Judgments Of Decision Makers Research Articles

Prediction of human error probability in helicopter to ship transfer operation under an evidential reasoning extended CREAM approach.

Although the helicopter to ship transfer operation is rarely applied in ports around the world, some of the hub ports have encouraged it. The operational process poses challenges to safety due to the nature of the work to be completed by the ship crew. This article performs a systematic human error prediction to ensure safe helicopter to ship transfer operations under different circumstances since the human factor is becoming a significant contributor to maritime accidents. To accomplish this, the article addresses a cognitive reliability and error analysis method (CREAM) under the evidential reasoning (ER) approach. In the proposed approach, the CREAM provides a comprehensive human error prediction tool and ER is capable of supporting experts' judgment in decision-making. The findings of the research show that 'Secure the loose objects within or adjacent to the operating area' poses a high human error probability (4.50E-02) during helicopter to ship transfer operations.

Evaluating the Application of an EMS Clinical Judgment Theoretical Framework

ABSTRACT OBJECTIVES Clinical judgment (CJ) encompasses clinical reasoning (process of evaluating a problem) and clinical decision-making (choice made). A theoretical model to better define emergency medical services (EMS) CJ has been developed but its use has not been evaluated in EMS training and assessments. Our objective was to evaluate the performance of this EMS CJ model to assess clinical reasoning and decision-making in a simulated environment. METHODS In this evaluation, EMS clinician teams (2-3 members) were directed to care for a simulated older adult patient in their home following a fall. Simulations were video recorded, clinician team actions coded, and evaluated for whether proper CJ reasoning and decisions were made. We evaluated CJ in two ways: 1) EMS medical directors' (MD) determination of whether the CJ questions were addressed (MD score) and 2) objective rubric evaluation of CJ questions using the EMS CJ model focused on recognition of appropriate cues, performance of actions, and revaluation after action (rubric score). The CJ questions addressed in this simulation included: 1) Is the patient stable/unstable?, 2) Are interventions necessary before movement?, 3) How should the patient be transferred from the floor?, and 4) Does the cause of the fall require hospital evaluation? Descriptive statistics were calculated, and concordance between the two assessments was evaluated (mean, 95% CI). Percent concordance was calculated with a validity threshold set at 70%. RESULTS Four EMS MDs reviewed 20 videos addressing 80 clinical judgment decisions. Overall concordance between MD score and rubric score for CJ decisions was above the threshold at 88.1% (85.0, 91.2). Concordance between MD score and rubric score for each CJ decision was 92.0% (87.3, 96.7) for question 1, 79.9% (71.5, 88.3) for question 2, 95.0% (90.4, 99.6) for question 3, and 85.4% (79.5, 91.2) for question 4. CONCLUSION An objective evaluation of CJ decisions using a rubric derived from an EMS CJ theoretical framework demonstrated high concordance to subjective evaluations of CJ made by EMS MDs. This approach may allow for reproducible and objective CJ evaluations that could be used for competency assessment in EMS.

A Distance-Based Approach to Fuzzy Cognitive Maps Using Pythagorean Fuzzy Sets

AbstractFuzzy Cognitive Maps (FCMs) have been attracting researchers from a wide application area due to being easy to apply and interpret. Since its proposal, the method has been improved to satisfy the diverse needs of practitioners such as solving different types of problems and representing particular types of uncertainty. The classical FCMs depend highly on the decision-maker judgments and the uncertainty inherent in the judgments deserves significant attention. Although there are several fuzzy extensions integrated into FCMs, the uncertainty caused by the lack of knowledge, the hesitancy of decision makers, and also the limited capacity of humans to deal with pre-defined rules should be considered. To address this issue, a new distance-based approach integrating Pythagorean Fuzzy Sets and FCMs is proposed. To the best of our knowledge, this is the first time this extension is integrated into FCMs. Besides allowing to represent the uncertainty until the end of the calculations, the new approach offers decision makers an easier and more flexible way to assess the strength of existing causal relationships. To provide a comparison between the proposed approach and the classical FCMs, two real-life applications are selected as case studies.

