Complex Judgments Research Articles

Political Leadership Through the Lens of Social Media

Abstract There is currently a global debate about the so-called media transformation, or metamorphosis, which is mainly concerned with technological benefits in the realm of information. This argument focuses on the relationship between "social media" and new media realities, specifically the impact that these platforms have on the development of political leadership in North Macedonia and the Western Balkans in general. This paper examines the differences between social media and traditional media, exploring their respective roles in shaping the public sphere and political leadership dynamics in North Macedonia and the Western Balkans. Information as a media product, produced with new technology, has caused a massive explosion in the 'membranes' of public interest and this has also diverted attention to a wider plan, where the diagram of reliability does not always match with that of reporting, due to the tendency for the media product to be seen more as a consumer good and less as a communication value. The study is based on comprehensive research, thorough analysis, and synthesis of data published in books and scientific articles on the Internet, with a critical analysis of the current situation. Furthermore, this scientific research procedure involves breaking down complex concepts, judgments, and conclusions into simpler components and elements. In the end, the study highlights that the professional challenges in conquering the media market remain constant, while the audience is the one who selects the offers that are closer to their social, political, and aesthetic tastes and affinities.

Artificial intelligence in dental practice: An integrated approach

Artificial intelligence (AI) has grown tremendously in importance and has presence in many different industries, including dentistry. By mimicking human intelligence, it can make complex forecasts and judgments in the medical field, particularly in endodontics. A variety of artificial intelligence models, such as convolutional neural networks and/or artificial neural networks, have shown promise in endodontics. These models have been used to study the anatomy of the root canal system, predict the viability of dental pulp stem cells, measure working lengths, identify periapical lesions and root fractures, and predict the outcome of retreatment procedures. Artificial intelligence (AI) technology is utilized in dentistry to expedite the comprehension of massive amounts of data and provide clinical decision-making support. This work aims to comprehensively review the areas in which artificial intelligence (AI) is now having a significant impact on clinical dentistry. Artificial intelligence (AI), which is evolving at a rapid pace, has the potential to replace physical expertise in dentistry. To reduce mistakes and oversight, these technologies must also be employed carefully and under human supervision. Early and accurate diagnosis of oral problems leads to better outcomes for patients.

Mapping cognitive biases in multidisciplinary team (MDT) decision-making for cancer care in Scotland: a cognitive ethnography study protocol

IntroductionThe efficiency of multidisciplinary teams (MDTs) in cancer care hinges on facilitating clinicians’ cognitive processes as they navigate complex and uncertain judgements during treatment planning. When systems and workflows are...

Research on Influencing Factors and Strategies of Art Collection Investment Decision

The realm of art collection holds an irreplaceable position in the long-standing cultural heritage, showcasing the taste and status of the holders from ancient nobility to modern high-net-worth individuals. However, with the development of economic globalization, art collection is not merely a symbol of identity, but also a hot investment target sought after by numerous investors. In recent years, the market value of the global art industry has rapidly climbed, making art gradually an essential component of diversified investment portfolios for investors. The development of the art market has sparked widespread attention from various sectors, prompting scholars and companies to explore, through scientific methods and data analysis, how to maximize the investment returns of art collection. However, art differs significantly from other investment tools in the financial market, involving not only the intrinsic value judgment of the art itself but also complex market environments and individual judgment and psychological expectations of investors. Its unpredictability and unique value assessment system make art collection investment more challenging and research-worthy compared to other types of investments.

ADVANTAGES AND DISADVANTAGES OF IMPLEMENTING ARTIFICIAL INTELLIGENCE IN THE EDUCATIONAL PROCESS

The article discusses the main aspects of the use of artificial intelligence in education, focusing on its advantages and challenges. The author defines AI as a system of functional computer technologies that model human thinking and skills, such as analysis of complex systems, judgment, and dialogue support. AI allows collecting and analyzing large amounts of data, developing individualized teaching methods, automating control, and providing feedback. Innovative technologies in education have proven to be effective even in times of crisis, such as quarantine and war. The main benefits of AI include personalization of learning, creation of individualized plans, immersive learning, and intelligent progress tracking, which reduces the workload of teachers and allows them to focus on students. At the same time, AI adoption is facing resistance to innovation, financial challenges, and skepticism about its effectiveness. Most AI programs focus on content presentation and testing, which does not promote critical thinking and creativity. Research prospects include developing methodologies for evaluating AI's effectiveness, integrating learning theories with practical developments, and addressing ethical issues.

ChatGPT Earns American Board Certification in Hand Surgery

PurposeArtificial Intelligence (AI), and specifically ChatGPT, has shown potential in healthcare, yet its performance in specialized medical examinations such as the Orthopaedic Surgery In-Training Examination and European Board Hand Surgery diploma has been inconsistent. This study aims to evaluate the capability of ChatGPT-4 to pass the American Hand Surgery Certifying Examination. MethodsChatGPT-4 was tested on the 2019 American Society for Surgery of the Hand (ASSH) Self-Assessment Exam. All 200 questions available online (https://onlinecme.assh.org) were retrieved. All media-containing questions were flagged and carefully reviewed. Eight media-containing questions were excluded as they either relied purely on videos or could not be rationalized from the presented information. Descriptive statistics were used to summarize the performance (% correct) of ChatGPT-4. The ASSH report was used to compare ChatGPT-4’s performance to that of the 322 physicians who completed the 2019 ASSH self-assessment. ResultsChatGPT-4 answered 192 questions with an overall score of 61.98%. Performance on media-containing questions was 55.56%, while on non-media questions it was 65.83%, with no statistical difference in performance based on media inclusion. Despite scoring below the average physician’s performance, ChatGPT-4 outperformed in the ‘vascular’ section with 81.82%. Its performance was lower in the ‘bone and joint’ (48.54%) and ‘neuromuscular’ (56.25%) sections. ConclusionsChatGPT-4 achieved a good overall score of 61.98%. This AI language model demonstrates significant capability in processing and answering specialized medical examination questions, albeit with room for improvement in areas requiring complex clinical judgment and nuanced interpretation. ChatGPT-4’s proficiency is influenced by the structure and language of the examination, with no replacement for the depth of trained medical specialists. This study underscores the supportive role of AI in medical education and clinical decision-making while highlighting the current limitations in nuanced fields such as hand surgery.

AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation

Background and aimsArtificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making. This study aims to evaluate the proficiency of GPT-3.5 and GPT-4 against experienced ED physicians in calculating five commonly used medical scores. MethodsThis retrospective study analyzed data from 150 patients who visited the ED over one week. Both AI models and two human physicians were tasked with calculating scores for NIH Stroke Scale, Canadian Syncope Risk Score, Alvarado Score for Acute Appendicitis, Canadian CT Head Rule, and HEART Score. Cohen's Kappa statistic and AUC values were used to assess inter-rater agreement and predictive performance, respectively. ResultsThe highest level of agreement was observed between the human physicians (Kappa = 0.681), while GPT-4 also showed moderate to substantial agreement with them (Kappa values of 0.473 and 0.576). GPT-3.5 had the lowest agreement with human scorers. These results highlight the superior predictive performance of human expertise over the currently available automated systems for this specific medical outcome. Human physicians achieved a higher ROC-AUC on 3 of the 5 scores, but none of the differences were statistically significant. ConclusionsWhile AI models demonstrated some level of concordance with human expertise, they fell short in emulating the complex clinical judgments that physicians make. The study suggests that current AI models may serve as supplementary tools but are not ready to replace human expertise in high-stakes settings like the ED. Further research is needed to explore the capabilities and limitations of AI in emergency medicine.

