subtraction emerged only after mastering CP. No clear spatial-directional OM pattern was found, however, the response time to rightward/leftward cues in addition/subtraction again depended on stage of mastering CP. Although the results support the hypothesis about engagement of spatial attention in early numerical processing, they point to at least partial independence of the spatial-directional and magnitude OM. This undermines the canonical version of the number line-based hypothesis. Mapping numerical magnitudes to space may be a complex process that undergoes reorganization during the period of acquisition of symbolic representations of numbers. Some hypotheses concerning the role of spatial-numerical associations in numerical development are proposed.

Overestimation and contraction biases of depth information stored in working memory depend on spatial configuration.

Depth perception is essential for effective interaction with the environment. Although the accuracy of depth perception has been studied extensively, it is unclear how accurate the depth information is stored in working memory. In this study, we investigated the accuracy and systematic biases of depth representation by a delayed estimation task. The memory array consisted of items presented at various stereoscopic depth positions, and the participants were instructed to estimate the depth position of one target item after a retention interval. We examined the effect of spatial configuration by comparing the memory performance in the whole-display condition where non-target memory items were present during retrieval with that in the single-display condition where non-target memory items were absent. In the single-display condition, we found an overestimation bias that the depth estimates were farther than the corresponding depth positions defined by disparity, and a contraction bias that the stored depth positions near the observer were overestimated and those far from the observer were underestimated. The magnitude of these biases increased with the number of to-be-stored items. However, in the whole-display condition, the overestimation bias was corrected and the contraction bias did not increase with the number of to-be-stored items. Our findings suggested that the number of to-be-stored items could affect the accuracy of depth working memory, and its effect depended crucially on whether the information of spatial configuration of memory display was available at the retrieval stage.

Overestimating the valuations and preferences of others.

People often make judgments about their own and others' valuations and preferences. Across 12 studies (N = 17,594), we find a robust bias in these judgments such that people overestimate the valuations and preferences of others. This overestimation arises because, when making predictions about others, people rely on their intuitive core representation of the experience (e.g., is the experience generally positive?) in lieu of a more complex representation that might also include countervailing aspects (e.g., is any of the experience negative?). We first demonstrate that the overestimation bias is pervasive for a wide range of positive (Studies 1-5) and negative experiences (Study 6). Furthermore, the bias is not merely an artifact of how preferences are measured (Study 7). Consistent with judgments based on core representations, the bias significantly reduces when the core representation is uniformly positive (Studies 8A-8B). Such judgments lead to a paradox in how people see others trade off between valuation and utility (Studies 9A-9B). Specifically, relative to themselves, people believe that an identically paying other will get more enjoyment from the same experience, but paradoxically, that an identically enjoying other will pay more for the same experience. Finally, consistent with a core representation explanation, explicitly prompting people to consider the entire distribution of others' preferences significantly reduced or eliminated the bias (Study 10). These findings suggest that social judgments of others' preferences are not only largely biased, but they also ignore how others make trade-offs between evaluative metrics. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration

A growing number of electricity utilities use machine learning-based outage prediction models (OPMs) to predict the impact of storms on their networks for sustainable management. The accuracy of OPM predictions is sensitive to sample size and event severity representativeness in the training dataset, the extent of which has not yet been quantified. This study devised a randomized and out-of-sample validation experiment to quantify an OPM’s prediction uncertainty to different training sample sizes and event severity representativeness. The study showed random error decreasing by more than 100% for sample sizes ranging from 10 to 80 extratropical events, and by 32% for sample sizes from 10 to 40 thunderstorms. This study quantified the minimum number of sample size for the OPM attaining an acceptable prediction performance. The results demonstrated that conditioning the training of the OPM to a subset of events representative of the predicted event’s severity reduced the underestimation bias exhibited in high-impact events and the overestimation bias in low-impact ones. We used cross entropy (CE) to quantify the relatedness of weather variable distribution between the training dataset and the forecasted event.

Robust, accurate depth-resolved attenuation characterization in optical coherence tomography.

