Decision Errors Research Articles

Peran Saksi dalam Persidangan sebagai Pendorong Keadilan: Studi Kasus Pencurian Sepeda Motor

This research examines the role of witnesses in motorbike theft cases which ended in peaceful resolution through a restorative justice approach. The main focus lies on how witnesses can influence the trial and mediation process in an effort to achieve balanced justice between the perpetrator and the victim. The method used is a normative juridical approach, which refers to legal studies and related documents to understand the role of witnesses as evidence and promoters of justice. The research results show that witness testimony plays an important role in revealing facts, supporting the mediation process, and preventing decision-making errors in court. Witnesses also function as moral monitors who ensure that restorative justice is carried out transparently and protects the rights of victims. This research suggests the need for systematic integration of restorative justice in the legal system, better witness protection, and public education regarding the importance of the role of witnesses in the judicial process.

Algorithmic solutions, subjectivity and decision errors: a study of AI accountability

Algorithmic solutions, subjectivity and decision errors: a study of AI accountability

Disentangling decision errors from action execution in mouse-tracking studies: The case of effect-based action control.

Mouse-tracking is regarded as a powerful technique to investigate latent cognitive and emotional states. However, drawing inferences from this manifold data source carries the risk of several pitfalls, especially when using aggregated data rather than single-trial trajectories. Researchers might reach wrong conclusions because averages lump together two distinct contributions that speak towards fundamentally different mechanisms underlying between-condition differences: influences from online-processing during action execution and influences from incomplete decision processes. Here, we propose a simple method to assess these factors, thus allowing us to probe whether process-pure interpretations are appropriate. By applying this method to data from 12 published experiments on ideomotor action control, we show that the interpretation of previous results changes when dissociating online processing from decision and initiation errors. Researchers using mouse-tracking to investigate cognition and emotion are therefore well advised to conduct detailed trial-by-trial analyses, particularly when they test for direct leakage of ongoing processing into movement trajectories.

Capturing uncertainty intuition in road maintenance decision‐making using an evidential neural network

AbstractDecision‐making of project‐level road maintenance is the process of mapping road information into a maintenance plan. Even though benefitting from deep learning, the decision‐making still faces the problem of maintenance data uncertainty. The data uncertainty derives from imperfect road information collection and arbitrary selection of maintenance plans. Such uncertainty always leads to unreasonable maintenance decision‐making. This study proposes an evidential approach using information entropy (IE) and Dempster–Shafer theory (DST) to capture and handle uncertainty in the decision‐making of project‐level road maintenance. The approach first uses an IE‐based judgment method (IE‐based method) to capture and observe quantitative data uncertainty. The DST‐based method is then developed to handle maintenance data uncertainty through utilizing evidential neural network and set‐valued decision‐making. A numerical experiment is performed on the maintenance data with 280 km of semi‐rigid base highways in China. The results indicate that the IE‐based method can measure the data uncertainty in the information of road sections. The DST‐based method captures the cautious intuition on the selection of maintenance plans, thereby reducing the decision error rate by over 14% under specific conditions when facing data uncertainty.

Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods.

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI's ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as 'artificial intelligence', 'machine learning', 'sports injury', and 'risk prediction'. While AI's predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.

Applications and Advances of UCB Algorithms in Dynamic and Contaminated Environments

The Upper Confidence Bound (UCB) algorithm is a widely used approach in the Multi-Armed Bandit (MAB) problem, where the goal is to maximize cumulative rewards over time by selecting the best possible action among several options. The UCB algorithm uses confidence bounds to balance exploration and exploitation, guiding its decision-making. In recent years, researchers have identified significant challenges in applying the UCB algorithm to dynamic and contaminated environments. In such scenarios, the underlying conditions may change over time, making it difficult for standard UCB to adapt, or the data may be polluted by noise and outliers, leading to incorrect estimations of reward distributions. To address these challenges, several variants of the UCB algorithm have been developed. These new approaches are designed to better handle the complexities of changing environments and data contamination, ensuring more robust and reliable performance in these difficult settings. This paper aims to provide a comprehensive review of Robust-UCB (cr-UCB), Sliding Window UCB (SW-UCB) and bandit-over-bandit UCB (BOB-UCB). Focusing on their theoretical foundations, practical applications, and empirical performance. By examining how these algorithms have been adapted to handle the complexities of dynamic and contaminated environments, we found that the adaptability of these algorithms in dynamic environments is significantly improved, and they can effectively reduce decision-making errors caused by data pollution, thus providing a more reliable solution to the multi-armed bandit problem.

Late-life psychiatric factors and life satisfaction are associated with cognitive errors: evidence from an experimental module of a large-scale survey in India

Older adults are at risk of committing cognitive and decision-making errors due to the decline in cognitive functions. However, the understanding of the determining factors of cognitive errors among ageing adults is limited. In this study, we explored the role of various psychiatric factors, life satisfaction, and other socioeconomic, health and behavioural risk factors of committing cognitive errors among middle-aged and older adults in India. The study utilized the data from the experimental module of the Longitudinal Ageing Study in India (LASI) Wave-1 (2017–2018) with a sample of 12,754 adults aged 45 years and above. The cognitive error was measured using logical fallacies committed in the activity-based experiments. The study employed descriptive, bivariate statistics and multivariable logistic regression models to identify the factors associated with cognitive errors among the study participants. Depression (aOR = 1.28, 99%, CI: 1.16–1.41), life satisfaction (aOR = 0.99, 99%, CI: 0.98–1.00), and cognitive impairment (aOR = 1.13, 90% CI: 1.00–1.30) self-reported psychiatric) were significantly associated with higher odds of committing cognitive errors among the middle-aged and older adults. Also, ageing adults with low educational levels, functional limitations, sleep disturbances, smoking history, living in rural areas and belonging to scheduled tribes had a higher probability of committing cognitive errors. However, involvement in physical activity, reading habits and social interactions reduced the odds of cognitive errors among this sample. Mental health and well-being indicators, including depression, life satisfaction, cognitive impairment, and other health and behavioural health factors, determine cognitive errors among ageing adults. Programs and policies should be initiated to address these factors, reduce cognitive errors, and ensure active ageing.

On the decision-making framework for policing: A proposal for improving police decision-making

This article introduces the Decision-Making Framework for Policing (DMFP), a comprehensive tool designed to enhance the decision-making understanding of police officers. The DMFP considers the principles of heuristic, naturalistic, and rational decision-making along a fluid cognitive continuum to create a framework that addresses the limitations of the existing police National Decision Model (NDM). It achieves this by including 10 proposed typologies of police decision-making including: Routine, Tactical, Operational, Crisis, Investigative, Ethical, Interpersonal, Administrative, Managerial, and Strategic. These are integrated alongside existing and adapted decision-making models which are presented using a mnemonic letter strategy. Although the DMFP is theoretical, and its utility is presently untested in comparison to the existing NDM, it is presented to provide a tool to help improve officers' tacit knowledge, pattern recognition, and experiential learning through provision of easily recallable mnemonic decision-models. Thereby fostering a deeper understanding of cognitive processes and the factors influencing police decisions, potentially increasing consistency in reasoning, reducing decision errors, and enhancing policing outcomes.

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Age of information optimal UAV swarm-assisted sweep coverage in wireless sensor networks

For sweep coverage in wireless sensor networks (WSNs), the freshness of data directly affects the efficiency of task execution, and time is needed to continuously cover points of interest (POIs) to ensure obtaining all the data. However, existing studies ignored both. The outdated data and missing data may lead to decision-making errors, resulting in significant losses. To address this issue, this paper proposes a simultaneous sweep mode and a batch sweep mode of Unmanned Aerial Vehicle (UAV) swarm to achieve sweep coverage in WSNs, considering the freshness of data and the continuous coverage time of POIs, where the age of information (AoI) is adopted to measure the freshness of data. The target is to minimize the average AoI of POIs under the continuous coverage time constraint and the constraints of UAV swarm. Firstly, the POIs are clustered to obtain the best sweep points. Then, the UAV swarm sweep coverage (USSC) algorithm is designed for the two sweep modes. Finally, various simulations are conducted to verify the performance of the USSC algorithm. Simulation results show that the USSC algorithm can effectively minimize the average AoI compared to baseline algorithms. The script of the proposed algorithm can be found from:https://github.com/lilibeat/USSC.

Methodological foundations of somatoscopic research in automated decision support systems

Automated Decision Support Systems (ADSS) are widely applied in various fields of science and technology, particularly in medicine, where their role in diagnostic, prognostic, and therapeutic processes is undeniable. The use of computational technologies in medical practice has become a necessary stage in the development of the field; however, the increasing volume of data that requires processing, along with heightened demands for accuracy, speed, and reliability of recommendations, significantly complicates decision-making processes. The growing amount of data, combined with the need to minimize the risks of erroneous decisions, drives the scientific community to seek innovative information technologies that can ensure a high level of accuracy in computational operations while minimizing the time required for their execution. Somatoscopic measurements, which are a crucial element in assessing the condition of the musculoskeletal system and overall physical posture of a patient, require precise and systematic image analysis that allows the identification of critically important anatomical markers. This process involves the accurate determination of key points on images of anatomical structures, which serves as the foundation for making precise predictions about the patient's condition and subsequent treatment planning. In the context of continuously increasing volumes of medical data and stricter requirements for the quality of decisions, the implementation of innovative technologies, particularly deep learning, becomes increasingly relevant as it enhances the processes of analysis and diagnosis. The aim of this study is to conduct a comprehensive analysis of the methodological aspects of applying deep learning in the context of automating the diagnosis of the musculoskeletal system and posture assessment. The study emphasizes the use of specialized neural network architectures that enable the identification of key anatomical points on images, thereby facilitating a deeper and more accurate analysis of anatomical structures. This approach significantly improves the efficiency of diagnostic processes by minimizing the likelihood of errors in decision-making and optimizing the performance of medical systems. The results of the conducted research demonstrate the significant potential of deep learning algorithms in medical systems for the automated analysis of images, allowing for substantial improvements in the accuracy and speed of decision-making. Such automation contributes to reducing the risk of subjective errors associated with the human factor, which is particularly important in complex clinical cases. Therefore, the further development of research in this direction is of paramount importance for the medical field, as it opens new opportunities for solving complex diagnostic tasks at an innovative technological level. The integration of deep learning technologies into the processes of extracting somatoscopic data not only enhances diagnostic efficiency but also creates prerequisites for the development of new decision support systems that optimize medical practice.

Examining flight time, cognitive reflection, workload, stress and metacognition on decision making performance for pilots during flight simulation

Despite technological advancements, human decision errors still contribute to civil aviation accidents. This study investigated whether flight time, cognitive reflection, task-load, metacognition, and perceived stress predicted decision-making (DM) performance during two in-flight training simulations with 104 commercial pilots at Bogota International Airport. Hierarchical regression analysis revealed that the predictors accounted for 56% of the variance. Cognitive reflection, flight time and performance task load emerged as significant positive predictors. Cognitive reflection significantly moderated the relationship between flight time and DM performance, with pilots scoring lower on cognitive reflection showing improved DM with increased flight time, while controlling for performance task load. The study did not find significant relationships between stress metacognition and DM performance. The study emphasises the significance of advanced training methods in improving pilots’ DM, especially for those with low cognitive reflection. Future research should expand to multiple airlines, address gender balance, and incorporate direct measures of metacognitive monitoring.

An integrative literature review on clinical decision-making of novice nurses for disaster management in the Emergency Room

Novice nurses play a workflow part in responsibility among an overwhelming of patient. This emphasizes the need for nurses to have comprehensive decision-making skills, especially in disaster management. However, in practice, clinical decision-making remains a challenging issue for novice nurses working in the Emergency Room (ER). This review was conducted to synthesize the strategy that novice nurses need to do in clinical decision-making in the ER during a disaster.This review used an integrative literature review approach to identify (n=16) relevant scientific articles. Six online databases (ScienceDirect, Proquest, Sage Journals, PubMed, Willey Library, and EBSCO) were utilized to identify articles published between 2013 and 2023. The gathered articles were selected using the PRISMA method by removing duplicate articles and those that did not align with the review's objectives. The PRISMA flowchart was used to illustrate the evidence search process. Out of a total of 2,264 identified articles, sixteen articles are relevant for review.The review revealed that clinical decision-making requires careful consideration in several aspects which focused on determining the priorities of triage, assessment, diagnostic skills, and communication skills in teamwork during a disaster in the ER. This review provides evidence that the accuracy of clinical decision-making is a major influence on patient outcomes during disaster management.In conclusion, comprehensive training and more education for novice nurses about disaster precision nursing in the future are needed to reduce errors in decision-making.

Calibration and Modeling of the Semmes-Weinstein Monofilament for Diabetic Foot Management.

Diabetic foot is a serious complication that poses significant risks for diabetic patients. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent ulceration and ultimately amputation. The primary method employed for evaluating this sensitivity loss is the 10 gf Semmes-Weinstein monofilament test, commonly used as a first-line procedure. However, the lack of calibration in existing devices often introduces decision errors due to unreliable feedback. In this article, the mechanical behavior of a monofilament was analytically modeled, seeking to promote awareness of the impact of different factors on clinical decisions. Furthermore, a new device for the automation of the metrological evaluation of the monofilament is described. Specific testing methodologies, used for the proposed equipment, are also described, creating a solid base for the establishment of future calibration guidelines. The obtained results showed that the tested monofilaments had a very high error compared to the 10 gf declared by the manufacturers. To improve the precision and reliability of assessing the sensitivity loss, the frequent metrological calibration of the monofilament is crucial. The integration of automated verification, simulation capabilities, and precise measurements shows great promise for diabetic patients, reducing the likelihood of adverse outcomes.

Integration of prediction and optimization for smart stock portfolio selection

Machine learning (ML) algorithms pose significant challenges in predicting unknown parameters for optimization models in decision-making scenarios. Conventionally, prediction models are optimized independently in decision-making processes, whereas ML algorithms primarily focus on minimizing prediction errors, neglecting the role of decision-making in downstream optimization tasks. The pursuit of high prediction accuracy may not always align with the goal of reducing decision errors. The idea of reducing decision errors has been proposed to address this limitation. This paper introduces an optimization process that integrates predictive regression models within a mean–variance optimization setting. This innovative technique introduces a general loss function to capture decision errors. Consequently, the predictive model not only focuses on forecasting unknown optimization parameters but also emphasizes the predicted values that minimize decision errors. This approach prioritizes decision accuracy over the potential accuracy of unknown parameter prediction. In contrast to traditional ML approaches that minimize standard loss functions such as mean squared error, our proposed model seeks to minimize the objective value derived directly from the decision-making problem. Furthermore, this strategy is validated by developing an optimization-based regression tree model for predicting stock returns and reducing decision errors. Empirical evaluations of our framework reveal its superiority over conventional regression tree methods, demonstrating enhanced decision quality. The computational experiments are conducted on a stock market dataset to compare the effectiveness of the proposed framework with the conventional regression tree-based approach. Remarkably, the results confirm the strengths inherent in this holistic approach.

Evaluation technologies for assessing drought tolerance of Kimchi cabbage seedlings using hyperspectral imaging and principal component analysis

Conventionally, methods for evaluating the tolerance of kimchi cabbage have primarily relied on visual inspection. The development of evaluation criteria based on hyperspectral imaging may help address the problem of decision-making errors arising from visual inspection. However, to evaluate the tolerance of kimchi cabbage using hyperspectral imaging, not only are hyperspectral big data processing and sophisticated exponential model development required, but also real-time evaluation technology with high field usability. This study presents an evaluation method for extracting vegetation information from hyperspectral data, logically analyzing the extracted data, validating the developed model, and ensuring field utility. The red-edge band between 680 and 700 nm is for evaluating the drought tolerance of Kimchi cabbage. The proposed method allows for assessing the drought tolerance of Kimchi cabbage seedlings within a range of 1 to 30, using a robust index with a variance of approximately 2.25 and a sensitivity index with a variance of around 8.4. Since the proposed nonlinear drought tolerance evaluation method must strongly correlate with the chlorophyll index, its performance was verified using the newly proposed cross-point analyzer. While nonlinear drought evaluation technology can serve as a robust analysis method based on extensive data, it has the disadvantage of requiring a long evaluation time. However, the additionally developed cross-point analyzer can improve the real-time capability of testing. Proper utilization of these two analytical technologies enhances the performance of robust and rapid drought tolerance evaluation for Kimchi cabbage seedlings. Therefore, this study introduces two techniques for non-destructively and quantitatively explaining plant water stress.

Sequential three-way decision with automatic threshold learning for credit risk prediction

Machine learning algorithms treat credit risk prediction as a binary classification problem. However, two-way decisions with a single threshold force to make either a default or non-default decision may be inappropriate. To reduce the risk of decision errors, this study introduces three-way decisions and proposes a sequential three-way decision model with automatic threshold learning to evaluate credit risk. Initially, the model uses the loan amount and interest to determine the decision loss of the three-way decision, assigning distinct decision thresholds to different samples. Subsequently, the model employs decision cost and information gain to formulate an objective for threshold optimisation. Finally, the model continuously optimises the classification process by using the outcomes of certain decisions as supplementary information. In addition, to validate our model, we conduct comparative experiments with various methods on a real credit dataset from a Chinese bank. The results indicate that the model not only enhances classification performance across several metrics but also assists financial institutions in reducing decision error costs.

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).

EmotionCast: An Emotion-Driven Intelligent Broadcasting System for Dynamic Camera Switching.

Traditional broadcasting methods often result in fatigue and decision-making errors when dealing with complex and diverse live content. Current research on intelligent broadcasting primarily relies on preset rules and model-based decisions, which have limited capabilities for understanding emotional dynamics. To address these issues, this study proposed and developed an emotion-driven intelligent broadcasting system, EmotionCast, to enhance the efficiency of camera switching during live broadcasts through decisions based on multimodal emotion recognition technology. Initially, the system employs sensing technologies to collect real-time video and audio data from multiple cameras, utilizing deep learning algorithms to analyze facial expressions and vocal tone cues for emotion detection. Subsequently, the visual, audio, and textual analyses were integrated to generate an emotional score for each camera. Finally, the score for each camera shot at the current time point was calculated by combining the current emotion score with the optimal scores from the preceding time window. This approach ensured optimal camera switching, thereby enabling swift responses to emotional changes. EmotionCast can be applied in various sensing environments such as sports events, concerts, and large-scale performances. The experimental results demonstrate that EmotionCast excels in switching accuracy, emotional resonance, and audience satisfaction, significantly enhancing emotional engagement compared to traditional broadcasting methods.

A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions.

Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle's behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor's failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge.