Individual Uncertainty Research Articles

Heterogeneity of foreign investors and Knightian uncertainty: Evidence from the Chinese capital market

Foreign investors are playing an increasingly important role in China's capital market, and the impact of northbound funds on Knightian uncertainty has attracted much academic attention. Based on the characteristics of northbound funds' trading behavior and investors' shareholding stability, this study divides them into long-term investors and short-term opportunists. It also examines the effect of long- and short-term foreign investors on individual stock Knightian uncertainty. The study finds that higher stability of northbound fund shareholding lowers the Knightian uncertainty of individual stocks, and long-term foreign institutional investors contribute to market stability. Furthermore, long-term investments in northbound funds can reduce Knightian uncertainty by improving the information environment, influencing the trading behavior of institutional investors, boosting investor confidence, and helping investors form consistent expectations. In conclusion, short-term foreign investors aggravate market volatility, whereas long-term foreign institutional investors help stabilize the market.

Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots

AbstractClosed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

The Relationship Between the Intolerance of Uncertainty and Time Management in Individuals with Generalized Anxiety Disorders

The Relationship Between the Intolerance of Uncertainty and Time Management in Individuals with Generalized Anxiety Disorders

Demonstration of the propagation of errors using resistors

We present a simple experiment to demonstrate the concept of propagation of errors by measuring resistors on printed circuit boards (PCBs). We devise a method for connecting the resistors on a PCB that allows a relatively quick measurement of 200 resistor pairs to show that the distributions of the measurement of individual resistances, as well as the total resistance, are Gaussian-distributed, as expected from the central limit theorem. The measurement demonstrates how individual uncertainties propagate to the total uncertainty. The experiment is part of a laboratory course on statistics for physics students that emphasizes the application of statistics in data analysis.

THE INFLUENCE INTOLERANCE OF UNCERTAINTY ON CYBERCHONDRIA IN EARLY ADULT INTERNET USERS

This study aims to determine the effect of intolerance of uncertainty on cyberchondria in early adult internet users. This research uses quantitative methods. The sample collection technique used in this study was purposive sampling. This study had a sample of 138 early adults in Padang City. The data collection method in this study used a questionnaire distributed via a google form link. The research instrument used is the cyberchondria scale that has been compiled which refers to the Cyberchondria Severity Scale (CSS) which is made based on the dimensions described by Mcelroy & Shevlin (2014) which were then modified by Aulia (2019) to become 30 items with a reliability of 0.899 and uses an uncertainty intolerance scale that refers to the IUS-12 (Intolerance of Uncertainty Scale- Short Form) which is made based on aspects that have been developed by Carleton et al. (2007) as many as 12 items, which were then adapted into Indonesian by Bagaskara (2009) and have a reliability of 0.735. The results of data analysis obtained using simple linear regression analysis show that Ho is rejected and Ha is accepted with an r-square value of 0.189 which indicates that there is a positive influence between intolerance of uncertainty on cyberchondria The higher the intolerance of uncertainty felt by individuals, the higher the cyberchondria in individuals, and vice versa the lower the intolerance of uncertainty in individuals, the lower the cyberchndria that occurs in individuals.

Banking sector reforms in a challenging environment: An emerging financial market experience

The long-lasting impact of reforms on financial institutions in the presence of country level risks and uncertainties may be unclear but has been rarely investigated. By using more than two decades of Pakistani banking industry data covering a period of uncertain policies and volatile political and economic environment alongside severe security threats due to terrorism, this study examines technical change, scale effect, and total factor productivity growth. Our estimates show that despite deep economic and regulatory reforms spreading over a decade, country level risks and uncertainties led to no longer run technological progress and productivity growth. This made the long-run outcome of broader reforms in the presence of a number of uncertainties less satisfactory and did not help materialize the true benefits of reforms. Change of ownership through privatization contributed positively in achieving technological progress and productivity growth, but this limited positive effect faded subsequently. Interestingly, the negative impact of risks and uncertainties is more severe for domestic privately owned and foreign-owned banking firms with parent institutions based in other than Western and developed countries. More specific analysis of the individual uncertainties and related risks shows that although economic, legal, and security-related risks and uncertainties are important for all firms in determining the outcome of reforms, increased uncertainty with corruption perception, law & order conditions, and investment climate in particular impacts more domestically owned firms.

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification.

Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored. Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold. Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74). Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.

Features of the vitality of student youth in conditions of uncertainty

The publication addresses the issue of the viability of modern youth in conditions of uncertainty. The concept of uncertainty, the concept of vitality, the type of attachment in various scientific directions is considered. The purpose of the study was to study the viability of student youth in conditions of uncertainty. The hypothesis of the study is that vitality in conditions of uncertainty in individuals with different types of representation of attachment to the mother and the nature of relationships in adulthood has psychological characteristics: individuals with a reliable type of representation of attachment choose constructive coping strategies when resolving emotionally tense situations, have a low level of intrapersonal conflicts, have value orientations that are individual priorities and are often manifested in behavior under conditions of uncertainty; have a high level of self-activation. The study involved 141 people, 3rd–4th year students of different areas of training at Tula State University and the International Police Academy of the WPA. The work was carried out using methods — questionnaire M. Maskulintser, F. Shaver Interview about attachment in adulthood; to determine ways to overcome difficult situations in various areas of mental activity, the method of R. Lazarus and S. Folkman, to diagnose value orientations, the test of S. Schwartz, to determine the level of intrapersonal conflict, the test of A. Shipilov; to determine the level of self-activation, the technique of M. Odintsova, N. Radchikova. It was revealed that to overcome a situation of uncertainty it is necessary to be viable and be able to manage your resources. At the same time, it is precisely the conditions of uncertainty that affect the ability to adequately assess and control the situation, the ability to value reorientations, and return to past life experiences. The vitality of student youth in conditions of uncertainty is determined by individual life experience — the type of representation of attachment formed in childhood. A healthy emotional connection with a significant adult, his love forms a reliable type of attachment, which provides a resource that allows one to resolve difficulties most effectively and accept changes.

Effective sample size: A measure of individual uncertainty in predictions.

Clinical prediction models are estimated using a sample of limited size from the target population, leading to uncertainty in predictions, even when the model is correctly specified. Generally, not all patient profiles are observed uniformly in model development. As a result, sampling uncertainty varies between individual patients' predictions. We aimed to develop an intuitive measure of individual prediction uncertainty. The variance of a patient's prediction can be equated to the variance of the sample mean outcome in hypothetical patients with the same predictor values. This hypothetical sample size can be interpreted as the number of similar patients that the prediction is effectively based on, given that the model is correct. For generalized linear models, we derived analytical expressions for the effective sample size. In addition, we illustrated the concept in patients with acute myocardial infarction. In model development, can be used to balance accuracy versus uncertainty of predictions. In a validation sample, the distribution of indicates which patients were more and less represented in the development data, and whether predictions might be too uncertain for some to be practically meaningful. In a clinical setting, the effective sample size may facilitate communication of uncertainty about predictions. We propose the effective sample size as a clinically interpretable measure of uncertainty in individual predictions. Its implications should be explored further for the development, validation and clinical implementation of prediction models.

대학생의 진로 불확실성 감소가 진로결정 자기효능감에 미치는 영향

The complexity and uncertainty of modern society changing every minute have enormous impacts on the lives and careers of individuals living in this era. The major changes to the structure of the occupational society driven by the COVID-19 pandemic and the Fourth Industrial Revolution are increasing uncertainty about careers. In this situation, how college students deal with the career uncertainty they experience works as an important element in their career decisions. This study thus sets out to verify the effects of reduced career uncertainty of college students on their career decision-making self-efficacy.
 In order to reduce career uncertainty, the investigator implemented a group counseling program for careers whose effectiveness on the reduced career uncertainty of college students was verified. The study selected college students in lower graders(freshmen and sophomores) to participate in the program for the research purpose at S University in Chungcheongnam Province. They were divided into 163 experimental groups and 159 control groups. While the experiment group participated in an 11-session program with a 50-minute session once or twice per week, the control group participated in no programs. A survey was conducted before and after the program, and collected data was put into the paired-sample t-test and covariance analysis with the SPSS Win 23.0 program.
 The findings show that the experiment group had reduction effects in all the subfactors of career uncertainty and that the control group showed no statistically significant effects. The experiment group that had reduction effects for career uncertainty made a statistically significant increase in career decision-making self-efficacy, but there were no significant changes to that of the control group. The present study demonstrated that the reduction of career uncertainty as the first measure for the career uncertainty of individuals had positive effects on the enhancement of career decision-making self-efficacy and shed light on causal relations between the reduction of career uncertainty and career decision-making self-efficacy, thus holding its significance. The findings of the study are expected to serve as grounds for plans to provide more effective career decision counseling by examining the relations of psychological factors in college students that have difficulties with their career decisions due to the uncertainty of their future.

