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The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state-of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Focusing on hunter-gatherer decision making, Mithen explores the implications of the human mind as a product of biological evolution for the way in which humans solve foraging problems. He draws on studies from ethology, psychology and ethnography prior to turning his attention to prehistoric hunter-gatherers.

Virtual patients are interactive digital simulations of clinical scenarios for the purpose of health professions education. There is no current collated evidence on the effectiveness of this form of education. The goal of this study was to evaluate the effectiveness of virtual patients compared with traditional education, blended with traditional education, compared with other types of digital education, and design variants of virtual patients in health professions education. The outcomes of interest were knowledge, skills, attitudes, and satisfaction. We performed a systematic review on the effectiveness of virtual patient simulations in pre- and postregistration health professions education following Cochrane methodology. We searched 7 databases from the year 1990 up to September 2018. No language restrictions were applied. We included randomized controlled trials and cluster randomized trials. We independently selected studies, extracted data, and assessed risk of bias and then compared the information in pairs. We contacted study authors for additional information if necessary. All pooled analyses were based on random-effects models. A total of 51 trials involving 4696 participants met our inclusion criteria. Furthermore, 25 studies compared virtual patients with traditional education, 11 studies investigated virtual patients as blended learning, 5 studies compared virtual patients with different forms of digital education, and 10 studies compared different design variants. The pooled analysis of studies comparing the effect of virtual patients to traditional education showed similar results for knowledge (standardized mean difference [SMD]=0.11, 95% CI -0.17 to 0.39, I =74%, n=927) and favored virtual patients for skills (SMD=0.90, 95% CI 0.49 to 1.32, I =88%, n=897). Studies measuring attitudes and satisfaction predominantly used surveys with item-by-item comparison. Trials comparing virtual patients with different forms of digital education and design variants were not numerous enough to give clear recommendations. Several methodological limitations in the included studies and heterogeneity contributed to a generally low quality of evidence. Low to modest and mixed evidence suggests that when compared with traditional education, virtual patients can more effectively improve skills, and at least as effectively improve knowledge. The skills that improved were clinical reasoning, procedural skills, and a mix of procedural and team skills. We found evidence of effectiveness in both high-income and low- and middle-income countries, demonstrating the global applicability of virtual patients. Further research should explore the utility of different design variants of virtual patients.

Global capital markets have undergone fundamental transformations in recent years and, as a result, have become extraordinarily complex and opaque. Trading space is no longer measured in minutes or seconds but in time units beyond human perception: milliseconds, microseconds, and even nanoseconds. Technological advances have thus scaled up imperceptible and previously irrelevant time differences into operationally manageable and enormously profitable business opportunities for those with the proper high-tech trading tools. These tools include the fastest private communication and trading lines, the most powerful computers and sophisticated algorithms capable of speedily analysing incoming news and trading data and determining optimal trading strategies in microseconds, as well as the possession of gigantic collections of historic and real-time market data. 0Fragmented capital markets are also becoming a rapidly growing reality in Europe and Asia, and are an established feature of U.S. trading. This raises urgent market governance issues that have largely been overlooked. Global Algorithmic Capital Markets seeks to understand how recent market transformations are affecting core public policy objectives such as investor protection and reduction of systemic risk, as well as fairness, efficiency, and transparency. 0The operation and health of capital markets affect all of us and have profound implications for equality and justice in society. This unique set of chapters by leading scholars, industry insiders, and regulators discusses ways to strengthen market governance for the benefit of society at whole.

The landscape of general surgery education has undergone a significant transformation over the past few years, driven in large part by the advent of surgical simulation and training technologies. These innovative tools have revolutionized the way surgeons are trained, allowing for a more immersive, interactive, and effective learning experience. In this review, we will explore the impact of surgical simulation and training technologies on general surgery education, highlighting their benefits, challenges, and future directions. Enhancing the technical proficiency of surgical residents is one of the main benefits of surgical simulation and training technologies. By providing a realistic and controlled environment, With the use of simulations, residents may hone their surgical skills without compromising patient safety. Research has consistently demonstrated that training with simulations enhances surgical skills., reduces errors, and enhances overall performance. Furthermore, simulators can be programmed to mimic a wide range of surgical scenarios, enabling residents to cultivate the essential critical thinking and decision-making abilities required to manage intricate surgical cases. Another area of development is incorporating simulation-based training into the wider surgical curriculum. As simulation technologies become more widespread, they will need to be incorporated into the fabric of surgical education, rather than simply serving as an adjunct to traditional training methods. This will require a fundamental shift in the way surgical education is delivered, with a greater emphasis on simulation-based training and assessment. Highlights Surgical simulation and training technologies have revolutionized general surgery education, enhancing technical skills and critical thinking abilities of surgical residents. Integration of simulation-based training into the broader surgical curriculum is necessary for its widespread adoption and effectiveness. With the support of educational agendas led by national neurosurgical committees, industry and new technology, simulators will become readily available, translatable, affordable, and effective. As specialized, well-organized curricula are developed that integrate simulations into daily resident training, these simulated procedures will enhance the surgeon’s skills, lower hospital costs, and lead to better patient outcomes.

Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. This study aimed to explore the role of large language models (LLMs) in mitigating these biases through the use of the multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy compared with humans. A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent framework, we leveraged GPT-4 (OpenAI) to facilitate interactions among different simulated agents to replicate clinical team dynamics. Each agent was assigned a distinct role: (1) making the final diagnosis after considering the discussions, (2) acting as a devil's advocate to correct confirmation and anchoring biases, (3) serving as a field expert in the required medical subspecialty, (4) facilitating discussions to mitigate premature closure bias, and (5) recording and summarizing findings. We tested varying combinations of these agents within the framework to determine which configuration yielded the highest rate of correct final diagnoses. Each scenario was repeated 5 times for consistency. The accuracy of the initial diagnoses and the final differential diagnoses were evaluated, and comparisons with human-generated answers were made using the Fisher exact test. A total of 240 responses were evaluated (3 different multi-agent frameworks). The initial diagnosis had an accuracy of 0% (0/80). However, following multi-agent discussions, the accuracy for the top 2 differential diagnoses increased to 76% (61/80) for the best-performing multi-agent framework (Framework 4-C). This was significantly higher compared with the accuracy achieved by human evaluators (odds ratio 3.49; P=.002). The multi-agent framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. In addition, the LLM-driven, multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.

In the field of health care, knowledge and clinical reasoning are key with regard to quality and confidence in decision making. The development of knowledge and clinical reasoning is influenced not only by students' intrinsic factors but also by extrinsic factors such as satisfaction with taught content, pedagogic resources and pedagogic methods, and the nature of the objectives and challenges proposed. Nowadays, professors play the role of learning facilitators rather than simple \"lecturers\" and face students as active learners who are capable of attributing individual meanings to their personal goals, challenges, and experiences to build their own knowledge over time. Innovations in health simulation technologies have led to clinical virtual simulation. Clinical virtual simulation is the recreation of reality depicted on a computer screen and involves real people operating simulated systems. It is a type of simulation that places people in a central role through their exercising of motor control skills, decision skills, and communication skills using virtual patients in a variety of clinical settings. Clinical virtual simulation can provide a pedagogical strategy and can act as a facilitator of knowledge retention, clinical reasoning, improved satisfaction with learning, and finally, improved self-efficacy. However, little is known about its effectiveness with regard to satisfaction, self-efficacy, knowledge retention, and clinical reasoning. This study aimed to evaluate the effect of clinical virtual simulation with regard to knowledge retention, clinical reasoning, self-efficacy, and satisfaction with the learning experience among nursing students. A randomized controlled trial with a pretest and 2 posttests was carried out with Portuguese nursing students (N=42). The participants, split into 2 groups, had a lesson with the same objectives and timing. The experimental group (n=21) used a case-based learning approach, with clinical virtual simulator as a resource, whereas the control group (n=21) used the same case-based learning approach, with recourse to a low-fidelity simulator and a realistic environment. The classes were conducted by the usual course lecturers. We assessed knowledge and clinical reasoning before the intervention, after the intervention, and 2 months later, with a true or false and multiple-choice knowledge test. The students' levels of learning satisfaction and self-efficacy were assessed with a Likert scale after the intervention. The experimental group made more significant improvements in knowledge after the intervention (P=.001; d=1.13) and 2 months later (P=.02; d=0.75), and it also showed higher levels of learning satisfaction (P<.001; d=1.33). We did not find statistical differences in self-efficacy perceptions (P=.9; d=0.054). The introduction of clinical virtual simulation in nursing education has the potential to improve knowledge retention and clinical reasoning in an initial stage and over time, and it increases the satisfaction with the learning experience among nursing students.

The health care industry must align with new digital technologies to respond to existing and new challenges. Digital twins (DTs) are an emerging technology for digital transformation and applied intelligence that is rapidly attracting attention. DTs are virtual representations of products, systems, or processes that interact bidirectionally in real time with their actual counterparts. Although DTs have diverse applications from personalized care to treatment optimization, misconceptions persist regarding their definition and the extent of their implementation within health systems. This study aimed to review DT applications in health care, particularly for clinical decision-making (CDM) and operational decision-making (ODM). It provides a definition and framework for DTs by exploring their unique elements and characteristics. Then, it assesses the current advances and extent of DT applications to support CDM and ODM using the defined DT characteristics. We conducted a scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol. We searched multiple databases, including PubMed, MEDLINE, and Scopus, for original research articles describing DT technologies applied to CDM and ODM in health systems. Papers proposing only ideas or frameworks or describing DT capabilities without experimental data were excluded. We collated several available types of information, for example, DT characteristics, the environment that DTs were tested within, and the main underlying method, and used descriptive statistics to analyze the synthesized data. Out of 5537 relevant papers, 1.55% (86/5537) met the predefined inclusion criteria, all published after 2017. The majority focused on CDM (75/86, 87%). Mathematical modeling (24/86, 28%) and simulation techniques (17/86, 20%) were the most frequently used methods. Using International Classification of Diseases, 10th Revision coding, we identified 3 key areas of DT applications as follows: factors influencing diseases of the circulatory system (14/86, 16%); health status and contact with health services (12/86, 14%); and endocrine, nutritional, and metabolic diseases (10/86, 12%). Only 16 (19%) of 86 studies tested the developed system in a real environment, while the remainder were evaluated in simulated settings. Assessing the studies against defined DT characteristics reveals that the developed systems have yet to materialize the full capabilities of DTs. This study provides a comprehensive review of DT applications in health care, focusing on CDM and ODM. A key contribution is the development of a framework that defines important elements and characteristics of DTs in the context of related literature. The DT applications studied in this paper reveal encouraging results that allow us to envision that, in the near future, they will play an important role not only in the diagnosis and prevention of diseases but also in other areas, such as efficient clinical trial design, as well as personalized and optimized treatments.

Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise—various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in SO as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.