Real Decision Research Articles

Medical judicial expertise: the real decision-maker in Chinese medical dispute litigation

PurposeThis article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.Design/methodology/approachThis article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.FindingsHigh reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.Originality/valueThe current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

The psychology of life's most important decisions.

Research on judgment and decision making typically studies "small worlds"-highly simplified and stylized tasks such as monetary gambles-among homogenous populations rather than big real-life decisions made by people around the globe. These transformative life decisions (e.g., whether or not to emigrate or flee a country, disclose one's sexual orientation, get divorced, or report a sexual assault) can shape lives. This article argues that rather than reducing such consequential decisions to fit small-world models, researchers need to analyze their real-world properties. Drawing on principles of bounded and ecological rationality, it proposes a framework that identifies five dimensions of transformative life decisions: conflicting cues, change of self, uncertain experiential value, irreversibility, and risk. The framework also specifies simple, versatile choice strategies that address these dimensions by, for instance, breaking down a decision into steps, avoiding trade-offs between present and future selves, or sampling others' experiences. Finally, it suggests benchmarks for assessing the rationality of transformative life decisions. Methodologically, this framework adapts a long tradition of mainly lab-based judgment and decision-making research to a text-based approach, thereby setting the stage for empirical work that analyzes real-world decisions using natural-language processing. Only by understanding decisions with the potential to transform life trajectories-and people in the process-will it be possible to develop encompassing and inclusive theories of human decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS.

Explainable Artificial Intelligence (XAI) in Healthcare: Enhancing Transparency and Trust

Artificial Intelligence has now taken a full-fledged role in healthcare and has started driving innovations not only in diagnostics and treatment planning but also in patient monitoring and operational efficiency. This will enable complex medical data analysis, extracting patterns and insights that no human is capable of. However, most of these models are per se opaque-that is, the so-called "black-box" problem-there are still great challenges in areas such as transparency, trust, and ethical applications in a clinical setting. This lack of interpretability can stand in the way of acceptance or integration for AI technologies when issues of understanding and accountability are relevant. Explainable AI solves these problems by making real artificial intelligence decisions understand the decisions made to humans. XAI techniques offer well-understandable and interpretable explanations of the models with minimum degradations in performance. This review article explains in detail the critical role of XAI in healthcare, underpinning how this field can bring more transparency into AI applications. We explain some of the current methods of XAI: model-agnostic techniques like LIME and SHAP, interpretable models relating to decision trees and linear models, and visualization techniques like saliency maps and mechanisms of attention.

Simpson�s Paradox: Conceptual Foundational Implications in Real-Life Decision-Making in Quantitative Investment and Trading

Pankaj Mani writes that a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined raises foundational issues in concluding the significance of statistical relationships among different variables reliably in real-world decision-making for trading, investing, risk management.

Why Kahneman matters

Abstract Daniel Kahneman's legacy is best understood in light of developments in economic theory in the early-mid-20th century, when economists were eager to put utility functions on a firm mathematical foundation. The axiomatic system that provided this foundation was not originally intended to be normative in a prescriptive sense but later came to be seen that way. Kahneman took the axioms seriously, tested them for descriptive accuracy, and found them wanting. He did not view the axioms as necessarily prescriptive. Nevertheless, in the research program he conceived, factual discoveries about real decision-making were stated as deviations from the axioms and thus deemed ‘errors’. This was an unfortunate turn that needs to be corrected for the psychological enrichment of economics to proceed in a productive direction.

Artificial intelligence and real decisions: predictive systems and generative AI vs. emotive-cognitive legal deliberations

IntroductionActivation of NLRP3-containing inflammasome, which is responsible for IL-1β maturation, has been shown to contribute to Alzheimer’s disease (AD)-associated pathogenesis in both APP- and tau-transgenic mice. However, effects of NLRP3 on pericytes and subsequent cerebrovascular pathology in AD remain unknown.MethodsNLRP3-deficient and wild-type AD animal models were generated by crossing human P301S tau-transgenic mice and Nlrp3 knockout mice. AD-associated neuroinflammation, tauopathy, vasculature and pericyte coverage in the brain were investigated using immunohistological and molecular biological methods. To investigate how NLRP3 regulates pericyte activation and survival, pericytes from the brains of Nlrp3 knockout and wild-type mice were cultured, treated with IL-1β and H2O2 at different concentrations and analyzed by confocal microscopy and flow cytometry after staining with fluorescently labelled phalloidin, annexin-V and PDGFRβ antibody.ResultsDeficiency of NLRP3 (1) reduced Iba-1, GFAP and AT8 antibody-immunoreactive phosphorylated tau-positive cells, without significantly altering transcription of inflammatory genes, (2) preserved cerebral vasculature and pericyte coverage and up-regulated Osteopontin gene transcription, and (3) improved cognitive function in tau-transgenic mice. In cell culture, NLRP3 deficiency prevented pericyte apoptosis. Treatment with IL-1β or H2O2 increased the expression of PDGFRβ in NLRP3-deficient pericytes, but decreased it in NLRP3 wild-type pericytes in a dose-dependent manner.DiscussionInhibition of NLRP3 can promote pericyte survival, improve cerebrovascular function, and attenuate AD pathology in the brain of tau-transgenic mice. Our study supports NLRP3 as a novel therapeutic target for Alzheimer’s patients.

AN EMPIRICAL EXAMINATION OF THE VALIDITY OF GENDER-INDEXED PHILLIPS CURVE IN TURKIYE

The potential existence and structure of the relationship between inflation and unemployment is a frequently examined topic in the literature. A.W.H. Phillips (1958) explored the correlation between these two significant economic variables in the context of the United Kingdom, highlighting the trade-off relationship between them. This seminal work has led various economists to rigorously investigate the same phenomenon across different countries and country groups. The primary reason for this intense scrutiny is the assertion that monetary policies implemented in response to economic shocks may push these two variables in opposite directions. In this regard, the significant shocks experienced in Turkey in recent years concerning inflation and unemployment have made it particularly intriguing to test the validity of the Phillips Curve for Turkey. The aim is to provide economic decision-makers with findings that can guide them in formulating more realistic, original, and valid policy decisions. This study tests the validity of the Phillips Curve relationship for Turkey. Utilizing monthly data from 2005 to 2023, the unemployment rates are modeled separately for male and female unemployment rates, distinguishing this study from others. The analysis employs the Johansen Cointegration Test and concludes that the Phillips Curve theory holds true for Turkey.

Counterfactual curiosity in real decisions: The roles of outcome valence and aging.

Non-instrumental counterfactual curiosity (i.e., the search for information about forgone options that is not useful for improving future outcomes) has especially been observed after outcomes perceived as negative and, consequently, attributed to forms of regret management. In three online experiments (N = 620), we extended the study of counterfactual curiosity about economically incentivized decisions in younger and older adults. Participants played independent rounds of a card-drawing game by choosing one of two decks to turn over the top, covered card, which could increase, decrease, or have no effect on an initial endowment. Following that, they could examine the top card of the other deck to see if and how the outcome could have differed. Experiment 1 featured identical decks, making the choice between them random. In Experiment 2, participants made a deliberate choice between a riskier and a safer deck, each varying in the extremity of potential wins and losses. In Experiment 3, the decks were identical to those in Experiment 2, but access to counterfactual information was contingent upon participants forfeiting part of their endowment. Results showed a relevant portion of both younger and older adults displayed curiosity for non-instrumental counterfactual information, especially when it was free and likely to reveal that the forgone option would have been better than the chosen one. Older adults exhibited a higher level of curiosity than younger counterparts only when choices were deliberate and counterfactual information was free. These findings are discussed in relation to current perspectives on the regret-management function of counterfactual curiosity.

A causal role of the right dorsolateral prefrontal cortex in random exploration

Decision to explore new options with uncertain outcomes or exploit familiar options with known outcomes is a fundamental challenge that the brain faces in almost all real-life decisions. Previous studies have shown that humans use two main explorative strategies to negotiate this explore-exploit tradeoff. Exploring for the sake of information is called directed exploration, and exploration driven by behavioral variability is known as random exploration. While previous neuroimaging studies have shown different neural correlates for these explorative strategies, including right frontopolar cortex (FPC), right dorsolateral prefrontal cortex (DLPFC), and dorsal anterior cingulate cortex (dACC), there is still a lack of causal evidence for most of these brain regions. Here, we focused on the right DLPFC, which was previously supported to be involved in exploration. Using the continuous theta burst stimulation (cTBS) and Horizon task on twenty-five healthy right-handed adult participants, we showed that inhibiting rDLPFC did not change directed exploration but selectively reduced random exploration, by increasing reward sensitivity over the average reward of each option. This suggests a causal role for rDLPFC in random exploration, and further supports dissociable neural implementations for these two explorative strategies.

Assessment on Maritime Students' Awareness, Perceptions and Needs in Career Planning in Tertiary Education

Although seafaring is seen as an attractive profession for prospective students in terms of having the opportunity to see different parts of the world while working and earning more money in less time compared to other professions, the harsh conditions of duties onboard sometimes overshadow this attractiveness, which may lead students to question their choices and seek different career alternatives. This study aims at assessing maritime students' knowledge, perceptions, and needs regarding career awareness and career planning at the tertiary level. The study also seeks to understand students' awareness of career-related activities and the role and responsibilities of the career centre. The research component of the study involves administering a questionnaire to 381 first-year students at a maritime-focused university. The questionnaire comprises twenty questions based on a five-point Likert scale and has been electronically distributed. The data collected has been analysed using parametric methods based on four variables: career anchor, career awareness, career centre awareness, and career planning awareness. The study has revealed that most of the students are aware of their own characteristics and the sacrifices required by their future profession, but they had not yet decided which career path to follow after graduation. The study has also revealed that students are interested in developing their skills through extracurricular activities and consider entrepreneurship as a future career option. The results suggest that career offices in maritime universities should create more opportunities to provide students with adequate information about their future careers and to enable them to make correct and realistic decisions about their career paths. The next study on this topic can focus on how to raise awareness of the functions of career officers and how to develop maritime-oriented career planning courses so that the students can make more realistic and satisfying career choices.

Short term efficacy of biological treatment for moderate-to-severe plaque psoriasis: a systematic review and network meta-analysis.

Psoriasis is a chronic inflammatory disease that is debilitating, particularly in its more severe forms. Multiple systemic therapies are used in moderate-to-severe psoriasis, but the development of biological interventions has revolutionized its management and improved its outcomes. To compare the effectiveness and safety of the different biological interventions approved for use in moderate-to-severe plaque psoriasis. Multiple databases were searched for relevant articles and a prospectively planned network meta-analysis was conducted on randomized controlled trials that assessed biological treatments in moderate-to-severe psoriasis. The search yielded 84 trials that encompassed 39,798 patients. Infliximab 5mg/kg had the highest probability of achieving 75% reduction on PASI scale in comparison to placebo (RR = 18.76, 95% CI [12.31; 28.57], high certainty), while Ixekizumab 80mg and Brodalumab 210mg had the highest probability at achieving PASI90 and PASI100 (37.81, [28.57; 50.03] and 81.04, [26.16; 251.01], respectively, with moderate certainty) On the other hand, Risankizumab 150mg and Ustekinumab 90mg were the only regimens with significantly less withdrawal rates due to adverse events (0.41, [0.18-0.96], and 0.57, [0.35-0.91], respectively with High certainty) compared to placebo. Anti-IL17 and Infliximab were among the most effective in ameliorating the symptoms of psoriasis, however, anti-IL17 were better at achieving full or almost full improvement on the PASI scale. Real life decision-making is not so clear-cut and should remain patient centered, taking into consideration factors such as safety, comorbidities, biologic naivety, dosing preferences and insurance considerations.