The Impact of Auditor Competence and Auditor Experience on Audit Judgement, Mediated by Task Complexity, within Public Accounting Firms in the City of Medan

Purpose: This research aims to investigate the intricate interplay between Auditor Competence and Auditor Experience, emphasizing their significant impact on Audit Judgment. The study also introduces Task Complexity as an intermediary variable, mediating the association between these competencies and judgment outcomes. Theoretical Framework: The research is grounded in a theoretical framework that highlights the relationships among Auditor Competence, Auditor Experience, Task Complexity, and Audit Judgment. This framework provides a conceptual basis for understanding how these variables interact and contribute to the overall audit process. Methods: Employing an associative methodological approach, the study utilizes primary data obtained through meticulously designed questionnaires. The participants consist of 173 auditors actively working in Public Accounting Firms (KAP) located in the urban expanse of Medan City. The sampling technique employed is saturated sampling to ensure a comprehensive representation of the population. Analytical pursuits involve a sequential tripartite process, including outer model analysis, inner model analysis, and hypothesis testing. Findings: The empirical analysis reveals several key insights. Firstly, Auditor Competence significantly influences Audit Judgment outcomes. Secondly, Auditor Competence has a substantive impact on Task Complexity. Thirdly, Auditor Experience significantly resonates with the tenets of Audit Judgment. Fourthly, Auditor Experience demonstrates meaningful correlations with Task Complexity. However, the relationship between Task Complexity and Audit Judgment is not statistically significant. Additionally, the intermediary effect of Task Complexity on the relationship between Auditor Competence and Audit Judgment lacks statistical substantiation. Lastly, the mediating role of Task Complexity in the association between Auditor Experience and Audit Judgment lacks empirical significance. Practical Implications: The study's findings hold practical implications for auditing practices. Firms can enhance Audit Judgment by focusing on improving Auditor Competence and strategically leveraging Auditor Experience. Understanding the nuanced relationship between these factors and Task Complexity can inform training programs and professional development initiatives within the auditing profession. Originality/Value: This research contributes to the field by offering nuanced insights into the relationships between Auditor Competence, Auditor Experience, Task Complexity, and Audit Judgment. The empirical findings provide a unique perspective, revealing both significant and non-significant associations, thereby contributing to a more comprehensive understanding of the factors influencing audit quality. This study's originality lies in its meticulous methodology and its potential to inform practical strategies for optimizing audit processes.

Contractual AI: Toward More Aligned, Transparent, and Robust Dialogue Agents

We present a new framework for AI alignment called Contractual AI, and apply it to the setting of dialogue agents chatting with humans. This framework incorporates and builds on previous approaches to alignment, such as Constitutional AI. We propose that fully aligned systems may need both a "think fast" and a "think slow" systems for approximating complex human judgements. Fast thinking (System 1) is computationally cheap but rigid and brittle in novel situations, while slow thinking (System 2) is more expensive but more flexible and robust. System 1 makes judgements by asking whether a rule or principle is violated. System 2 does the explicit reasoning that produces the rules, explicitly tallying costs and benefits for all stakeholders. Rule-based systems like Constitutional AI correspond roughly to System 1. Here, we implement a prototype of System 2, and lay out a road-map for enabling the system to make more thorough and accurate considerations for all stakeholder groups, including those underrepresented in the training data (e.g. racial minorities). For initial testing, we guided the decision process through the steps of: 1) identifying all stakeholders, 2) listing their individual concerns, 3) soliciting the projected opinions of various experts, and 4) combining the expert opinions into a final moral judgement. The resulting text was less generic, more aware of complex stakeholder needs, and ultimately more actionable.

Performance of complex energy geostructures

Energy geostructures are innovative technologies that combine the functions of structural support and geothermal energy harvesting. To date, theoretical, computational, and experimental investigations have explored the performance of distinct types of energy geostructures, i.e. energy piles, walls, slabs, and tunnels. However, there seemingly exists limited knowledge on the performance of complex energy geostructures that integrate different types of geostructures in a unique building solution, despite being common in practice. This work addresses this knowledge gap by providing a computational study of the performance of two real-world installations of complex energy geostructures. The study resorts to 3-D, time-dependent, thermo-mechanical finite element simulations that consider the dynamic building thermal energy needs and the operation of the considered complex energy geostructures over a design lifetime of fifty years. The work reveals that the energy, geotechnical, and structural performance of complex energy geostructures is far from trivial. Especially the structural performance of complex energy geostructures exhibits responses that are counterintuitive and different compared to those that would be predicted by simplified modeling approaches referring to individual types of energy geostructures. Accordingly, this investigation calls for attention in the analysis of complex energy geostructures and judgment in the application of simplified design methods.