Depth-resolved optical attenuation coefficient is a valuable tissue parameter that complements the intensity-based structural information in optical coherent tomography (OCT) imaging. Herein we systematically analyzed the under- and over-estimation bias of existing depth-resolved methods when applied to real biological tissues, and then proposed a new algorithm that remedies these issues and accommodates general OCT data that contain incomplete decay and noise floor, thereby affording consistent estimation accuracy for practical biological samples of different scattering properties. Compared with other algorithms, our method demonstrates remarkably improved estimation accuracy and numerical robustness, as validated via numerical simulations and on experimental OCT data obtained from both silicone-TiO2 phantoms and human ventral tongue leukoplakia samples.

Total mercury bias in soil analysis by CV-AFS: causes, consequences and a simple solution based on sulfhydryl cotton fiber as a clean-up step.

This study examines the matrix effect over the trueness for determining total mercury (THg) using CV-AFS. We demonstrate that matrix interferences in soils and sediment samples cannot be eliminated by acid digestion and establish the use of sulfhydryl cotton fiber (SCF), a malleable, cheap and easy to synthesize fiber, as a mandatory solution capable to overcome this bias. Using the classic CV-AFS approach, an overestimation bias for THg recovery values of >140% in a certified reference material was reported. Interference metals test was conducted, thus discarding any influence of metals in the overestimation bias. Therefore, a clean-up step using SCF was proposed, and tests with synthesized fiber did not present a dispersion of >0.08 ng L-1. Moreover, validation was performed by analyzing three certified reference material and yielding mean recovery percentages of 100% ± 1%. A validated methodology was applied to ten environmental soil samples; THg values obtained varied from 129 to 384 ng g-1. Finally, a comparison between sample results obtained and reference method did not show any significant differences (p > 0.05), thus highlighting the efficacy of SCF-CV-AFS for THg quantification in environmental solid samples.

The Flux Distribution and Sky Density of 25th Magnitude Main Belt Asteroids

Abstract Digital tracking enables telescopes to detect asteroids several times fainter than conventional techniques. We describe our optimized methodology to acquire, process, and interpret digital tracking observations, and we apply it to probe the apparent magnitude distribution of main belt asteroids fainter than any previously detected from the ground. All-night integrations with the Dark Energy Camera (DECam) yield 95% completeness at R magnitude 25.0 and useful sensitivity to R = 25.6 mag when we use an analytical detection model to correct flux overestimation bias. In a single DECam field observed over two nights, we detect a total of 3234 distinct asteroids, of which 3123 are confirmed on both nights. At opposition from the Sun, we find a sky density of 697 ± 15 asteroids per square degree brighter than R = 25.0 mag, and 1031 ± 23 brighter than R = 25.6 mag. We agree with published results for the sky density and apparent magnitude distribution of asteroids brighter than R = 23 mag. For a power law defined by dN/dR ∝ 10 αR , we find marginally acceptable fits with a constant slope α =0.28 ± 0.02 from R = 20 to 25.6 mag. Better fits are obtained for a broken power law with α = 0.218 ± 0.026 for R = 20–23.5 mag, steepening to α = 0.340 ± 0.025 for R = 23.5–25.6 mag. The constant or steepening power law indicates that asteroids fainter than R = 23.5 mag are abundant, contrary to some previous claims but consistent with theory.

Evaluation of an AI-based, automatic coronary artery calcium scoring software

ObjectivesTo evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference.MethodsThis observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test.ResultsThe correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001).ConclusionsThere was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding.Key Points• Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting.• An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.

Hydrologic evaluation of bias corrected CMORPH rainfall estimates at the headwater catchment of the Zambezi River

Uncorrected and bias corrected CMORPH satellite-based rainfall estimates were evaluated for application in the Representative Elementary Watershed (REW) modelling approach in the Kabompo Basin, a headwater catchment of the Zambezi River. Results of modelling (2008–2013) by use of raingauge rainfall served as benchmark to the simulation results. The uncorrected CMORPH rainfall show overestimation (>20%) and underestimation (<−15%) biases for the 2000–2015 period. A linear-multiplicative bias correction scheme was applied to remove the bias. When applied to the REW model, bias corrected CMORPH rainfall improved the match between observed and simulated streamflow. Improvements in streamflow simulation are reported for highest discharges and streamflow totals. Objective function values for Relative Volume Error and for Nash Sutcliffe Efficiency improved significantly by the bias correction. Errors in uncorrected CMORPH rainfall estimates cause large mismatch between simulated and observed streamflow, however mismatch reduces notably for bias corrected satellite rainfall. Optimized REW parameter values changed by > 50% when gauge-based rainfall inputs are replaced by uncorrected CMORPH estimates. However, parameter values, remained within physically acceptable ranges for bias corrected CMORPH, for intermediate flows and for the wet season. This study shows that bias correction of CMORPH rainfall in the Kabompo Basin is critical for successful application in rainfall-runoff modelling.

Behavior of the Linear Regression method to estimate bias and accuracies with correct and incorrect genetic evaluation models

Bias in genetic evaluations has been a constant concern in animal genetics. The interest in this topic has increased in the last years, since many studies have detected overestimation (bias) in estimated breeding values (EBV). Detecting the existence of bias, and the realized accuracy of predictions, is therefore of importance, yet this is difficult when studying small data sets or breeds. In this study, we tested by simulation the recently presented method Linear Regression (LR) for estimation of bias, slope, and accuracy of pedigree EBV. The LR method computes statistics by comparing EBV from a data set containing old, partial information with EBV from a data set containing all information (old and new, a whole data set) for the same individuals. The method proposes an estimator for bias (Δpˆ), an estimator of slope (bpˆ), and 3 estimators related to accuracies: the ratio between accuracies [Formula: see text] the reliability of the partial data set (accp2ˆ), and the ratio of reliabilities (ρp,w2ˆ). We simulated a dairy scheme for low (0.10) and moderate (0.30) heritabilities. In both cases, we checked the behavior of the estimators for 3 scenarios: (1) when the evaluation model is the same as the model used to simulate the data; (2) when the evaluation model uses an incorrect heritability; and (3) when the data includes an environmental trend. For scenarios in which the evaluation model was correct, the LR method was capable of correctly estimating bias, slope, and accuracies, with better performance for higher heritability [i.e., corr(bp,bpˆ) was 0.45 for h2 = 0.10 and 0.59 for h2 = 0.30]. In cases of the use of incorrect heritabilities in the evaluation model, the bias was correctly estimated in direction but not in magnitude. In the same way, the magnitudes of bias and of slope were underestimated in scenarios with environmental trends in data, except for cases in which contemporary groups were random and greatly shrunken. In general, accuracies were well estimated in all scenarios. The LR method is capable of checking bias and accuracy in all cases, if the evaluation model is reasonably correct or robust, and its estimations are more precise with more information (e.g., high heritability). If the model uses an incorrect heritability or a hidden trend exists in the data, it is still possible to estimate the direction and existence of bias and slope but not always their magnitudes.

Adaptive phase correction of diffusion-weighted images

Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which follow a non-Gaussian distribution, e.g. Rician. PC finds its natural application to diffusion-weighted images (DWIs) due to their inherent low signal-to-noise ratio and consequent non-Gaussianity that induces a signal overestimation bias that propagates to the calculated diffusion indices. PC effectiveness depends upon the quality of the phase estimation, which is often performed via a regularization procedure. We show that a suboptimal regularization can produce alterations of the true image contrast in the real-valued phase-corrected images. We propose adaptive phase correction (APC), a method where the phase is estimated by using MRI noise information to perform a complex-valued image regularization that accounts for the local variance of the noise. We show, on synthetic and acquired data, that APC leads to phase-corrected real-valued DWIs that present a reduced number of alterations and a reduced bias. The substantial absence of parameters for which human input is required favors a straightforward integration of APC in MRI processing pipelines.

Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China

Over the last decade, a series of global moderate resolution leaf area index (LAI) products have become available and been widely applied in many disciplines. At the same time, there is an increasing demand for the uncertainties associated with these products, which has to be determined through rigorous validation studies. This study validated seven global LAI products — EPS, GEOV2, GLASS, GLOBMAP, MODIS, PROBA-V, and VIIRS — over typical agricultural croplands in northeastern China. Seasonal continuous LAI measurements were obtained from field campaigns in paddy rice fields in 2012 and 2013, and in maize, soybean, and sorghum fields in 2016. High resolution reference LAI maps were first derived from HJ-1, Landsat 7, and Sentinel-2A images with the look-up table (LUT) inversion method and were evaluated with the field measured LAI (R2 = 0.85 and RMSE = 0.66). Subsequently, the moderate resolution LAI products were validated with the upscaled high resolution reference LAI.All LAI products show typical seasonal variation patterns of agricultural crops, but distinct differences exist among the products. The product quality indicators show large deviations during the peak growing season, whereas the relative uncertainties are higher during the green-up and senescent phases. Both EPS and GLASS show some saturation effects at LAI ~ 4.0 and underestimate the reference LAI (>0.5), whereas GLOBMAP shows the largest overestimation (bias = 0.96). GEOV2 and PROBA-V significantly overestimate the LAI for all crops. In contrast, MODIS and VIIRS underestimate and show high variations (RMSE >1.50, RRMSE >47%) compared with the reference LAI. In general, the global moderate resolution LAI products show moderate agreement with the reference LAI (RMSE: 0.80–2.0 and RRMSE: 25–60%). The product uncertainties are higher over paddy rice fields than those over the other crop fields. The uncertainties are mainly attributed to the lack of regional tuning of the global algorithms for agricultural crops at different growth stages. Further algorithm improvement and validation studies are necessary to improve the global LAI products for regional applications.

Rainfall partitioning measurement and rainfall interception modelling in a tropical semi-deciduous Atlantic forest remnant

Understanding the interaction between the hydrological cycle and native forests is essential to improve watershed management and support ecological services. The objectives of this study were to analyse and evaluate rainfall interception models applied to an ecosystem from the Brazilian Atlantic Forest Biome (BAFB), classified as “Montane Semi-Deciduous Forest.” The variables Gross Rainfall (P) (rainfall that reaches the canopy), Throughfall (Tf), Stemflow (Sf), and Interception Loss (I) were measured from September 2012 to March 2015. Weather variables were quantified above the canopy by a weather station and then used for canopy evaporation estimation. This estimation was based on two approaches: the slope of the linear regression of gross rainfall against interception, or the Gash procedure (Ev1), and the Penman–Monteith equation (Ev2). Two rainfall interception models (Liu and Gash) were employed, using both Ev1 and Ev2. Thirty-two fixed rain gauges were used for Tf measurements and 32 trees were selected across species and diameter at breast height ranges for Sf observations. The revised version of the analytical Gash model underestimated I by -17.5% and -11.1% for Ev1 and Ev2, respectively, resulting in less accurate estimates. Based on these relative errors, the performance of the Gash model was classified as “fair.” The Liu model overestimated I by 5.3% and 11.3% for Ev1 and Ev2, resulting in assessments of “good” and “fair” respectively, and thus indicating improved performance compared to the Gash model. Therefore, the Liu model coupled with Ev1 is preferable for simulation of rainfall interception in semi-deciduous forests of the BAFB. However, slight overestimation bias was observed, and the model requires tweaking with respect to the estimation of forest storage capacity for this ecosystem. Interception loss modelling is a strategic tool for assessing the influence of different weather patterns and forest vegetative features on water balance in semi-deciduous forest ecosystems.

Biology Teachers' Worldviews on the Global Distribution and Loss of Biodiversity: A GIS-Based Mental-Mapping Approach.

This paper explores (1) student teachers’ mental maps of the global distribution and loss of biodiversity and (2) their perception of threatened biodiversity at the national, transnational and global levels. Data was collected from a questionnaire study of student biology teachers from Germany (n = 868) and Costa Rica (n = 284). Student teachers’ mental maps matched quite well with the scientific view. Nevertheless, they clearly showed a “brazilisation bias,” meaning that the first and foremost country associated with high and threatened biodiversity was Brazil. Industrialized countries were often misconceived to have a particularly threatened biodiversity. Except for Brazil (and Costa Rica in the Costa Rican sample), most students neglected a connection between a country’s high biodiversity and its high threat as proposed by the biodiversity hotspots concept. Despite this common ground, major ethnocentric distortions merged in the composite mental maps for each sample: German students had a more global perspective on biodiversity and its loss, whereas Costa Ricans students had a more localized view. Student teachers from both countries have largely overestimated the percentage of threatened plant species on a national, transnational and global level (“overestimation bias”). In addition, the estimated percentage of threatened plant species have correspondingly increased with a greater distance from the students’ home country (“spatial optimism bias”). Results will be discussed in terms of educational implications.

