Errors In Judgment Research Articles

Working the Crowd: Citizen Forecasting, Sophistication and Diversity in Canadian Federal and Provincial Elections

Abstract According to the “miracle of aggregation” principle, in the absence of systematic biases, errors in individual judgments within a population should cancel each other out and lead to a correct decision at the aggregate level. This article explores potential individual- and group-level correlates of the accuracy of citizens’ electoral expectations and investigates how potential markers of political sophistication—namely, educational attainment and political interest—could be used to improve upon the raw aggregation of citizens’ forecasts using massive survey datasets collected during six Canadian national and provincial election campaigns between 2011 and 2022 (n = 279,003). We find that while educational attainment and interest increase the probability of a correct forecast at the individual level, delegating the forecasting task based on these variables does not necessarily lead to improvements in the accuracy of aggregate-level predictions. At the group level, we fail to uncover any evidence that sociological or informational diversity increases forecasting accuracy.

Intrinsic judgment error in men’s championship world surf league: WSL 2021

Introduction: Surfers’ performances are subjectively ranked by 5 judges. Low reliability and validity in judgment may lead to preventable errors and unfair scores. Objective: The aims of the present study is to describe the judgment error for each wave surfed in world surf league (WSL) championship tour of 2021, establish between-judge reliability, and establish judgment validity comparing each judge score with the final control score of the wave. Methodology: To describe the judgment error, we analyzed the available WSL data related to the 2021 Men’s Championship Tour (4,095 waves; 20,475 scores). Results: We found an inverted ‘U’-shaped pattern for the judgment error score vs. control score, explained by a quadratic regression model (R = 0.52; SEE = 0.10). The reliability produced an excellent intraclass correlation coefficient (CI95% = 0.97, 1.00), with a between judge (typical) error of 0.15. Validity analyses indicated a minimal real difference of 0.49 in the sum of two waves between the surfers for having 95% certainty for the heat winner. Conclusion: We recommend WSL incorporate intrinsic judgment errors to increase fairness and trust in the WSL championship tour.

Sensorimotor adaptation reveals systematic biases in 3D perception

The existence of biases in visual perception and their impact on visually guided actions has long been a fundamental yet unresolved question. Evidence revealing perceptual or visuomotor biases has typically been disregarded because such biases in spatial judgments can often be attributed to experimental measurement confounds. To resolve this controversy, we leveraged the visuomotor system’s adaptation mechanism — triggered only by a discrepancy between visual estimates and sensory feedback — to directly indicate whether systematic errors in perceptual and visuomotor spatial judgments exist. To resolve this controversy, we leveraged the adaptive mechanisms of the visuomotor system to directly reveal whether systematic biases or errors in perceptual and visuomotor spatial judgments exist. In a within-subject study (N=24), participants grasped a virtual 3D object with varying numbers of depth cues (single vs. multiple) while receiving haptic feedback. The resulting visuomotor adaptations and aftereffects demonstrated that the planned grip size, determined by the visually perceived depth of the object, was consistently overestimated. This overestimation intensified when multiple cues were present, despite no actual change in physical depth. These findings conclusively confirm the presence of inherent biases in visual estimates for both perception and action, and highlight the potential use of visuomotor adaptation as a novel tool for understanding perceptual biases.

Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network

The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.

Analysis implementation system control internal government in Indonesia

Law No. 1 of 2004 concerning the Treasury of the State shall preserve the President as the Head of Government regulating and organizing the Government's Internal Control System in Indonesia. In 2008, the Government of Indonesia issued Government Regulation Number 60 of 2008 concerning the Government's Internal Control System to implement the provisions of Article 58 paragraph (2) in Law Number 1 of 2004. The internal control system adopted in Indonesia was adopted from the concept of internal control was made by the 1992 Committee of Sponsoring Organizations of the Treadway Commission (COSO). Since the enactment of Government Regulation Number 60 of 2008 counted for 10 years the regulation has come into force. However, the policy contained in Government Regulation Number 60 Year 2008 has not been fully implemented in government governance in Indonesia. This can be seen from the results of the government internal control system maturity evaluation conducted by BPKP until March 2018 which shows the maturity level of SPIP which is still far from the target with the majority of Institutions still below level 3 or far from level 5 (optimum level) and weaknesses of the Internal Control System The government found by the Supreme Audit Agency in the examination of 2015-2017. Factors that can hinder the implementation of the government's internal control system in Indonesia include: (a) Weakness in terms of content and context of the Policy, (b) Absence of sanctions, (c) Inadequate judgment (d) Errors in translating orders, (e) Management neglect, (f) Collusion.