A Serious Game for the Digital Transformation of Organizations

To aid human resource management professionals design a configuration of human resource management practices that facilitates the employee behaviour needed for the digital transformation, a serious game was created. The serious game challenges human resource management professionals to combine and design human resource management practices that align to the organizational strategy and optimize employee digital transformation behaviour over multiple simulated years. In this paper, the underlying rationale, design, and outcomes of the first test rounds of the game are presented. Organizational change shaped by the diffusion of technology is labelled the digital transformation (Hanelt et al., 2021). Employees can accelerate or slow down the digital transformation. Hence, organizations are looking to facilitate digital transformation enhancing employee behaviours: adaptive performance and proactive agility behaviour (Collou and Bruinsma, 2022). Aligned human resource management configurations (i.e., combination of human resource management practices such as recruitment, selection, and job design) can shape that employee behaviour (Gratton and Truss, 2003; Delery and Doty, 1996). Designing such a human resource management configuration however is challenging. There are many human resource management practices that need to be designed over multiple years and effects on employee behaviour vary. Serious games are well suited tools to enhance understanding of challenges such as strategic human resource management design. They allow professionals to experiment with choices and gauge the outcomes of their selection without the need to test these decisions in real life. Prior work (Collou and Bruinsma, 2022, 2019) illustrated that a serious game can aid human resource management professionals in their quest to design human resource management configurations that align to the organizational strategy. In the current game an additional challenge was added: shaping digital transformation behaviour. Tested in multiple rounds the game shows great promise for professionals and students to experiment with human resource management configuration design. First evaluations illustrate that responders are positive in terms of their learning experience. Balancing the need to align the human resource management configuration and affecting the employee behaviour simultaneously has proven to be challenging for players of the game. Future steps are focused on this balance and empirically validating the serious game.

A Comparative Study of The Application of NPV And IRR To Real Estate Investment Decision- Making

The China real estate industry is undergoing a fluctuation nowadays. And this paper is going to investigate this industry by researching a leading enterprise, CHINA VANKE CO., LTD. with the assistance of two financial instruments, Net Present Value (NPV) and Internal Rate of Return (IRR). Furthermore, the paper has another purpose that is comparing the properties between NPV and IRR. This paper uses literature research method and case study to analyse the situation of Vanke and list the possible considerations while having the analysis. After a series of research, the author considers NPV as a better indicator compared to IRR. NPV has the advantage of simple marginal determination, however, the related indicator, discount rate cannot be determined easily and accurately in the real estate analysis since it can be affected by many factors. For advantage of IRR, it is simple to calculate the value because it is only decided by the cashflows. On the other hand, IRR cannot clarify the real estate investment decision-making by itself, it must have the assistance of NPV versus discount rate graph. Consequently, the mixed employment would present a clearer and more comprehensive analysis for investors.

Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning

Large Language Models (LLMs) excel in tasks from translation to complex reasoning. For AI systems to help effectively, understanding and predicting human behavior and biases is essential. However, it remains an open question whether LLMs can achieve this goal. This paper addresses this gap by leveraging the reasoning and generative capabilities of LLMs to predict human behavior in two sequential decision-making tasks. These tasks involve balancing between exploratory and exploitative actions and handling delayed feedback, which is essential for simulating real-life decision processes. We compare the performance of LLMs with a cognitive instance-based learning (IBL) model, which imitates human experiential decision-making. Our findings indicate that LLMs excel at rapidly incorporating feedback to enhance prediction accuracy. In contrast, the IBL model better accounts for human exploratory behaviors and effectively captures loss aversion bias — the tendency to choose a sub-optimal goal with fewer step-cost penalties rather than exploring to find the optimal choice, even with limited experience. The results highlight the benefits of integrating LLMs with cognitive architectures, suggesting that this synergy could enhance the modeling and understanding of complex human decision-making patterns.

Game Theory Applications In The Airbus-Boeing Dispute

This study examines the intricate trade dispute between Airbus and Boeing which took place from the late 1970s all the way till 2021. By using game theory tools including Nash equilibrium, mixed strategies, Stackelberg competition and credible threats, I can explore the strategic trade decisions, such as tarif s, subsidies and complaints to the WTO, taken by each player and how this af ects the overall competitiveness and the health of the LCA sector. Each of these games use data from the real world in order to ef ectively depict the outcomes that could have arisen and those that actually happened. The key findings were able to illustrate how although some of the theoretical economic ideologies were used, the real life decisions still deviated from the stable outcomes due to external pressures and factors. The results provide us a more intricate understanding of the trade world and the economic environment of the LCA industry

Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate

In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.

Increasing recycling with charitable incentives

Many businesses have moved toward a circular economy, collecting their packaging material from customers to be reintroduced into their remanufacturing processes. For these programs to be successful, individuals must voluntarily, and often effortfully, dispose of their used materials in specific ways. The goal of the present research is to evaluate the effectiveness of charitable incentives in promoting effortful recycling. We explore whether the type of charitable recipient – human or non-human – differently promotes effortful recycling, and examine how people's environmental values may moderate these effects. Five studies (N = 3572) utilizing real disposal decisions and consequential disposal intentions demonstrate that when individuals are informed that by recycling an item a donation will be made to a non-profit organization, effortful recycling increases—an effect that is observed for both environmental and youth recipients (Studies 1–4b). However, an environmental charitable recipient was less motivating for people who had less concern for the environment (Studies 3 and 4b). We find that an environmental charitable recipient increases effortful recycling because of an enhanced perception of making an impact, while a youth charitable recipient increases effortful recycling through greater empathy (Studies 4a and 4b). We conclude that efforts to increase material recovery using charitable incentives can be effective, and should carefully consider the values of their intended audience along with charitable recipient type.

Response Coordination Model for Real Time Decision Making in Coastal Search and Rescue Operation Using Fuzzy Logic Technique

Coastal search and rescue (SAR) operations are complex, involving dynamic and uncertain conditions that demand real-time, effective decision-making. This paper aimed to analyze response coordination for real- time decision-making in coastal SAR operations using the fuzzy logic technique. The main aims of this study were to identify the key parameters and linguistic variables critical for effective decision-making in SAR operations and finally to design a fuzzy logic model tailored to the dynamic and uncertain conditions inherent in coastal SAR operations. The proposed fuzzy logic model demonstrated improved responsiveness and adaptability to changing conditions, offering a more robust framework for decision-making in SAR operations. However, this study contributes to enhancing the efficiency and effectiveness of real time decision making in SAR operations in coastal environments, with broader implications for maritime safety.

What Affects the Value of Our Time? The Case of Buying a Present vs. Buying for Ourselves and the Impact of Decision-Making Styles.

We study how the buying purpose affects the trade-off between time and money. We consider the case of buying something for ourselves versus buying a gift and the decision whether to spend time traveling to a cheaper store to save money. We hypothesized that when purchasing a gift, people make less effort to save money, and therefore will be less willing to spend time traveling to a cheaper store when they buy a gift. In experiments with several scenarios, we fail to find support for this hypothesis. We also explore the impact of telling subjects that the purchase is made abroad. This is hypothesized to increase the valuation of the buyer's time. We also hypothesize that the interaction of being abroad and buying a gift will be negative. The data support both hypotheses. Subjects answered 15 questions about their decision-making style (rational, intuitive, and spontaneous). Subjects with more rational decision-making exhibit lower time value, which is likely to be closer to the real one. Subjects with more rational decision-making also show more strongly that time abroad is more valuable. These results suggest that questions about decision-making style are correlated with decision-making in scenarios of realistic purchase decisions.