Propagating Uncertainty in Predicting Individuals and Means Illustrated with Foliar Chemistry and Forest Biomass

Quantifying uncertainty is important to establishing the significance of comparisons, to making predictions with known confidence, and to identifying priorities for investment. However, uncertainty can be difficult to quantify correctly. While sampling error is commonly reported based on replicate measurements, the uncertainty in regression models used to estimate forest biomass from tree dimensions is commonly ignored and has sometimes been reported incorrectly, due either to lack of clarity in recommended procedures or to incentives to underestimate uncertainties. Even more rarely are the uncertainty in predicting individuals and the uncertainty in the mean both recognized for their contributions to overall uncertainty. In this paper, we demonstrate the effect of propagating these two sources of uncertainty using a simple example of calcium concentration of sugar maple foliage, which does not require regression, then the mass of foliage and calcium content of foliage, and finally an entire forest with multiple species and tissue types. The uncertainty due to predicting individuals is greater than the uncertainty in the mean for studies with few trees—up to 30 trees for foliar calcium concentration and 50 trees for foliar mass and calcium content in the data set we analyzed from the Hubbard Brook Experimental Forest. The most correct analysis will take both sources of uncertainty into account, but for practical purposes, country-level reports of uncertainty in carbon stocks can safely ignore the uncertainty in individuals, which becomes negligible with large enough numbers of trees. Ignoring the uncertainty in the mean will result in exaggerated confidence in estimates of forest biomass and carbon and nutrient contents.

Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020

The Amazon is the largest continuous tropical forest in the world and plays a key role in the global carbon cycle. Human-induced disturbances and climate change have impacted the Amazon carbon balance. Here we conduct a comprehensive synthesis of existing state-of-the-art estimates of the contemporary land carbon fluxes in the Amazon using a set of bottom-up methods (i.e., dynamic vegetation models and bookkeeping models) and a top-down inversion (atmospheric inversion model) over the Brazilian Amazon and the whole Biogeographical Amazon domain. Over the whole biogeographical Amazon region bottom-up methodologies suggest a small average carbon sink over 2010-2020, in contrast to a small carbon source simulated by top-down inversion (2010-2018). However, these estimates are not significantly different from one another when accounting for their large individual uncertainties, highlighting remaining knowledge gaps, and the urgent need to reduce such uncertainties. Nevertheless, both methodologies agreed that the Brazilian Amazon has been a net carbon source during recent climate extremes and that the south-eastern Amazon was a net land carbon source over the whole study period (2010-2020). Overall, our results point to increasing human-induced disturbances (deforestation and forest degradation by wildfires) and reduction in the old-growth forest sink during drought.

Remaining useful life prediction of proton exchange membrane fuel cell based on Wiener process and Bayesian GRU network considering multi-source uncertainties

ABSTRACT Proton exchange membrane fuel cell (PEMFC) is considered as one of the most promising green energy devices. Although fruitful results can be available for the remaining useful life (RUL) prediction of PEMFC, stochastic uncertainties have never been considered. To tackle this problem, a hybrid method is proposed in this paper. Wiener process with temporal uncertainty and individual uncertainty is adopted to model the degradation of the state of health (SOH), which is then estimated from monitoring voltage with measurement noise using the unscented Kalman filter (UKF), where unknown filtering and model parameters are jointly identified by expectation-maximization (EM) algorithm and Rauch-Tung-Striebel (RTS) smoother. Finally, gated recurrent unit (GRU) network is employed to realize the RUL prediction with the prediction uncertainty quantified by the Bayesian variational inference technology. The proposed method is verified on the experimental data. Results indicate that smaller mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) values can be obtained compared with other methods. When 60% data are used for prediction, the proposed method can achieve a RUL prediction accuracy with 1.63% and 2.17% relative errors under static and dynamic conditions, respectively, which illustrates the feasibility and superiority of the proposed method.

Benefits of spatial uncertainty aggregation for segmentation in digital pathology.

Uncertainty estimation has gained significant attention in recent years for its potential to enhance the performance of deep learning (DL) algorithms in medical applications and even potentially address domain shift challenges. However, it is not straightforward to incorporate uncertainty estimation with a DL system to achieve a tangible positive effect. The objective of our work is to evaluate if the proposed spatial uncertainty aggregation (SUA) framework may improve the effectiveness of uncertainty estimation in segmentation tasks. We evaluate if SUA boosts the observed correlation between the uncertainty estimates and false negative (FN) predictions. We also investigate if the observed benefits can translate to tangible improvements in segmentation performance. Our SUA framework processes negative prediction regions from a segmentation algorithm and detects FNs based on an aggregated uncertainty score. It can be utilized with many existing uncertainty estimation methods to boost their performance. We compare the SUA framework with a baseline of processing individual pixel's uncertainty independently. The results demonstrate that SUA is able to detect FN regions. It achieved of 0.92 on the in-domain and 0.85 on the domain-shift test data compared with 0.81 and 0.48 achieved by the baseline uncertainty, respectively. We also demonstrate that SUA yields improved general segmentation performance compared with utilizing the baseline uncertainty. We propose the SUA framework for incorporating and utilizing uncertainty estimates for FN detection in DL segmentation algorithms for histopathology. The evaluation confirms the benefits of our approach compared with assessing pixel uncertainty independently.

The difference of model robustness assessment using cross‐validation and bootstrap methods

AbstractThe validation principles on Quantitative Structure Activity Relationship issued by Organization for Economic and Co‐operation and Development describe three criteria of model assessment: goodness of fit, robustness and prediction. In the case of robustness, two ways are possible as internal validation: bootstrap and cross‐validation. We compared these validation metrics by checking their sample size dependence, rank correlations to other metrics and uncertainty. We used modeling methods from multivariate linear regression to artificial neural network on 14 open access datasets. We found that the metrics provide similar sample size dependence and correlation to other validation parameters. The individual uncertainty originating from the calculation recipes of the metrics is much smaller for both ways than the part caused by the selection of the training set or the training/test split. We concluded that the metrics of the two techniques are interchangeable, but the interpretation of cross‐validation parameters is easier according to their similar range to goodness‐of‐fit and prediction metrics. Furthermore, the variance originating from the random elements of the calculation of cross‐validation metrics is slightly smaller than those of bootstrap ones, if equal calculation load is applied.

The death of a neurotrauma trial lessons learned from the prematurely halted randomized evaluation of surgery in elderly with traumatic acute subdural hematoma (RESET-ASDH) trial

IntroductionAcute subdural hematoma (ASDH) due to traumatic brain injury (TBI) constitutes an increasing global health problem, especially in the elderly population. Treatment decisions on surgical versus conservative management pose a neurosurgical dilemma. Large practice variation exists between countries, hospitals, and individual neurosurgeons, illustrating the presence of ‘clinical equipoise’. The RESET-ASDH trial aimed to address this dilemma but was terminated prematurely due to insufficient patient recruitment. Research questionWhat factors may have contributed to the premature discontinuation of the RESET-ASDH trial? Materials and methodsThe RESET-ASDH was a multicenter randomized controlled trial (RCT) comparing functional outcome at 1 year after early surgery or an initial conservative treatment in elderly patients (≥65 years) with a traumatic ASDH. Logs of registry data, medical-ethical approval timelines and COVID-19 related research documents were analyzed. Furthermore, non-structured interviews with involved clinical research personnel were conducted. ResultsThe concept of clinical equipoise was broadly misinterpreted by neurosurgeons as individual uncertainty, hampering patient recruitment. Also, the elderly target population complicated the inclusion process as elderly and their informal caregivers were hesitant to participate in our acute surgical trial. Moreover, the COVID-19 pandemic added additional hurdles like delayed medical-ethical approval, a decline in eligible patients and repeated trial halts during the peaks of the pandemic. Discussion and conclusionThe premature termination of the RESET-ASDH study may have been related to the trial’s methodology and target population with an additional impact of COVID-19. Future acute neurosurgical trials in elderly may consider these challenges to prevent premature trial termination.

Investigating the Gold Investment Decision during the COVID-19 Pandemic in Bengkulu City, Indonesia

The current landscape of the Indonesian economy bears the weight of the COVID-19 pandemic, witnessing a widespread downturn across nearly all sectors. In response to this economic strain, one potential avenue for bolstering or preserving individual welfare levels lies in investment strategies. This study scrutinises the impact of various sociodemographic factors on individuals' decisions to invest in gold amidst the COVID-19 crisis. Specifically, gender, age, education, occupation, income, and risk tolerance are examined to discern their influence on gold investment decisions. Conducted within Bengkulu City, the study engages with 190 respondents and employs multiple regression techniques to illuminate key insights. This research reveals a nuanced interplay between sociodemographic variables and gold investment choices during the pandemic. Gender, age, income, and risk tolerance are significant influencers, exerting discernible effects on individuals' inclinations toward gold investment. Notably, the analysis unveils that gender and age dynamics, alongside income disparities, play pivotal roles in shaping investment behaviours, with certain demographics exhibiting a heightened propensity for gold investment in the face of economic uncertainty. Conversely, the impact of education and occupation on gold investment decisions appears less pronounced within the context of Bengkulu City during the pandemic. While these variables undoubtedly shape individuals' financial dispositions, their direct influence on the inclination toward gold investment seems less substantial amidst the prevailing economic challenges. In essence, this study underscores the multifaceted nature of investment decision-making during times of crisis, shedding light on the intricate interplay between sociodemographic factors and investment behaviours. By unravelling these dynamics, policymakers, and investors can glean valuable insights into crafting tailored strategies to navigate economic turbulence and safeguard individual welfare amid uncertainty.

Monte Carlo Evaluation of Uncertainties of UV Spectra Measured With Brewer Spectroradiometers

AbstractPrecise spectral ultraviolet (UV) measurements are needed to ensure human protection as well as to support scientific research. Quantifying the uncertainty of the UV spectra recorded is crucial to evaluate the quality of the measurements which is needed, in turn, for the assessment of their reliability. However, for double‐monochromator spectroradiometers, the analytical derivation of this uncertainty is a challenging task due to the difficulties involved in propagating individual uncertainties. Under these circumstances, a Monte Carlo simulation is a reliable alternative as it does not require the calculation of partial derivatives and considers both nonlinear effects and correlations in the data. In the present study, the uncertainty of the spectral UV irradiance measured by a Brewer MKIII spectrophotometer is evaluated using a Monte Carlo approach. This instrument belongs to the National Institute of Aerospace Technology and has successfully participated in several international campaigns, which ensures its precise calibration. The average expanded uncertainty (k = 2) of the global UV irradiance measured by this instrument varies between 10% at 300 nm and 7% at 363 nm. At shorter wavelengths, it increases sharply due to thermal and electronic noise as well as wavelength misalignment. The results indicate that a Brewer spectrophotometer is suitable for climatological studies and model validation. Nevertheless, a substantial reduction of these uncertainties might be required for accurately detecting long‐term UV trends. Although the study focused on a Brewer spectrometer, the methodology used for the uncertainty analysis is general and can be adapted to most UV spectroradiometers.

A review on sources of uncertainties for groundwater recharge estimates: insight into data scarce tropical, arid, and semiarid regions

Abstract Successful sustainable groundwater management requires accurate information on recharge for a given aquifer system. However, recharge estimates are usually used in relative terms rather than an absolute sense. A review of available studies on groundwater recharge estimate uncertainty as well as tools for uncertainty analysis was conducted. Nonetheless, except for the handful of studies that have conducted proper uncertainty analysis, most were inclined to implement multiple methods as an indication of the range of uncertainty. The global trend indicates that considering the significant number of methods for recharge estimation, very little has been done to assess the uncertainty of each method. Therefore, more attention should be given to the individual uncertainty analysis of selected methods as much as using multiple methods recommended for investigating uncertainty. Insight from the review indicates that, when used carefully, tracer-based analysis can be effective and coupling is required for uncertainty analysis. Furthermore, spatial uncertainty due to input data could potentially be minimized by using input data from multiple sources. Better conceptualization of the hydrogeological process can reduce the uncertainty of numerical modelling. This review is limited to widely used methods and excludes uncertainty due to inappropriate method implementation and controlled experimental uncertainties.

Belief in Superstition and Intolerance in Conditions of Uncertainty

The condition of intolerance to uncertainty in individuals arises when there is a possibility of a bad situation or having a risky future. Therefore, many individuals try to reduce these adverse effects that may occur in the future by believing in superstitions. This research will reveal whether there is a superstitious belief in intolerance in uncertain conditions. Data will be analyzed using Pearson Product Moment involving 395 subjects selected through quota sampling. The results showed that there was a relationship between superstitious beliefs and intolerance of uncertainty with a correlation strength of 0.423. This means that when an individual is in an uncertain situation, it can trigger the presence of intolerance of uncertainty. On the other hand, the presence of superstitious belief is believed to have existed in the individual and tends to be used as a protector in making decisions.