Higher-order CBT skills: are there differences in meta-competence between trainee and experienced therapists?

Abstract Meta-competencies govern the application of more basic therapeutic competencies and allow CBT therapists to know when and why particular skills are needed. Meta-competencies are particularly important when responding to the needs of complex or atypical clinical cases. We explore CBT meta-competencies through therapist reflections on complex clinical scenarios and judgements about CBT skills. Three groups of therapists were compared in their responses to four complex clinical scenarios: trainees, recently qualified and experienced therapists. Participants reflected on how they would respond in each scenario and made ratings of the importance of different skills. There was a highly significant difference between trainees and experienced therapists in the number of reflective statements made, but no differences in the number of anticipated actions. There were no group differences in judgements about CBT skills. Reflective capacity is a meta-competency and higher-order skill that CBT therapists continue to develop several years post-qualification. Further studies are needed to replicate this finding and understand its impact on clinical practice. Key learning aims (1) To learn about CBT meta-competencies when considering clinical complexity. (2) To learn how to test meta-competencies in groups of therapists with differing levels of experience. (3) To identify which meta-competencies are prioritised in clinically complex scenarios. (4) To support the development of the scale which measures meta-competencies in therapists.

Level of Ethical Awareness of Bs Accountancy Students in Selected Private Universities in Camarines Sur: Basis for Ancillary Business Ethics Guide

Accounting is a discipline that involves not just the mechanical application of accounting expertise but also making complex judgments, both quantitative and qualitative, which demands the highest standards of integrity and objectivity. Ethical considerations are therefore of utmost importance to all accountants. Ethics education should continue to play an important part in accounting education. Accounting ethics were first introduced by Luca Pacioli and later expanded by government groups, professional organizations, and independent companies. Robert H. Montgomery, describing ethics in accounting, has said that “Accountants and the accounting profession exist as a means of public service; the distinction that separates a profession from a mere means of livelihood is that the profession is accountable to standards of the public interest and beyond the compensation paid by clients.”

ENGINE-An EHS Project for Future Guidelines.

Clinical guidelines are evidence-based recommendations developed by healthcare organizations or expert panels to assist healthcare providers and patients in making appropriate and reliable decisions regarding specific health conditions, aiming to enhance the quality of healthcare by promoting best practices, reducing variations in care, and at the same time, allowing tailored clinical decision-making. European Hernia Society (EHS) guidelines aim to provide surgeons a reliable set of answers to their pertinent clinical questions and a tool to base their activity as experts in the management of abdominal wall defects. The traditional approach to guideline production is based on gathering key opinion leader in a particular field, to address a number of key questions, appraising papers, presenting evidence and produce final recommendations based on the literature and consensus. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) method offers a transparent and structured process for developing and presenting evidence summaries and for carrying out the steps involved in developing recommendations. Its main strength lies in guiding complex judgments that balance the need for simplicity with the requirement for complete and transparent consideration of all important issues. EHS guidelines are of overall good quality but the application of GRADE method, began with EHS guidelines on open abdomen, and the increasing adherence to the process, has greatly improved the reliability of our guidelines. Currently, the need to application of this methodology and the creation of stable and dedicated group of researchers interested in following GRADE in the production of guidelines has been outlined in the literature. Considering that the production of clinical guidelines is a complex process, this paper aim to highlights the primary features of guideline production, GRADE methodology, the challenges associated with their adoption in the field of hernia surgery and the project of the EHS to establish a stable guidelines committee to provide technical and methodological support in update of previously published guideline or the creation of new ones.

Trajectory Planning for Hopping Rover on Small Bodies Under Pre-Collision Attitude Adjustment Modulation

A novel pre-collision attitude adjustment (PCAA) modulation based on angular velocity and quaternions control is developed to more controllable and reliably adjust the non-spherical hopping rover's trajectory on small bodies. This corresponds to a hopping trajectory planning (HTP) framework that addresses inherent technical challenges, including attitude-trajectory coupled features, hard-to-depicting near-truth gravity and continues dynamical contact models, difficult-to-predetermine collision times with the small bodies’ surface, and complex judgments regarding whether the period of attitude adjustment is sufficient in current hops. Two main contributions are proposed, focusing on modeling and planning. To achieve a more realistic modeling of the rover's motion, its attitude-trajectory coupling dynamic model is researched, including a ground-truth irregular gravity model and continuous dynamical contact model. Additionally, a collision detection method based on the local surface smoothing technique is proposed for rapid and exact switching between the ballistic and contact motions for the subsequent planning purpose. On the other hand, redundancy planning and binary event-trigger techniques are combined for handling unpredictable available dimensions of optimization variables of PCAA-based HTP, which arise from the indeterminate scale of collision times and potential deficiency in ballistic flight time required for completing attitude maneuvers. Specifically, the solution space is preset to guarantee the unchanging dimensions of the design variables, which are activated or deactivated by coupling binary event variables for each dimension. These newly introduced events are triggered by comparing the realistic rebound time of the current hop with the necessary time for attitude maneuver, which is accurately computed by designing a first-order sliding mode controller. This approach enables an equivalent and easily implementable version of the problem to be obtained. Logically and numerically, the application of PCAA modulation is found to significantly enlarge or contract the area of the reachable region of the hopping rover and contribute to improving the convergence and reducing the relative final position error of the hopping trajectory planning problem.

Relational visual representations underlie human social interaction recognition

Humans effortlessly recognize social interactions from visual input. Attempts to model this ability have typically relied on generative inverse planning models, which make predictions by inverting a generative model of agents’ interactions based on their inferred goals, suggesting humans use a similar process of mental inference to recognize interactions. However, growing behavioral and neuroscience evidence suggests that recognizing social interactions is a visual process, separate from complex mental state inference. Yet despite their success in other domains, visual neural network models have been unable to reproduce human-like interaction recognition. We hypothesize that humans rely on relational visual information in particular, and develop a relational, graph neural network model, SocialGNN. Unlike prior models, SocialGNN accurately predicts human interaction judgments across both animated and natural videos. These results suggest that humans can make complex social interaction judgments without an explicit model of the social and physical world, and that structured, relational visual representations are key to this behavior.

Societal Acceptability of Insect-Based Livestock Feed: A Qualitative Study from Europe

Against the background of high demand for protein-rich feed in the EU and the environmental degradation associated with intensive livestock farming, insect-based feed is discussed as a potential sustainable alternative to conventional feed. However, the establishment of such an innovation depends not only upon technical and economic feasibility, but also on social factors impacting acceptability. The aim of this paper was to determine the acceptability of different social actor groups towards the use of insects as livestock feed, and to gain insights into value-based arguments leading to positive or negative attitudes and perceived benefits and risks. By means of qualitative content analysis, we analysed responses of an EU public consultation process linked to the authorization of insect protein in pig and poultry feed. We found a broad range of value-based arguments that influence the complex judgement process of acceptability that have been neglected in prior quantitative surveys. In particular, our results bring forward more critical voices, such as animal welfare concerns and scepticism that insect-based feed can contribute to a more sustainable livestock system. Based on these findings, we discuss whether insects as feed are a viable contribution to a sustainability-oriented transformation of the agri-food system. We conclude that under certain conditions (when raised on low-value organic side streams such as manure) insect-based livestock feed may contribute to incremental sustainability improvements. Overall, however, the innovation has limitations, not only in terms of regulatory conditions but especially with regard to ethical concerns.

Training needs for staff providing remote services in general practice: a mixed-methods study.

Contemporary general practice includes many kinds of remote encounter. The rise in telephone, video and online modalities for triage and clinical care requires clinicians and support staff to be trained, both individually and as teams, but evidence-based competencies have not previously been produced for general practice. To identify training needs, core competencies, and learning methods for staff providing remote encounters. Mixed-methods study in UK general practice. Data were collated from longitudinal ethnographic case studies of 12 general practices; a multi-stakeholder workshop; interviews with policymakers, training providers, and trainees; published research; and grey literature (such as training materials and surveys). Data were coded thematically and analysed using theories of individual and team learning. Learning to provide remote services occurred in the context of high workload, understaffing, and complex workflows. Low confidence and perceived unmet training needs were common. Training priorities for novice clinicians included basic technological skills, triage, ethics (for privacy and consent), and communication and clinical skills. Established clinicians' training priorities include advanced communication skills (for example, maintaining rapport and attentiveness), working within the limits of technologies, making complex judgements, coordinating multi-professional care in a distributed environment, and training others. Much existing training is didactic and technology focused. While basic knowledge was often gained using such methods, the ability and confidence to make complex judgements were usually acquired through experience, informal discussions, and on-the-job methods such as shadowing. Whole-team training was valued but rarely available. A draft set of competencies is offered based on the findings. The knowledge needed to deliver high-quality remote encounters to diverse patient groups is complex, collective, and organisationally embedded. The vital role of non-didactic training, for example, joint clinical sessions, case-based discussions, and in-person, whole-team, on-the-job training, needs to be recognised.

Shinrin yoku as a pedagogy for peace amidst violence: generating dynamic narratives of Palestine-Israel relations on college campuses

ABSTRACT Amidst violent conflict over Palestine-Israel relations at colleges across America, how might we use our classrooms and campus landscapes to generate dynamic narratives that facilitate peace? Moving beyond a chronological ordering of events, a narrative is a constructed cohesive account of occurrences used to make sense of experiences and motivate action. In violent settings, narratives tend to retrench into static accounts that increase prejudice and motivate greater acts of violence. Alternatively, dynamic narratives offer complex judgement, plot, character, and value assessments of the world thus encouraging more openness to others and peace. I propose a novel intervention for the generation of dynamic narratives. I use the practice of shinrin yoku or guided forest walks in a seminar about Palestine and Israel, to invite liminality, the experience of communal spaces where traditional markers of power and social obligations are stripped. I expected that increasing experiences of shinrin yoku, and in turn liminality, will induce dynamic understandings of Palestinian Israeli relations on campus. Digital diary responses from eleven student participants kept over twelve weeks in a Fall 2022 seminar reveal that even with the eruption of hostilities, 1 students adopted dynamic stories about Palestine and Israel relations when they spent increasing time engaged in shinrin yoku.

The effects of quantity, order, and spatial proximity of elements on subjective complexity judgment

The effects of quantity, order, and spatial proximity of elements on subjective complexity judgment

Tax Compliance: Development of Artificial Intelligence on Tax Issues

The development of robotic technology, especially AI, is able to forecast and present statistical analysis for almost all purposes, including taxes. This study aims to explore the various breakthroughs presented that helped managers and policymakers determine alternatives to improve corporate tax compliance by the qualitative method. The results of this study showed that the implementation of AI has been adapted quite a lot, but it is still not enough, especially in measuring the right tax compliance model for the manufacturing industry, which is the largest tax contributor in Indonesia. AI is said to help managers make more complex judgments with simulated risks. Early detection of tax fraud, leakage of year-end tax shocks, and misuse of tax costs can be minimized by AI optimization. However, there is a discussion about this AI threat on the security aspect that needs to be understood, so that the AI optimization strategy in taxes can mitigate the level of tax audit risk. The recommendation for next study is the exploration of ultimate expectations of corporate tax compliance are apparent.
 Keywords: artificial intelligence, development, tax issues, digitalization, manufacture