Links Between Users' Online Social Network Homogeneity, Ambiguity Tolerance, and Estimated Public Support for Own Opinions.

False consensus effect (FCE) refers to a cognitive bias of relative overestimation of public support for one's own opinion. FCE has been linked to selective social interaction with like-minded people as well as to selective exposure to attitude-consistent information. Previous studies tested these links mostly in offline settings. However, it is assumed that FCE is also affected by the homogeneity of users' online contact network, the extent to which they use online social network (OSN), and their individual tendency to avoid ambiguous information. Two online studies with a total of 380 participants aged 18-35 years were conducted to test these hypotheses through a multilevel modeling approach. In Study 1, participants with a more homogeneous online network, longer daily OSN usage time, and lower ambiguity tolerance displayed significantly higher FCE. The effects of network homogeneity and ambiguity tolerance were replicated in Study 2. The implications of these findings are interpreted in the context of prior studies on FCE as well as the notion of OSN as "echo chambers."

Overestimating the Valuations of Others: People Perceive Others as Experiencing Everything More Intensely

People often make judgments about their own and others’ valuations and preferences. Across 12 studies (N=18,818), we find a robust bias in these judgments such that people overestimate the valuations and preferences of others. This overestimation arises because, when making predictions about others, people rely on their intuitive core representation of the experience (e.g., is the experience generally positive?) in lieu of a more complex representation that might also include countervailing aspects (e.g., is any of the experience negative?). We first demonstrate that the overestimation bias is pervasive for a wide range of positive (Studies 1-5) and negative experiences (Study 6). Furthermore, the bias is not merely an artifact of how preferences are measured (Study 7). Consistent with judgments based on core representations, the bias significantly reduces when the core representation is uniformly positive (Studies 8A-8B). Such judgments lead to a paradox in how people see others trade off between valuation and utility (Studies 9A-9B). Specifically, relative to themselves, people believe that an identically-paying other will get more enjoyment from the same experience, but paradoxically, that an identically-enjoying other will pay more for the same experience. Finally, consistent with a core representation explanation, explicitly prompting people to consider the entire distribution of others’ preferences significantly reduced or eliminated the bias (Study 10). These findings suggest that social judgments of others’ preferences are not only largely biased, but they also ignore how others make tradeoffs between evaluative metrics.

Estimating glomerular filtration rate at the transition from pediatric to adult care

The current Kidney Disease Improving Global Outcomes (KDIGO) guidelines recommend the use of the bedside creatinine-based Chronic Kidney Disease in Children (CKiD) equation to estimate glomerular filtration rate (GFR) in children and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation in adults. However, this approach causes implausible changes in estimated GFR (eGFR) at the transition from pediatric to adult care. We investigated the performance of the KDIGO strategy and various creatinine-based eGFR equations in a cross-sectional dataset of 5,764 subjects (age 10-30 years), using directly measured GFR (mGFR) as reference. We also evaluated longitudinal GFR slopes in 136 subjects who transitioned to adult care. Implausible changes in eGFR resulted from the large overestimation (bias=+21 mL/min/1.73m2) and poor precision of the CKD-EPI equation in the 18-20 year age group, compared to CKiD in the 16-18 year age group (bias=-2.7 mL/min/1.73m2), resulting in a mean change of 23 mL/min/1.73m2 at the transition to adult care. Averaging the CKiD and CKD-EPI estimates in young adults only partially mitigated this issue. The Full Age Spectrum equation (with and without height), the Lund-Malmö Revised equation, and an age-dependent weighted average of CKiD and CKD-EPI resulted in much smaller changes in eGFR at the transition (change of 0.6, -2.1, -0.9 and -1.8 mL/min/1.73m2, respectively). The longitudinal analysis revealed a significant difference in average GFR slope between mGFR and the KDIGO strategy (-2.2 vs.+2.9 mL/min/1.73 m2/year), which was not observed with the other approaches. These results suggest that the KDIGO recommendation for GFR estimation at the pediatric-adult care transition should be revisited.