Irresolution and Other Weaknesses of Soul in Descartes

Abstract This paper contributes to a better understanding of Cartesian irresolution by clarifying its relation to akrasia and wantonness. It argues that irresolution (qua passion) is the same as neither akrasia nor wantonness, but is, like them, a kind of weakness of soul. If akrasia consists in having what Descartes calls ‘firm and decisive judgments’ (F&D judgments) but failing to act on them, and wantonness consists in not having any F&D judgments at all, but acting completely at the behest of the passions, then irresolution can be understood as a state between akrasia and wantonness: the irresolute lacks F&D judgments (like the wanton) but (unlike the wanton) actively strives to attain them.

CMAA–AHP: combinatorial multicriteria acceptability analysis with the analytic hierarchy process

AbstractCombinatorial multi-criteria acceptability analysis (CMAA) is a framework for supporting multicriteria group decisions that provides both a detailed analysis of the effects of individual decision-maker inputs as well as interactive guidance for a consensus-building process. The analytical hierarchy process (AHP) is a widely-used model of decision-maker evaluations that is based on pairwise comparisons. The goal of this work is to show how CMAA can be integrated with AHP in order to make its benefits available to AHP users. We use a minimal input format for AHP which avoids a problem with inconsistency and also reduces the cognitive load on the decision-makers. We extend the CMAA method by introducing new judgement and preference sensitivity variables, which provide helpful insights for the facilitator of the group decision. An example illustrates the combined CMAA–AHP method and its ability to deliver consensus in a very small number of iterations. Monte Carlo simulation is used to study the convergence behavior of the method for a range of problem dimensions. It was found that the mean number of steps to reach consensus grows linearly with the number of alternatives and criteria. We consider two previously published group decisions that use the standard AHP approach of averaging decision-maker judgements and preferences. In both cases, CMAA–AHP delivers the same rankings based on the original input. However, the new method also provides insight into each decision and would have been able to guide each group to consensus within a small number of resolution steps.

Judgment and decision strategies used by weather scientists in southeast Asia to classify impact severity

Impact-based weather forecasting requires forecasters to predict what weather might do (impact information), rather than solely what weather might be (meteorological information). In a collaboration between the UK Met Office, UK psychologists, and weather scientists in Indonesia, Malaysia, the Philippines, and Vietnam, the present study employed Judgment Analysis and decision strategy comparisons to better understand weather scientists' impact severity judgments. In the Judgment Analysis Task, weather scientists (from Indonesia, Malaysia, the Philippines, and Vietnam) made numerical and categorical severity judgments for 70 hypothetical heavy rainfall events, each described via six impacts (e.g., number of deaths, number of people affected). The hypothetical impacts were generated from a multivariate distribution estimated from a distribution of real rainfall events. Subsequently, participants provided categorical severity classifications for a list of impact values for each type of impact (Threshold Identification Task) to aid the identification of decision strategies. In all four countries, weather scientists' severity judgments were best predicted by incorporating all six impacts via a compensatory judgment strategy. However, considerable individual differences were identified in the weights assigned to the different impacts and in the identified thresholds for each impact's categorical severity classification. To improve impact-based forecasting, meteorological agencies should seek to enhance consistency among forecasters.

Modelling of Human Factors in Aviation Maintenance Using HFACS ME Human Factors Analysis and Classification System Maintenance Extension and Bayesian Network

Aircraft maintenance is a complex task involving a skilled human workforce, spare parts, and various other resources. Human factors are an inherent element of the human workforce. Human factors analysis, therefore, becomes an essential aspect of aviation maintenance. Human factors have been identified and classified using various methods in existing literature. However, there is a gap in the study of the interdependency of critical human factors including subfactors, and measuring them effectively to reduce incidents and accidents. This research work proposed a novel approach for human factors modeling using human factors analysis and classification system maintenance extension (HFACS-ME), and bayesian network (BN). Inadequate maintenance processes, inadequate documentation, inadequate supervision, Judgement decision, and attention memory were identified as some of the critical human factors in aircraft maintenance. These critical human factors were further analysed and divided into subfactors. The main contribution of the present research work is the methodology of developing a dependency model of the human factors and subfactors to analyse their measured effects on aircraft maintenance. The proposed BN model demonstrated the estimation of the probability of effective maintenance by considering the critical human factors with available facilities, and resources in an aviation maintenance setup.

When is it acceptable to break the rules? Knowledge representation of moral judgements based on empirical data

Constraining the actions of AI systems is one promising way to ensure that these systems behave in a way that is morally acceptable to humans. But constraints alone come with drawbacks as in many AI systems, they are not flexible. If these constraints are too rigid, they can preclude actions that are actually acceptable in certain, contextual situations. Humans, on the other hand, can often decide when a simple and seemingly inflexible rule should actually be overridden based on the context. In this paper, we empirically investigate the way humans make these contextual moral judgements, with the goal of building AI systems that understand when to follow and when to override constraints. We propose a novel and general preference-based graphical model that captures a modification of standard dual process theories of moral judgment. We then detail the design, implementation, and results of a study of human participants who judge whether it is acceptable to break a well-established rule: no cutting in line. We then develop an instance of our model and compare its performance to that of standard machine learning approaches on the task of predicting the behavior of human participants in the study, showing that our preference-based approach more accurately captures the judgments of human decision-makers. It also provides a flexible method to model the relationship between variables for moral decision-making tasks that can be generalized to other settings.

How Sure is the Driver? Modelling Drivers’ Confidence in Left-Turn Gap Acceptance Decisions

When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgments could provide novel insights into human behavior. However, so far confidence judgments have been mostly studied in simplistic laboratory tasks while little is known about confidence in naturalistic dynamic tasks such as driving. In this study, we made a first attempt of connecting fundamental research on confidence with naturalistic driver behavior. We investigated the confidence of drivers in left-turn gap acceptance decisions in a driver simulator experiment (N = 17). We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle. Specifically, confidence increased with the gap size for trials in which the gap was accepted, and decreased with the gap size for rejected gaps. Similarly to more basic tasks, confidence was negatively related to the response times and correlated with action dynamics during decision execution. Finally, we found that confidence judgments can be captured with an extended dynamic drift–diffusion model. In the model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the gap size. Furthermore, we demonstrated that allowing for post-decision evidence accumulation in the model increases its ability to describe confidence judgments in rejected gap decisions. Overall, our study confirmed that principles known from fundamental confidence research extend to confidence judgments in dynamic decisions during a naturalistic task.

Evaluating Human Expert Knowledge in Damage Assessment Using Eye Tracking: A Disaster Case Study

The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts.

An exploration of in-service teachers’ framing, instrumental, and core design judgments in dynamic decision-making for technology integration

Dynamic decision-making plays a critical role in teachers’ instructional design and technology integration processes; it directly influences the design and development of teaching practices, student learning experiences, and learning environments. However, limited studies have investigated how teachers engage in dynamic decision-making processes in designing instruction and technology integration, wherein teachers are involved in multiple types of design judgments that inform their design decisions for instruction and technology integration. The purpose of this study was to explore teachers’ framing, instrumental, and core judgments that were intertwined in dynamic decision-making for technology integration. The study utilized the critical decision method approach to investigate teachers’ three prioritized design judgments in dynamic decision-making. The findings of this study helped us gain a better understanding of teachers’ dynamic decision-making processes, pedagogical reasoning, and design judgments while designing instruction for technology integration. It also informs professional development strategies to develop teachers’ instructional design decision-making skills for technology integration.

Dividend Policy and its Impact on Stock Prices: An Empirical Study on Selected Indian Steel Companies

Dividend decision is a very decisive judgment for any firm as it will impacted on several factors like stock prices, future development, debt repayments etc. in this particular paper it has been tested whether EPS and DPS has any significant effect on MPS or not. For that fifteen companies are chosen on the basis of market capitalization basis and there after correlation and regression analysis has been done to see the effect of EPS and DPS on MPS. Finally after all analysis it was found that ther was a positive correlation between EPS and DPS with MPS but not significantly and on the other side by regression analysis it was fond that approximately twenty two percent variations in MPS explained by EPS and DPS and rest due to residual term

Identification of Critical Areas of Openness–Vitality Intensity Imbalance in Waterfront Spaces and Prioritization of Interventions: A Case Study of Xiangjiang River in Changsha, China

Identification of Critical Areas of Openness–Vitality Intensity Imbalance in Waterfront Spaces and Prioritization of Interventions: A Case Study of Xiangjiang River in Changsha, China

Optimal Wind Farm Siting Using a Fuzzy Analytic Hierarchy Process: Evaluating the Island of Andros, Greece

In recent decades, fuzzy logic and fuzzy multi-criteria decision-making systems have been applied in several fields. This paper aims to determine the optimal wind farm siting solution in a fuzzy environment. Therefore, the main research question of the present paper is whether and to what extent the uncertainty in the researcher’s judgments affects the ranking of wind farm siting solutions. The fuzzy analytical hierarchy method is applied to an existing case study of wind farm siting on the island of Andros, examining the stability of the final priorities of the alternatives under a regime of gradual increases in ambiguity, as well as whether the introduced ambiguity in the model corresponds to any uncertainty the researcher has during the process of scoring the criteria and alternatives. Five assessment criteria (wind potential, ground slope, distance from road network, distance from high-voltage network, and social acceptance of local population) and eight eligible suitable alternatives (A1–A8) for wind farm siting are considered in the computations. The methodology includes the fuzzification of initial decision-maker judgments, the calculation of fuzzy intermediate priorities (weights), the defuzzification of fuzzy intermediate priorities (weights), and the synthesis of intermediate priorities into final priorities of alternatives, according to the procedures of the crisp AHP (CAHP). Under the assumptions of the initial case study, the results show that the final priorities are quite robust when faced with increased ambiguity. In almost all the examined cases, the alternative initially chosen as the best, A1, is dominant, followed by A3. In addition, in all cases, social acceptance favors alternative A1, and wind velocity favors alternative A8. Therefore, fuzzy multi-criteria methods can be applied to determine an optimal wind farm siting solution when criteria with qualitative characteristics are used and the manifestation of preferences involves strong elements of subjectivity.

An [formula omitted]-cut intervals based Fuzzy Best–Worst Method for Multi-Criteria Decision-Making

To address ambiguity arising from linguistic decision judgments, Guo and Zhao introduced a model of the well-known Multi-Criteria Decision-Making (MCDM) method, Best-Worst Method (BWM), using fuzzy sets, known as Fuzzy Best–Worst Method (FBWM). Despite its versatility, FBWM has limitations, such as criteria-weights being independent of fuzzy comparison values’ shape and reliance on approximate fuzzy arithmetic operations. In response to these limitations, we introduce a novel model of Fuzzy Best–Worst Method based on α-cut intervals. In this model, the calculation of optimal weights involves formulating an optimization problem that utilizes exact fuzzy arithmetic operations defined in terms of α-cut intervals. This approach aims to optimize the entire shape of fuzzy comparison values simultaneously. However, it turns out that, although we have proven the existence of optimal solution for this problem, solving it proves to be challenging. To resolve this issue, we approximate optimal weight set(s) using triangular fuzzy weight sets and assess the Error of Approximation (EA). The approximate weight set with the desired EA is then defuzzified to obtain a crisp weight set. In situations where there are multiple approximate weight sets with the same EA, interval-weights are calculated. Following that, we establish the necessary conditions for a Fuzzy Pairwise Comparison System (FPCS) to be consistent. Using these conditions, we calculate a lower bound of the Consistency Index (CI), providing a measure of the accuracy of weights. Finally, we discuss numerical examples and a real-world scenario, ranking of risk factors in supply chain 4.0, to validate the effectiveness of the proposed model.

Weighted probability kernel multi-granularity three-way decision integrating GRA and its application in medical diagnosis

Three-way decision, an outstanding method to handle decision-making uncertainties, relies on essentially the loss functions derived from the Bayesian risk decision process. Actually, there are plentiful loss functions that depend on the subjective judgment of decision-makers under different decision scenarios, lacking uniform and objective measurement frameworks. This study pays attention to the real clinical diagnosis, and constructs a weighted probability kernel multi-granularity three-way decision method (WKMG-TWD) integrating gray relation analysis (GRA) over a multi-source heterogeneous decision information system (MHDIS). The method establishes a standardized data-driven calculation framework of loss functions. Foremost, the multi-kernel probabilistic similarity is defined and granularity's weights with knowledge consistency are explored. Subsequently, a weighted probability kernel multi-granularity rough set (WKMGRS) is constructed in this paper. Secondly, to introduce the three-way decision, this study proposes the cost-sensitive individual loss functions considering the correlation determined by GRA between decision objects and different decision classes. Ultimately, this study establishes and applies a three-way iterative classification model to hypertension diagnosis. The experimental results confirm the effectiveness and superiority of the model. The main contribution of this paper is twofold. One is to offer a uniform calculation framework for loss functions and granularity's weights. The other is to furnish invaluable guidance for solving complex medical decision-making problems.

Ethical Principles in Dental Research of Behavioral and Social Factors

Given the importance of conducting research ethically when studying humans, this article involves considerations on basic ethical principles in dental research of behavioral and social factors. Research in these fields contribute to understand social interactions, attitudes, individual and group dynamics. As well, research in these areas not only delve into scientific knowledge of human nature, but also support evidence-based decision-making of health policies and interventions. The four main ethical principles apply to studies on behavioral and social factors. First, respect for persons dictates researchers must protect participants’ autonomy, which is the basis for informed consent. Moreover, the principle of beneficence refers to act in such a way that participants benefit from taking part of a research study. Also, beneficence promotes participants’ safety and well-being. Non-maleficence requires that researchers avoid or minimize harm to others. Justice implies fair treatment. Additional aspects of justice involve respecting participants’ privacy and confidentiality. Ethical principles require interpretation. Therefore, some examples are provided, aiming that researchers overview and apply these norms. Ethical judgement and appropriate decision making are of great importance at any stage of research with human beings.

Risk measurement of aggregation approaches in multiple attribute decision making under uncertain information

The decision-maker's judgment deviates from uncertain attribute information will lead to decision risk in multiple attribute decision making (MADM), and different aggregation approaches result in different risk levels. This paper aims to study the risk levels of aggregation operators in MADM with uncertain attribute information. We use the signal detection theory to characterize the decision-maker's noisy perceptions of multiple attributes to present his/her judgment deviation. Then, we establish the aggregation models to aggregate these perceptions based on the weighted averaging (WA) and the ordered weighted averaging (OWA) operators. Furthermore, a risk measurement model of each aggregation approach is constructed to measure the risk levels of the commission risk (CR), the omission risk (OR), and the overall risk. A numerical example is used to verify the validity of the proposed model, while simulation experiments are designed to compare the risk levels of the WA and OWA operators. The results reveal that the overall risk level of the WA operator is higher than that of the OWA operator when judging the quality of the alternative with high standards; otherwise, the WA operator is lower. This finding provides a scientific reference for aggregation approach selection under uncertain information. Code metadataPermanent link to reproducible Capsule: https://doi.org/10.24433/CO.0911610.v1.

Balancing Act: Exploring the Interplay Between Human Judgment and Artificial Intelligence in Problem-solving, Creativity, and Decision-making

This study explores the repercussions of excessive reliance on Artificial Intelligence (AI) on human cognitive processes, specifically targeting problem-solving, creativity, and decision-making. Employing qualitative semi-structured interviews and Interpretative Phenomenological Analysis (IPA), it delves into the nuanced challenges and risks stemming from an overemphasis on AI. The research illuminates a nuanced landscape: while AI streamlines problem-solving tasks and provides valuable support, there’s a crucial need to safeguard human judgment and intuition. In the realm of creativity, divergent viewpoints emerge, underscoring concerns regarding AI’s potential limitations and advocating for a harmonious interplay between AI-generated suggestions and individual creative thought. Regarding decision-making, participants recognize AI’s utility but underscore the necessity of blending AI insights with critical thinking and consideration of unique circumstances. They caution against complacency, advocating for a judicious equilibrium between AI guidance and individual expertise. This study innovates by providing multifaceted insights into the complexities of AI-human interaction, uncovering nuanced perspectives on its impacts across problem-solving, creativity, and decision-making domains. By bridging this gap, it advances understanding of how AI integration influences cognitive processes, offering practical implications for fostering a balanced approach. Its innovative methodology combines qualitative interviews and IPA, offering rich, nuanced data that provide a deeper understanding of the subject matter. This research serves as a beacon for promoting awareness of the risks associated with overreliance on AI, advocating for a mindful integration that upholds human agency while leveraging AI capabilities effectively.

Applications of artificial intelligence in anesthesia: A systematic review.

This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner.