Expert Intuition Research Articles

Equipment Development Scheme Decision Based on IFS‐Choquet Integral Improved Matter Element Model

An optimized matter element decision method is proposed based on intuitionistic fuzzy set (IFS) and Choquet integral to solve the problems of equipment development scheme decision (e.g., the correlation of scheme attributes and the low decision reliability of evaluation results). First, the nonlinear programming problem of expert intuition fuzzy subjective and objective decision information difference is constructed using the Choquet integral method, and the measure values and related factors of decision attributes are solved using the optimization algorithm. Second, given the comprehensive contribution of attributes, the fuzzy measure Shapley value is employed to represent the attribute weight. Last, the matter element extension model is optimized using the attribute weight information and intuitionistic fuzzy score value, and a two‐level evaluation strategy is formulated to determine the ranking and grade. On that basis, scientific decisions are made for several different schemes to be selected.

A Study on the Intuitive Design of Target Search Tasks under Time and Information Pressure.

In modern warfare, operators of radar equipment are confronted with a large amount of information in a short period of time that they must process to make decisions. Such conditions often lead to human error, resulting in the loss of the best operational opportunities and putting themselves at a disadvantage. To address this issue, in this paper, we presents three intuitive designs for radar display interface icons, namely image schema, stereotypes, and expert intuition. Based on event-related potential techniques and eye-movement techniques, a modified sample delay matching task experimental paradigm is used to investigate the advantages and mechanisms of three intuitionistic designs with varying time pressures and numbers of tasks. The experimental results showed: (1) When representing the attributes of a target, image schema are most suitable for expressing the motion attributes of the target, such as speed and height, whereas stereotypes are most suitable for expressing fixed attributes, such as target threat. (2) Tasks with high time pressure are more difficult, leading to higher error rates; the reaction time of a single task should be no less than 4000 ms. (3) When multiple attributes of the target need to be observed at the same time, the same type of expression should be used. (4) Rational use of color to represent the target attributes can effectively reduce the cognitive load of people searching for the target.

Automatic neural network hyperparameter optimization for extrapolation: Lessons learned from visible and near-infrared spectroscopy of mango fruit

Configuring a neural network’s architecture and hyperparameters often involves expert intuition and hand-tuning to extrapolate well without overfitting. This paper considers automatic methods for configuring a neural network for the domain of visible and near-infrared (Vis-NIR) spectroscopy. In particular, we study the effect of (a) validation set choice for validating configurations and (b) using ensembles. We consider several validation set choices: a random sample of 33% of non-test data (the technique used in previous work), samples from the latest year (a harvest season), and the first, middle, and latest 33% of samples sorted by time. To test these methods, we do a comprehensive study of a held-out 2018 harvest season of mango fruit given Vis-NIR spectra from 3 prior years. We find that ensembling improves the state-of-the-art model’s variance and accuracy. Furthermore, hyperparameter optimization experiments show that when ensembling is combined with using the latest 33% of samples as the validation set, a neural network configuration is found automatically that performs as well as an expertly-chosen configuration.

Language Norms: What and where are they?

The notion of a language norm is far from clear. Lacking a conclusive explication, one may wonder what language studies are actually studying. Is it the experience of speakers? Is it their brains? Is it their behavior? Is it the product of expert intuitions? Even within linguistics the views seem scattered on this point, and no consensus has been reached on what the object of study in linguistics is supposed to be. The ontology of language norms is thus of high relevance, not only philosophically, but for the empirical implications of any potential ontological claim. In a question: what are we studying when we are studying language norms? This paper is thus an effort to, primarily with the help of Husserlian phenomenology, discuss language norms and their location in order to answer this question as well as to obviate or at least mitigate some of the concerning trends found in recent discussions on language norms.

An intuitive approach to judicial expertise

Research shows that expert performance in many fields of activity is embodied in an expert level of intuition. This appears to be true in predictable domains with fixed rules. However, evidence suggests that this type of expertise also exists among representatives of more naturalistic domains, such as firefighters or art specialists. This paper considers whether a kind of expertise embodied in expert intuition can occur in judges. It supports the thesis that it is possible to achieve expert intuition in the scope of some types of court situations consisting of legal problems with an objective legal standard for solving them. However, legal intuition is ineffective in cases involving legal problems with no such standard, and thus it cannot be developed to the expert level in this respect. The paper discusses two model examples of comparable legal problems: those that generate a completely novel normative issue for judges, and those that create a conflict between judicial intuitions, frequently regarding the relation between law and morality. In both of these situations there are no visible environmental regularities (repetitive patterns of legal practice) to adapt, and hence, it is impossible to perform a deliberate practice – a form of training indispensable for developing expert intuition – for legal decision-making. Legal intuitive expertise, therefore, appears to be powerless in determining the holistic answers to some legal court cases, despite the skills and experience of the judge.

Modeling Speedup in Multi-OS Environments

For workloads that place strenuous demands on system software, novel operating system designs like unikernels, library OSes, and hybrid runtimes offer a promising path forward. However, while these systems can outperform general-purpose OSes, they have limited ability to support legacy applications. Multi-OS environments, where the application’s execution is split between a control plane and a data plane operating system, can address this challenge, but reasoning about the performance of applications that run in such a split execution environment is currently guided only by expert intuition and empirical analysis. As the level of specialization in system software and hardware continues to increase, there is both a pressing need and ripe opportunity for investigating analytical models that can predict application performance and guide programmers’ intuition when considering multi-OS environments. In this paper we present such a model to place bounds on application speedup, beginning with a simple, intuitive formulation, and progressing to a more refined model. We present an analysis of the model for a diverse set of benchmarks, as well as a prototype tool to project multi-OS speedups for applications on existing systems. Finally, we validate our model on state-of-the-art multi-OS systems, demonstrating that it reliably predicts speedup with 96% average accuracy.

Predictive hospital site selection model using machine learning techniques

Globally, countries are faced with healthcare challenges that vary from one to the next. While health service delivery challenges are more often seen in countries with a very high HumanDevelopment Index (HDI), inefficient healthcare intervention challenges attract more attention in those with a low HDI. Health systems and infrastructure interventions are major challenges for most countries in Africa. The conventional or traditional approach to situating hospitals has been subjected to the unreliable intuition of experts and perhaps biased by nepotism, favoritism, and tribalism of recognized interest. In this research, we prioritize health factors for hospital site predictions. A hospital is a healthcare intervention infrastructure that should meet the healthcare needs of people where it is located. Many hospital site selection researchers have considered other important factors such as geographical, economic, and socio-demographic factors. However, healthcare factors that will specifically address the healthcare needs of people in that locality have not been mentioned. This paper considers a robust, viable, reliable, and dependable approach to solve the specific problem of selecting the best location for building new hospitals based on the specific healthcare needs of the location for improved healthcare service delivery. We propose a supervised machine learning approach to predict the most suitable sites for building new hospitals. Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LGR), and Decision Tree (DT) machine learning algorithm are used to predict the optimal location for hospital site selection on the basis of various attributes used in the dataset. The dataset was extracted from clinical exploratory data using parameters such as reproductive, maternal, newborn, and child health, Infectious diseases, Noncommunicable diseases, Adequate sanitation, Service capacity, and access to essential medicines as a Universal Health Coverage (UHC) standard by World Health Organization (WHO). The model is implemented using Python programming language. The system will improve health systems and infrastructural interventions, thereby enabling efficient healthcare service delivery in developing countries, especially in Africa.

When do expert decision makers trust their intuition?

AbstractThe aim of this study was to explore when experts trust their intuition. The Take‐The‐First heuristic suggests that experts generate a few options based on option validity that match the current situation and probably pick the first one they generated. In chess, the rated quality of moves can be used to analyze fast and slow decisions. We provided players with strategic (long‐term) and tactical (short‐term) situations and asked for fast choices, further candidate moves, and the best choice. We divided the participants into three groups based on expertise. Results indicate that chess players at lower skill levels were more vulnerable in tactics than in strategy, especially under time pressure. Masters scored better than near‐experts on intuitive and final decisions, and whereas near‐experts profited from more time, the masters did not. This finding implies that Take‐The‐First is both boundedly and ecologically rational. Conclusions are made regarding trusting the intuitions of experts.

The Role of Intuitive Cognition in Radiologic Decision Making.

The decision making involved in radiologic interpretation entails distinct cognitive pathways. On one side is analytic reasoning, which represents a deliberate, stepwise process integrating discrete data to formulate an interpretation, in which a range of diagnostic possibilities are directly compared. On the other side is intuition, which represents an automatic, rapid, and holistic form of decision making that generates an interpretation absent the sequential processing of data and direct comparison of possibilities. Nonexpert intuitive cognition often reflects domain-independent heuristics (ie, mental rules of thumb) that are often effective but prone to bias and systematic error. In contrast, expert intuition reflects the domain-specific skills developed among highly experienced practitioners who have gained deep knowledge in a given task domain from extensive practice and feedback. In this article, the authors define intuitive cognition, show evidence for its pervasive use among experts in a variety of fields, and explain its strengths and weaknesses relative to deliberate reasoning. Developing expert intuition requires the opportunity to learn from reliable feedback, and the authors describe various measures that can be used by radiology departments to foster such opportunities. Finally, the authors discuss implications for diagnostic performance and error reduction in clinical radiology.