How human-AI feedback loops alter human perceptual, emotional and social judgements.

Artificial intelligence (AI) technologies are rapidly advancing, enhancing human capabilities across various fields spanning from finance to medicine. Despite their numerous advantages, AI systems can exhibit biased judgements in domains ranging from perception to emotion. Here, in a series of experiments (n = 1,401 participants), we reveal a feedback loop where human-AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans. This amplification is significantly greater than that observed in interactions between humans, due to both the tendency of AI systems to amplify biases and the way humans perceive AI systems. Participants are often unaware of the extent of the AI's influence, rendering them more susceptible to it. These findings uncover a mechanism wherein AI systems amplify biases, which are further internalized by humans, triggering a snowball effect where small errors in judgement escalate into much larger ones.

Standard Setting for Assessments in Anatomy using the Angoff Method in a Medical School in Sri Lanka

Introduction: Anatomy is a fundamental subject in medical education. A reliable and valid assessment tool is crucial for evaluating students' understanding. The Angoff method, an absolute method of standard-setting has been widely used in various educational contexts. This study aimed to develop and implement a standard-setting process for an assessment in Anatomy using the Angoff method in a Sri Lankan medical school. Methods: Five Anatomy faculty members including two senior lecturers and three temporary assistant lecturers who were recent graduates participated in the standard-setting process. They were presented with each item on the anatomy assessment and asked to estimate the percentage of minimally competent students who would answer the item correctly. Their judgments were then used to determine the cut score for the assessment. Results: The Angoff method yielded a cut score of 56.06 with a cutoff value of 8.41 and a standard error of judgment of 0.18 (1.2%). There was a high interrater reliability (0.89). Conclusion: This study demonstrated the feasibility and effectiveness of using the Angoff method for standard setting in an anatomy assessment in a Sri Lankan medical school. The set cut score provides a benchmark for evaluating students' performance and ensuring that they possess the necessary anatomical knowledge to progress in their medical education.

Cognitive biases in Mohs micrographic surgery.

Mohs surgeons' fallibility is an important topic to explore. There are no studies on the role of cognitive biases in heuristic decision-making within Mohs micrographic surgery (MMS) as a contributing factor in mistakes or errors of clinical judgement. We reviewed established cognitive biases applied to heuristics during the pre-operative, intraoperative and reconstruction stages of MMS, and recommend conscious cognitive bias mitigating strategies for Mohs surgeons. Clinical errors/incidents may be reframed as opportunities to learn and develop, rather than evidence of competency or status. These experiences offer the opportunity to sharpen skills within metacognition and allow for more cognizant Mohs surgeons. We highly recommend all Mohs surgeons develop in-depth knowledge and maintain awareness of cognitive biases throughout clinical practice, to enhance clinical performance.

Enhancing Talent Intelligence Evaluation with Improved Pre-training Models

In the past few years, there has been a noticeable surge in the utilization of artificial intelligence techniques within the realm of talent evaluation, particularly in response to the exponential growth of big data. Conventional talent evaluation approaches, typically relying on the integration of expertise and system-based title appraisal, are plagued with challenges including errors in subjective judgment. The strategic importance of intelligent talent assessment cannot be overemphasized, especially for discovering and fostering high-potential talent across various domains, inclusive of research. This paper introduces an innovative method, termed TE-RCB, auxiliary applications in the evaluation of actual talent titles. The initial stage of this method’s execution involves the meticulous construction of a talent evaluation dataset. This data is sourced from actual talent title applications and standardized scores provided by simulation expert engineers. The subsequent step is the application of the RoBERTa-WWM-large model, employed for the vectorization of pertinent information indispensable for talent evaluation. Further, the TextCNN model is utilized for the extraction of critical features from the talent-related information, succeeded by the application of the BiLSTM model to delve into deeper semantic correlations within the talent attributes. Comprehensive experiments are conducted on both the talent evaluation dataset and the publicly available dataset. The empirical outcomes decisively underline the superior accuracy of the proposed method, asserting its efficacy in addressing the pertinent challenges in the domain of intelligent talent assessment.

