Coordination with Constrained Resources; A Modeling Framework

Collective Wisdom and Multidisciplinary Tumor Boards

A case study activity to assess nursing students′ perceptions of their interprofessional healthcare team’s collaborative decision-making process

Decisionmaking functions in many large scale systems are distributed over multiple human decisionmakers who may have access to resources at different locations and are responsible for processing randomly arriving tasks. This leads to a complex team resource allocation and coordination problem. This paper presents a mathematical model and a solution methodology to investigate human team task processing behaviors based on a laboratory experiment. The problem at the current stage of investigation is formulated as a centralized scheduling problem involving multiple tasks and multiple platforms (vehicles equipped with resources). The geographical factors (e.g., locations and velocities) and combination of platforms for task processing are considered. A solution methodology is developed by combining decision tree and the concept of "attractiveness measure." This method is then applied to solve the experimental cases, and the solution methodology generates results similar to those obtained from the human experiment and captures human decisionmaking behaviors. Further model-dats comparison reveals several hidden tendencies in human team behavior, e.g., human teams adapts to tempo by speeding up resource usage but sacrificing efficiency. This model and solution methodology will be extending to a distributed case to resemble the coordination between team members, and a normative-descriptive model will be developed to analyze human decisionmaking.

This paper investigates how individual decisions are aggregated to team decisions under risk in field experiment with a sample of Ethiopian farmers and the team decision-making process behind this aggregation. In an experiment structurally similar to the Gneezy-Potters' risk game, subjects make decisions first individually and then jointly with a random anonymous partner. In the aggregate, teams make higher allocation into the risky asset compared to the average of individual decisions. However, teams are neither polarizing nor simply averaging individual preferences. Instead, team decisions are consistent with an outcome of group bargaining and deliberation process based on intensity of individual preferences. But more risk-taking subjects have a stronger influence on team decisions. This influence of more risk-taking members on the group decision is leveraged by their better education levels. Analysis of the team decision-making process reveals several interesting insights. About 54% of the teams do not reach immediate agreement with initial allocations. Both less risk-taking and more risk-taking team members are equally likely to disagree with initial allocations but for different reasons. Teams that disagreed with initial allocations reached final team decisions significantly different from disagreed initial allocations. Less risk-taking subjects are more willing to concede to allocations proposed by more risk-taking subjects to reach at an agreed team decision. Demanding messages in group communications have a stronger effect on outcomes of group decision-making. Finally, teams with greater differences in willingness to take risk among members are more likely to disagree with initial team allocations, take more rounds of deliberations to come to a decision, and make choices further away from the average of individual decisions. Our results permit to better characterize the process of group decision-making beyond differences between individual and group decisions.

The literature on team members’ satisfaction has mainly focused on analysis at the team level; however, studies of team members’ satisfaction at the individual level are less frequent. In this work, we analyzed how team members’ attitudes and perceptions developed before starting the team decision-making process, how their perceptions developed during the team decision-making process, and how the consequences of the team decision-making process influence individual satisfaction with the team. Questionnaires were filled out by 84 undergraduate students working in 28 teams that had to make a management decision. The results show that individual satisfaction with the team is positively related to team members’ self-efficacy for teamwork and to their perception of the decision’s comprehensiveness, but negatively related to their perception of team debate and the deviation between their individual preference and the team decision.

Large Language Models (LLMs) demonstrate impressive capabilities across diverse tasks, yet their safety mechanisms remain susceptible to adversarial exploitation of cognitive biases---systematic deviations from rational judgment. Unlike prior studies focusing on isolated biases, this work highlights the overlooked power of multi-bias interactions in undermining LLM safeguards. Specifically, we propose CognitiveAttack, a novel red-teaming framework that adaptively selects optimal ensembles from 154 human social psychology-defined cognitive biases, engineering them into adversarial prompts to effectively compromise LLM safety mechanisms. Experimental results reveal systemic vulnerabilities across 30 mainstream LLMs, particularly open-source variants. CognitiveAttack achieves a substantially higher attack success rate than the SOTA black-box method PAP (60.1% vs. 31.6%), exposing critical limitations in current defenses. Through quantitative analysis of successful jailbreaks, we further identify vulnerability patterns in safety-aligned LLMs under synergistic cognitive biases, validating multi-bias interactions as a potent yet underexplored attack vector. This work introduces a novel interdisciplinary perspective by bridging cognitive science and LLM safety, paving the way for more robust and human-aligned AI systems.

The rising prevalence of harassment in Middle Eastern countries is mirrored in literary works from the region. However, extracting data from these texts to understand the typology and frequency of the cases poses a significant challenge due to human cognitive limitations and potential biases. Thus, this study aims to use natural language processing (NLP) approaches to propose a machine learning framework for text mining of sexual harassment content in literary texts. The data source for this study consists of twelve Middle Eastern novels. The proposed framework involves the classification of physical and non-physical types of sexual harassment using a machine-learning model. Lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis. Finally, a long short-term memory-gated recurrent unit (LSTM-GRU) deep learning model is built to classify the sentiment characteristics that induce sexual harassment. The proposed model achieved an accuracy of 75.8% while outperforming five other algorithms. Additionally, a sentiment classification with three labels—negative, positive, and neutral—was developed using an LSTM-GRU RNN deep learning model. The accuracy of this model was 84.5%. Most statements, even those involving physical sexual harassment, which had greater levels of sexual harassment, had negative sentiments, according to lexicon-based sentiment analysis. This study contributes to the field of text mining by providing a novel approach to identifying instances of sexual harassment in literature in English from the Middle East. The use of machine learning models and sentiment analysis techniques allows for more accurate identification and classification of different types of sexual harassment. Furthermore, this study sheds light on the prevalence of sexual harassment in Middle Eastern countries and highlights the need for further research and action to address this issue.

Combining conscious and unconscious knowledge within human-machine-interfaces to foster sustainability with decision-making concerning production processes

Task. Risk management is a structured approach to identifying, assessing and minimizing risks that may affect the achievement of an organization's goals. The subject of the study is the methodology of risk management of an organization. The object of the study is the risk management system of an organization. The problems are mainly related to the fact that risk management is often based on incomplete or outdated data, which makes it difficult to accurately assess risks. Assessing the probability and consequences of risks can be subjective and depend on the opinion of experts, which can lead to erroneous conclusions. Many methodologies do not take into account uncertainty and volatility, which can lead to underestimation of risks. Risk management is sometimes not integrated into the overall strategy of the company, which makes it less effective. At the same time, new technologies, economic conditions or legislative changes can affect the outcomes. Also, the human factor and cognitive biases can affect the perception and management of risks, for example, ignoring rare but catastrophic events, and the negative influence of management culture, manifested in the lack of encouragement for open discussion of risks or the use of tools to reduce them, negatively affects the results of the organization. Model. To substantiate the methodological aspects of forming the organization's risk management in accordance with the standards. The theoretical and methodological basis of the study was the works of leading domestic and foreign scientists in the field of theory and methodology of risk management of organizations. In the course of the study, a set of general scientific and special methods was used. Analysis and synthesis were used to identify the essential characteristics and components of the risk management system. Conclusions. The methodology of forming the organization's risk management in accordance with the management standards is of theoretical significance in terms of developing scientific and theoretical provisions concerning the construction of a risk management system and improving its efficiency. Practical significance. The practical significance of the study lies in the formation of applied recommendations for forming the risk management system in the context of increasing the balance and adaptability of the organization's strategic priorities to changes in the internal and external environment. The developed methodological approaches to forming risk management, taking into account the parameters of the organization's risk sustainability, are aimed at improving the efficiency of implementing the results of activities in a positive way. Originality. Methodological aspects of forming the organization's risk management in adaptation to modern conditions of economic processes development.

Virtual work opened a whole lot of change when it came to the cybersecurity landscape. Technology has made many contributions to secure remote workplaces, but the human element has always been a targeted attack vector to mitigate cyber threats. This paper explores the influence of human behavior and cognitive biases on the vulnerabilities that affect remote work environments due to poor security decisions coming because of some particular biases and how the features of remote work amplify their effects. The paper also suggests appropriate techniques for organizations on how to tackle these issues through effective training, awareness programs, and by improving security policies. Understanding and mitigating the human factors involved in Cybersecurity would give organizations an edge in developing more secure and resilient remote work environments.

The Behavioural Economics of Cybersecurity, Emphasizing the Role of Human Behaviour, Cultural Influences, and Cognitive Biases in Shaping Cybersecurity Vulnerabilities and Solutions.

This article explores the emerging field of Behavioral Macroeconomics, which seeks to integrate insights from psychology into traditional economic models. The intersection of behavioral science and macroeconomics opens new avenues for understanding economic phenomena by incorporating human behavior, emotions, and cognitive biases into the analysis. We examine key principles, methodologies, and challenges in this interdisciplinary approach, emphasizing the potential implications for policy-making and the broader understanding of economic dynamics.

On occasion, I am appointed by our local probate court to help make health care decisions for patients who are no longer capable of making these decisions for themselves. I'll visit my client in the hospital and review his or her chart, and I'll have numerous opportunities to talk about the illness and treatment options with attending physicians, residents, and nurses. My phone calls to them will be returned promptly. If doctors arrive at the conclusion that nothing further can be done for the patient, we'll discuss how best to proceed. This end‐of‐life talk never comes as a surprise to me because I will have been included in the team's decision‐making process, in the manner of professionals interacting with another professional.Last fall, I became a family surrogate decision‐maker after my mother was hospitalized over the course of a few months, for a number of reasons, in three hospitals in New York. I assumed I would manage her care as I did my clients’, with a sense of control and involvement in the process, but I quickly learned that I had in fact entered a kind of alternate universe as far as decision‐making was concerned.

Individual Long-Term Mortality PredictionFollowing Either Coronary Stenting orBypass Surgery in PatientsWith Multivessel and/or Unprotected Left MainDisease: An External Validation of the SYNTAX Score II Model in the 1,480 Patients of the BEST and PRECOMBAT Randomized Controlled Trials.

Error management theory (EMT; Haselton & Buss, 2000) draws on parental investment theory (Trivers, 1972) and signal detection to make novel predictions about human cognitive biases and their adaptive implications. EMT predicts that heterosexual men overperceive sexual interest from women, whereas women underperceive honest signals of relationship commitment from men. In turn, sexual strategies theory (Buss & Schmitt, 1993) predicts that men may experience more regret over romantic or sexual omission (missed opportunities), whereas women may experience more regret over romantic or sexual commission (regretting past decisions). We tested these predictions using craigslist's missed connections (personal ads posted by people on craigslist.org seeking to contact someone they saw briefly in public) and FMyLife.com's (FML) love and intimacy sections (embarrassing incidents that people experience and choose to share online anonymously). We recorded missed connections for men seeking women and women seeking men in all 50 U.S. states at 3 time points (N > 61,000). We also recorded FMLs posted by men and women over a 3-year span (N > 3,500). Consistent with EMT, parental investment theory, and sexual strategies theory, men were more likely to post missed connections (sexual or romantic omission regret), whereas women were more likely to post in FML's love and intimacy sections (sexual or romantic commission regret). We discuss EMT's broad theoretical implications for psychology.