Machine learning for microfluidic design and control.

Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.

The four types of intuition managers need to know

In today’s rapidly changing but data-rich environments, managers at all organizational levels need to use appropriate intuition, balanced with analytic thinking, to create and capture opportunities. Intuition is often thought of as a single construct, but our 2-year longitudinal study of multiple managers developing opportunities uncovered four distinct types of intuition: expert intuition, based on previous experience; creative intuition, based on a sense of direction for a novel solution; social intuition, based on a sense of interpersonal relationships; and temporal intuition, based on a sense of the timing being right to create or capture an opportunity. We offer a range of recommendations regarding the strengths and limitations of each type of intuition. With a more nuanced understanding of the types of intuition, managers will be better equipped to leverage the strengths—and be wary of the limitations—of intuition in their own decision-making and that of others.

Algorithms and venture investment decisions: better, fairer or biased?

PurposeThis paper is a rejoinder to the work of Blohm, Antretter, and colleagues recently published in both Entrepreneurship Theory and Practice and Harvard Business Review titled “It's a Peoples Game, Isn't It?! A Comparison Between the Investment Returns of Business Angels and Machine Learning Algorithms” and “Do Algorithms Make Better – and Fairer – Investments than Angel Investors?”, respectively.Design/methodology/approachWhile we agree with authors of prior scholarship on the importance of counteracting human biases, honing expert intuition and optimizing the odds of success in investment decision-making contexts, in the spirit of open academic discourse, this paper respectfully challenges some of the underlying assumptions concerning algorithmic bias on which prior work is based.FindingsInvesting remains part art and part science, and while algorithms may begin to play a more significant role in investment decision-making, human intuition remains hard to imitate. In both people and in algorithms, sources of bias remain both implicit and explicit and often have systemic roots, so more research continues to be needed to fully understand why algorithms produce potentially biased outcomes across a wide array of contexts.Originality/valueThis paper contributes to our collective understanding on the use of algorithms in making investment decisions, highlighting the fact that bias exists in humans and algorithms alike, even when the best of intentions are present.

430. Strategies for Prevention of COVID-19 Transmission in Hospitals

BackgroundInfection control measures against the coronavirus disease 2019 (COVID-19) within a hospital often rely on expert experience and intuition due to the lack of clear guidelines. This study surveyed current strategies for the prevention of the spread of COVID-19 in medical institutions.MethodsUpon systematic review of the guidelines at the national level, 14 key topics were selected. Six hospitals were provided an open survey that assessed their responses to these topics between August 11 and 25, 2020. Using these data, an online questionnaire was developed and sent to the infection control teams of 46 hospitals in South Korea. The survey was conducted between January 31, 2021, and February 20, 2021.ResultsAll 46 hospitals responded to the survey, and 24 hospitals (52.2%) had treated 100 or more cases of COVID-19. All hospitals operated screening clinics, and the criteria were respiratory symptoms (100%), fever (97.8%), and epidemiological association (93.5%). It was found that 89.1% (41/46) of hospitals allowed symptomatic patients to visit their general outpatient clinics if fever or respiratory symptoms were not associated with COVID-19. Most hospitals (87.2%; 34/39) conducted polymerase chain reaction (PCR) tests for all hospitalized patients. Moreover, 76.1% (35/46) of hospitals implemented preemptive isolation policies for hospitalized patients, of which 97.1% (34/35) were released from isolation after a single negative PCR test. A little over half of the hospitals (58.7%; 27/46) treated patients that met the national criteria for release from isolation but consistently had positive PCR results. Of these hospitals, 63% (17/27) used N95/KF94 masks, and 40.7% (11/27) used surgical masks without other personal protective equipment for treating them. Most hospitals (76.9%; 20/26) accommodated them in shared rooms when the cycle threshold value of the PCR test was more than a certain value (34.6%; 9/26), or after a certain period that satisfied the national criteria (26.9%; 7/26). Finally, 76.1% (35/46) of hospitals performed emergency procedures or operations on suspected patients.Table 1. Screening and selective treatment policy to prevent COVID-19 patients from entering the hospital NoteValues are presented as number (%) Abbreviations: COVID-19, coronavirus disease 2019; PCR, polymerase chain reaction 1 This question requested the respondent to select multiple items. 2 Suspected cases of COVID-19 include fever, respiratory symptoms, and epidemiological associations with COVID-19 patients. NoteValues are presented as number (%) Abbreviations: COVID-19, coronavirus disease 2019; PCR, polymerase chain reaction; PAPR, powered air-purifying respirator; Ct, cycle threshold 1 This question requested the respondent to select multiple items. 2 It includes infectious diseases, pulmonology, and the infection control and prevention office. 3 One hospital that wrote a non-categorical answer for the question was excluded. The hospital made a decision after discussing it with an infectious diseases specialist.ConclusionVarious guidelines were being applied by each medical institution, but there was a lack of an explicit set of national guidelines to support them.Disclosures All Authors: No reported disclosures

Midsagittal Plane First: Building a Strong Facial Reference Frame for Computer-Aided Surgical Simulation

A facial reference frame is a 3-dimensional Cartesian coordinate system that includes 3 perpendicular planes: midsagittal, axial, and coronal. The order in which one defines the planes matters. The purposes of this study are to determine the following: 1) what sequence (axial-midsagittal-coronal vs midsagittal-axial-coronal) produced more appropriate reference frames and 2) whether orbital or auricular dystopia influenced the outcomes. This study is an ambispective cross-sectional study. Fifty-four subjects with facial asymmetry were included. The facial reference frames of each subject (outcome variable) were constructed using 2 methods (independent variable): axial plane first and midsagittal plane first. Two board-certified orthodontists together blindly evaluated the results using a 3-point categorical scale based on their careful inspection and expert intuition. The covariant for stratification was the existence of orbital or auricular dystopia. Finally, Wilcoxon signed rank tests were performed. The facial reference frames defined by the midsagittal plane first method was statistically significantly different from ones defined by the axial plane first method (P=.001). Using the midsagittal plane first method, the reference frames were more appropriately defined in 22 (40.7%) subjects, equivalent in 26 (48.1%) and less appropriately defined in 6 (11.1%). After stratified by orbital or auricular dystopia, the results also showed that the reference frame computed using midsagittal plane first method was statistically significantly more appropriate in both subject groups regardless of the existence of orbital or auricular dystopia (27 with orbital or auricular dystopia and 27 without, both P<.05). The midsagittal plane first sequence improves the facial reference frames compared with the traditional axial plane first approach. However, regardless of the sequence used, clinicians need to judge the correctness of the reference frame before diagnosis or surgical planning.

Deep reinforcement learning-designed radiofrequency waveform in MRI

Carefully engineered radiofrequency (RF) pulses play a key role in a number of systems such as mobile phone, radar, and magnetic resonance imaging. The design of an RF waveform, however, is often posed as an inverse problem with no general solution. As a result, various design methods each with a specific purpose have been developed based on the intuition of human experts. In this work, we propose an artificial intelligence (AI)-powered RF pulse design framework, DeepRF, which utilizes the self-learning characteristics of deep reinforcement learning to generate a novel RF pulse. The effectiveness of DeepRF is demonstrated using four types of RF pulses that are commonly used. The DeepRF-designed pulses successfully satisfy the design criteria while reporting reduced energy. Analyses demonstrate the pulses utilize new mechanisms of magnetization manipulation, suggesting the potentials of DeepRF in discovering unseen design dimensions beyond human intuition. This work may lay the foundation for an emerging field of AI-driven RF waveform design.

Managing Complex Crises through the Lens of Intuitive Expertise: A Naturalistic Decision-Making Perspective

This theoretical paper draws extensively on the extant literature to examine the role of expert intuition in the management of non-routine crises within a volatile, uncertain, complex, and ambiguous (VUCA) environment. It follows a theoretically driven inductive design to explore the construct of intuitive expertise, with a specific focus on high-risk domains. Methodically, the paper builds on the naturalistic decision-making (NDM) theory to explore how experienced crisis managers perform complex tasks with the aid of their tacit and intuitive knowledge. Evidence suggests that experienced decision-makers are more likely to solve time-pressured tasks using their intuitive mode as the default strategy, only switching to a deliberative mode when the proposed course(s) of action require some form of justification or where pattern recognition has proven insufficient. The paper also develops a four-dimensional framework that describes both individual and situational factors that generally influence decision-making dynamics in a VUCA crisis environment. A synthesis of the literature results in the emergence of four theoretical propositions, with implications discussed for crisis and emergency practice. A key recommendation is to integrate the NDM sub-discipline into the field of crisis management, with suggestions that such integration may lead to significant improvements in crisis response effectiveness.

Стандарт научности педагогической экспертизы: созидательное измерение

Pedagogical expertise is considered as a system-forming scientific technology in education, which has a significant impact on the practice of teaching and the formation of a creative personality, actually becoming a method of scientific cognition in the context of improving the quality of education. It is advisable to design all programs of higher pedagogical education according to the deductive principle-from the general content component presented in the structure of technologies of pedagogical expertise of educational projects and programs, to private methodological practice-oriented recommendations proposed by experts as an indicative basis for teacher actions.&#x0D; The role of an expert and his personal qualities are of particular importance in the composition of pedagogical expertise. Based on the professional analysis of the expert's activity, his personal portrait is formed. In pedagogical expertise, the ratio of the objective (various normative documents regulating the activities of an expert) and the subjective is crucial, when the quality of the examination depends on the competence, experience of an expert specialist, his wisdom, intuition and creative insight. The analytical review of the mistakes of pedagogical experts should be used as the basis of the methodology for training teachers and expert specialists in the field of education.

DIFFERENTIATED APPROACH TO THE CONTENT AND VOLUME OF FORENSIC MEDICAL KNOWLEDGE IN COMPETENCES OF DIFFERENT SUBJECTS OF LEGAL PROCEEDING

Aim of the article: to conduct a legal analysis of a differentiated approach to the content and volume of special forensic medical knowledge in the competence of various subjects of legal proceedings for their effective implementation of the norms of criminal procedure and other legislation, as well as of the educational context, within which preparation for professional activities requiring a certain the scope of the relevant competencies is conducted. Conclusions. The article deals with the requirements for the competence in the field of forensic medicine of various subjects of legal proceedings. Expert consciousness (as an integral system of expert thinking, expert knowledge, expert intuition, etc.) is attributed to the special competence of medical persons professionally knowledgeable in the field of forensic medicine. The lawyers participating in the proceedings are assigned an integrating and coordinating role, which requires general competence in forensic medicine, in particular, awareness of the modern possibilities of forensic medical examination and other forms of procedural and non-procedural application of forensic medical knowledge. The specified differentiated approach to the scope of competences of various subjects of the legal process does not imply any consolidation, interchangeability, but directly stipulates the need for goal-setting cooperation within the framework of a strict separation of the procedural roles assigned to these subjects, the integrity of the system of expert consciousness with the separation from it of the minimum necessary elements of forensic medicine knowledge sufficient to implement the procedural role of the investigator and other procedural ones. The consequences of the gaps are predictably negative in the education of lawyers, in particular, investigators, prosecutors, advocates and judges, caused by the absence of the discipline “Forensic medicine” in the basic (compulsory) part of the federal state standard of higher professional education in the field of “Jurisprudence”. The formation of an effective model of cooperation seems to be of current interest between subjects of legal proceedings with considering the establishment of differentiated requirements for the volume and content of forensic medical competencies. The complex structure of forensic competences, qualification requirements for forensic medical experts, the peculiarities of the education of medical specialists - should be taken into account by the subjects of the investigation, the court when involving forensic experts and other doctors to participate in the proceedings. It is necessary to change the wording of Art. 178 of the Criminal Procedure Code of the Russian Federation: «with the participation of a forensic medical expert, and if one’s participation is impossible, - for a doctor» to the wording: «with the participation of a forensic medical expert, and if his participation is impossible, - for another doctor».

Developing Expertise in Clinical Radiology: The Feedback Challenge.

In 2009, psychologists Daniel Kahneman and Gary Klein published a now widely cited article describing conditions for developing expert intuition [1]. The article represented an “adversarial collaboration” because the authors hold contrasting perspectives on the topic of professional judgment and decision making. Kahneman is a Nobel prize winner who (along with his former colleague Amos Tversky) is famous for having developed the “heuristics and bias” research program [2]. Their seminal research studies uncovered many of the heuristics (ie, mental shortcuts) that humans use for judgment and decision making and the conditions under which bias and systemic error predictably ensue.

Combating small-molecule aggregation with machine learning

Biological screens are plagued by false-positive hits resulting from aggregation. Methods to triage small colloidally aggregating molecules (SCAMs) are in high demand. Herein, we disclose a neural network to flag such entities. Our data demonstrate the utility of machine learning for predicting SCAMs, achieving 80% of correct predictions in an out-of-sample evaluation. The tool is competitive with a panel of expert chemists, who correctly predict 61% ± 7% of the same molecules in a Turing-like test. Our computational routine provides insight into features governing aggregation that had remained hidden to expert intuition. Further, we quantify that up to 15%–20% of ligands in publicly available chemogenomic databases have high potential to aggregate at a typical screening concentration (30 μM), imposing caution in systems biology and drug design programs. Our approach provides a means to augment human intuition and mitigate attrition and a pathway to accelerate future molecular medicine.