Fuzzy RANCOM: A novel approach for modeling uncertainty in decision-making processes

Multi-Criteria Decision Analysis (MCDA) methods are essential for addressing real-world challenges in the dynamic field of decision support technology. As technology advances, the expanding variety of multi-criteria evaluation methods highlights the critical importance of method selection for reliable results. These methods can be applied to scenarios involving both precise data and uncertainties. Expert knowledge is relevant in navigating real-world decision problems, particularly in uncertain environments where judgment inaccuracies can create challenges. Determining criteria weights is a pivotal step, as it significantly influences final outcomes and provides a nuanced understanding of each factor's impact on the evaluation.This paper addresses the pressing need for methods that account for uncertainty and effectively reduce the impact of inaccuracies in expert judgment. To this end, we introduce the Fuzzy RANking COMparison (RANCOM) method, a novel fuzzy subjective weighting approach that extends the advantages of the original RANCOM method to manage uncertainty. Using Triangular Fuzzy Numbers (TFNs), the proposed extension maintains simplicity, rapid execution, and flexibility in establishing criteria relationships. Key contributions of this paper include the development of the Fuzzy RANCOM method and a comparative analysis with the Fuzzy Analytical Hierarchy Process (AHP) method. Through multiple simulations, the study examines the operational dynamics of these methods, offering a valuable tool for decision-makers, especially in contexts where expert judgments may be prone to inaccuracies. The development of the Fuzzy RANCOM method provides a robust solution, equipping decision-makers with an effective tool to determine criteria weights while minimizing the impact of potential judgment errors.

Temporal error monitoring: Does agency matter?

Error monitoring is the ability to report one's errors without relying on feedback. Although error monitoring is investigated mostly with choice tasks, recent studies have discovered that participants parametrically also keep track of the magnitude and direction of their temporal, spatial, and numerical judgment errors. We investigated whether temporal error monitoring relies on internal generative processes that lead to the to-be-judged first-order timing performance. We hypothesized that if the endogenous processes underlie temporal error monitoring, one can monitor timing errors in emitted but not observed timing behaviors. We conducted six experiments to test this hypothesis. The first two experiments showed that confidence ratings were negatively related to error magnitude only in emitted behaviors, but error directionality judgments of observed behaviors were more precise. Experiment 3 replicated these effects even after controlling for the motor aspects of first-order timing performance. The last three experiments demonstrated that belief of agency (i.e., believing that the error belongs to the self or someone else) was critical in accounting for the confidence rating effects observed in the first two experiments. The precision of error directionality judgments was higherin thenon-agency condition. These results show that confidence is sensitive to belief, and short-long judgment is sensitive to the actual agency of timing behavior (i.e., whether the behavior was emitted by the self or someone else).

Emotional aftermath of the 2020 U.S. presidential election: a study of hindsight bias in younger and older adults

ABSTRACT Hindsight bias – also known as the knew-it-all-along effect – is a ubiquitous judgment error affecting decision makers. Hindsight bias has been shown to vary across age groups and as a function of contextual factors, such as the decision maker’s emotional state. Despite theoretical reasons why emotions might have a stronger impact on hindsight bias in older than in younger adults, age differences in hindsight bias for emotional events remain relatively underexplored. We examined emotion and hindsight bias in younger and older adults (N = 272) against the backdrop of the 2020 U.S. presidential election. Participants predicted electoral college votes for the two presidential candidates before the election and were asked to remember their predictions approximately three weeks later, after the election results had been finalised. Republicans, for whom the electoral outcome was negatively tinged, exhibited greater hindsight bias for President Biden’s result compared with Democrats, for whom the electoral outcome was positive. The asymmetry in hindsight bias between Republicans and Democrats was similar for younger and older participants. This study suggests that negative emotions may exacerbate hindsight bias, and that adult age differences in hindsight bias observed in laboratory settings may not translate to real-world contexts.

Debiasing Judgements Using a Distributed Cognition Approach: A Scoping Review of Technological Strategies.

To review and synthesise research on technological debiasing strategies across domains, present a novel distributed cognition-based classification system, and discuss theoretical implications for the field. Distributed cognition theory is valuable for understanding and mitigating cognitive biases in high-stakes settings where sensemaking and problem-solving are contingent upon information representations and flows in the decision environment. Shifting the focus of debiasing from individuals to systems, technological debiasing strategies involve designing system components to minimise the negative impacts of cognitive bias on performance. To integrate these strategies into real-world practices effectively, it is imperative to clarify the current state of evidence and types of strategies utilised. We conducted systematic searches across six databases. Following screening and data charting, identified strategies were classified into (i) group composition and structure, (ii) information design and (iii) procedural debiasing, based on distributed cognition principles, and cognitive biases, classified into eight categories. Eighty articles met the inclusion criteria, addressing 100 debiasing investigations and 91 cognitive biases. A majority (80%) of the identified debiasing strategies were reportedly effective, whereas fourteen were ineffective and six were partially effective. Information design strategies were studied most, followed by procedural debiasing, and group structure and composition. Gaps and directions for future work are discussed. Through the lens of distributed cognition theory, technological debiasing represents a reconceptualisation of cognitive bias mitigation, showing promise for real-world application. The study results and debiasing classification presented can inform the design of high-stakes work systems to support cognition and minimise judgement errors.

Failure in Fully Funded Pensions: Voluntary Exchange and Coercion

Private pension failure for much of the economics mainstream would be regarded as an artifact of markets that are insufficiently regulated, such that suppliers are able to exploit monopoly advantages, and are free to make serious errors of judgment. Market failure analysis is flawed, however, by its own failure to acknowledge salient elements of the institutional context of private pension provision, which, while it has been regulated according to approved public interest ends, has been prone to suboptimal investment performance. Drawing on classical liberal political economy, an alternative account of private pension failure would emphasize the perverse consequences of state intrusion in the market for retirement income protection. The origins of regulation failure can be traced back to a combination of two policy decision dynamics, one of intentional rent creation, a second of imperfect knowledge and understanding of markets resulting in flawed regulatory arrangements. By stifling competition, regulation diminishes the capacity of pension markets to serve pension plan participants by means of improved investment performance. The prevalence of private pension failure is an artifact of regulatory intrusiveness. Given this direction of causality, a better approach to policy would rest centrally on the deregulation of pension markets.

Complications: skull base and cerebrovascular.

Neurosurgical complications are a rich source for learning, but they are grossly underutilized for the purpose of surgeon education. Details of the complications, which make them all the more powerful as teaching tools, are restricted to morbidity and mortality conferences behind closed doors, and open discussions of the topic are blurred by hypotheticals in order to shield the presenters from medicolegal risks. In this issue of Neurosurgical Focus, 9 neurosurgeons were invited to present complications they encountered along with the details and specific lessons they learned. The contributors were picked for their well-known track record of skill, experience, and candor, so readers can be confident in the lessons. The video and the accompanying written article present the clinical facts. The actions that led to the complications are demonstrated in the surgical video segments. In ancient Western civilization, the seven "deadly sins" categorized and conceptualized human malfeasance, and similarly, the neurosurgical correlate of this set of sins can provide a framework for discussing the errors that lead to unexpectedly poor outcomes. Although errors in judgment and planning grow rarer with experience, errors in execution can still occur, no matter the stage of one's career. Interestingly, even though skill and experience do not eliminate complications, they may affect the lesson that the neurosurgeon takes home. The lack of open discourse on complications may slow progress in the field and set the stage for repeating mistakes. The writing and production teams hope that this work opens a rich source of learning. The video can be found here: https://stream.cadmore.media/r10.3171/2024.7.FOCUS24379.

Artificial intelligence in the radiological diagnosis of cancer

Artificial intelligence (AI) is being used to diagnose deadly diseases such as cancer. The possible decrease in human error, fast diagnosis, and consistency of judgment are the key incentives for implementing these technologies. Therefore, it is of interest to assess the use of artificial intelligence in cancer diagnosis. Total 200 cancer cases were included with 100 cases each of Breast and lung cancer to evaluate with AI and conventional method by the radiologist. The cancer cases were identified with the application of AI-based machine learning techniques. The sensitivity and specificity check-up was used to assess the effectiveness of both approaches. The obtained data was statistically evaluated. AI has shown higher accuracy, sensitivity and specificity in cancer diagnosis compared to manual method of diagnosis by radiologist.

Minimum loss modulation method for various power factor angles

In recent years, the energy crisis has prompted widespread attention to electric vehicles (EVs). As a core component of electric drive system in EV, motor controller has a significant impact on vehicle energy consumption. In such situation, this paper proposes a minimum loss modulation method for all power factors and reduces current THD by dead-band compensation. Firstly, the mechanism model of the controller is established for loss calculation, and the relationship between the clamping region and the current peak is analyzed based on the traditional modulation methods. Secondly, a discontinuous pulse width modulation considering power factor angle method (CPFA-DPWM) is proposed to ensure that clamping region always falls at the peak current at all power factors. Furthermore, the adoption of current vector method instead of direct sampling for current polarity determination effectively reduces judgment error and avoids false dead-band compensation. The effectiveness of the proposed method is verified by simulation and thermal measurement experiment. Under China Light-duty Vehicle Test Cycle (CLTC), the controller loss of CPFA-DPWM is reduced by 18.61 % compared to SVPWM.

The power of words: the impact of police interviewer’s judgment error and apology on sexual violence victims in simulated interviews

This experiment examines how a police interviewer’s judgment error and apology affect a sexual violence victim’s secondary victimization, trust in the interviewer, rapport, and willingness to provide information. Ninety students from Peru were asked to imagine being sexual violence victims and were interviewed online by a mock police interviewer. Participants were randomized into three conditions: judgment error without apology, judgment error with apology, and a no-error control. The judgment error involved suggesting the victim was partly responsible for their victimhood, citing women’s clothing choices and questioning men’s masculinity. Our findings show judgment errors reduced trust, rapport, and willingness to provide information while increasing secondary victimization; apologizing improved trust and rapport but did not affect secondary victimization or willingness to provide information. These outcomes indicate that judgment errors in sexual violence victim interviews can negatively impact the interviewing process and outcomes and highlight the importance of an apology.

Not our kind of crowd! How partisan bias distorts perceptions of political bots on Twitter (now X).

Social bots, employed to manipulate public opinion, pose a novel threat to digital societies. Existing bot research has emphasized technological aspects while neglecting psychological factors shaping human-bot interactions. This research addresses this gap within the context of the US-American electorate. Two datasets provide evidence that partisanship distorts (a) online users' representation of bots, (b) their ability to identify them, and (c) their intentions to interact with them. Study 1 explores global bot perceptions on through survey data from N = 452 Twitter (now X) users. Results suggest that users tend to attribute bot-related dangers to political adversaries, rather than recognizing bots as a shared threat to political discourse. Study 2 (N = 619) evaluates the consequences of such misrepresentations for the quality of online interactions. In an online experiment, participants were asked to differentiate between human and bot profiles. Results indicate that partisan leanings explained systematic judgement errors. The same data suggest that participants aim to avoid interacting with bots. However, biased judgements may undermine this motivation in praxis. In sum, the presented findings underscore the importance of interdisciplinary strategies that consider technological and human factors to address the threats posed by bots in a rapidly evolving digital landscape.

Probability errors in children's judgements about the likelihood of social characteristics.

The studies reported here investigated mechanisms underlying children's tendency to commit the conjunction fallacy (judging that a conjunction of two events is more likely than one of the events in isolation) when judging people's characteristics. Study 1 investigated these errors in 4- and 5-year-olds (N = 58) using a newly developed social judgement task in which children judged whether a conjunction or one of its elements would apply to a protagonist. Children made conjunction fallacy errors at chance level. Study 2 (N = 71) replicated these findings using an adapted version of the task, in which children separately judged the likelihood of the conjunction and each of its events. Study 3 investigated age-related changes in conjunction fallacy errors in a sample of 148 children aged 4 to 11 years old and 130 adults. This study also investigated how providing background information on the protagonist influenced error rate. Unlike younger children, 10- and 11-year-olds committed the conjunction fallacy at chance level in the absence of background information, but providing information consistent with the likely component of the conjunction significantly increased their error rate. Adults' error rate also significantly increased after the introduction of background information. Across all three studies, conjunction fallacy errors were unrelated to cognitive and social-cognitive abilities, such as verbal ability, theory of mind, and inhibitory control (Studies 1 and 2), and prejudice and hindsight bias (Study 3). These findings suggest that it is only in the second decade of life that children use social information to inform their judgements about people and that social decision-making errors are not determined by core aspects of cognitive and social-cognitive development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).