Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of these systems has increased by facilitating access for community members through mobile phones and the Internet. One of the issues raised in crowdsourcing is how to choose people and how to collect answers. Usually, users are separated based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be selected to assess characteristics in individuals that are relevant to the main questions. One of the ways to increase the accuracy of crowdsourcing systems is by considering individuals\' cognitive characteristics and decision-making models to form a crowd and improve the estimation of their answer accuracy to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of a crowdsourcing system by understanding individuals\' metacognition and recording and utilizing users\' confidence in their answers?

This study investigated the influence of group size (individual, groups of five, and 20) on the response of common minnow to visual cues created by vertical black and white stripes over time. The stripes were displayed on a monitor either at one end of an experimental tank, while the other was uniform white, or both ends simultaneously. Reponses were compared with a control (stripes absent). Visual cues were pseudo-randomly presented every 15-minutes over six-hours. Three predictions were made: first, due to more efficient flow of information, larger groups would respond more rapidly (Rate of response) to the visual cues. Second, assuming visual cues provide a proxy for structure and larger groups experience greater benefits of group membership due to reduced predatory risk, there will be stronger association (Strength of association and Final association) with stripes for individuals and smaller groups compared with larger groups. Consequently, the association with visual cues exhibited by larger groups would diminish over time compared to smaller, more risk averse groups. As expected, larger groups exhibited a faster Rate of response to visual cues, and individual fish a greater Strength of association compared with the largest group size. Final association, however, was more common for larger groups compared to both smaller groups and individuals. Contrary to the final prediction, responses to visual cues did not decrease over time for any group size, suggesting innate behaviour or an experimental duration insufficient to observe habituation.

BACKGROUND: Systematic biases in group decision making (i.e., group biases) may result in suboptimal decisions and potentially harm patients. It is not well known how impaired group decision making in patient care may affect medical training. This study aimed to explore medical residents\' experiences and perspectives regarding impaired group decision making and the role of group biases in medical decision making.
METHODS: This study used a qualitative approach with thematic analysis underpinned by a social constructionist epistemology. Semi-structured interviews of medical residents were conducted at a single internal medicine residency program. Residents were initially asked about their experiences with suboptimal medical decision making as a group or team. Then, questions were targeted to several group biases (groupthink, social loafing, escalation of commitment). Interviews were transcribed and transferred to a qualitative data analysis software. Thematic analysis was conducted to generate major themes within the dataset.
RESULTS: Sixteen interviews with residents revealed five major themes: (1) hierarchical influence on group decision making; (2) group decision making under pressure; (3) post-call challenges in decision making; (4) interactions between teamwork and decision making; and (5) personal and cultural influences in group decision making. Subthemes were also identified for each major theme. Most residents were able to recognize groupthink in their past experiences working with medical teams. Residents perceived social loafing or escalation of commitment as less relevant for medical team decision making.
CONCLUSIONS: Our findings provide unique insights into the complexities of group decision making processes in teaching hospitals. Team hierarchy significantly influenced residents\' experiences with group decision making-most group decisions were attributed to consultants or senior team members, while lower ranking team members contributed less and perceived fewer opportunities to engage in group decisions. Other factors such as time constraints on decision making, perceived pressures from other staff members, and challenges associated with post-call days were identified as important barriers to optimal group decision making in patient care. Future studies may build upon these findings to enhance our understanding of medical team decision making and develop strategies to improve group decisions, ultimately leading to higher quality patient care and training.

BACKGROUND: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts\' subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology.
METHODS: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights.
CONCLUSIONS: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Sometimes we have a personal preference but we agree with others to follow a different course of action. In this study, 3- and 5-year-old children (N = 160) expressed a preference for playing a game one way and were then confronted with peers who expressed a different preference. The experimenter then either got the participants to agree with the peers explicitly or just shrugged her shoulders and moved on. The children were then left alone to play the game unobserved. Only the older children stuck to their agreement to play the game as the peers wished. These results suggest that by 5 years of age children\'s sense of commitment to agreements is strong enough to override their personal preferences.

Sintering is a commonly used agglomeration process to prepare iron ore fines for blast furnace. The quality of sinter significantly impacts the blast furnace ironmaking process. In the vast majority of sintering plants, the judgment of sintering quality still relies on the intuitive observation of the cross section at sintering machine tail by operators, which is susceptible to the external environment and the experience of operators. In this paper, we propose a new sintering state recognition method using deep learning based feature selection and ensemble learning. First, features from the infrared thermal images of sinter cross section at the tail of the sinterer are extracted based on ResNeXt. Then, to eliminate the irrelevant, redundant and noisy features, an efficient feature selection method based on binary state transition algorithm (BSTA) is proposed to find the truly useful features. Subsequently, an ensemble learning (EL) method based on group decision making (GDM) is proposed to recognize the sintering states. Novel combination strategies considering the varying performance of the base learners are designed to further improve recognition accuracy. Industrial experiments conducted at a steel plant verify the effectiveness and superiority of the proposed method.

The scholarship on flirting as a persuasive tactic in the workplace indicates that flirting can have negative consequences for task groups. The goal of this study was to extend the investigation of instrumental flirting by operationalizing this form of flirting as charm and by examining the consequences of charm in decision-making groups for the individual group members. In the current study, participants (60 women, 60 men) made decisions in four-person, mixed sex groups. The results of the study demonstrate that the use of charm was negatively associated with perceptions of group member task competence. Differences in perceptions of charm were also examined.

Leader selection plays a key role in how human social groups are formed and maintained. Leadership is either assigned through formal processes within an organization, or emerges informally through interactions with other group members-particularly in novel contexts. COVID-19 has accelerated the adoption of virtual meetings and more flexible team structures. However our understanding of how assigned leadership influences subsequent leadership emergence in virtual settings is limited. Here we examine the relationship between assigned leadership within an existing organization and subsequent emergent leadership attributions as members engage in virtual interactions. To do so, we created and implemented a novel virtual group decision-making task designed to support quantification of a more comprehensive set of communication style elements, such as speech dynamics and facial expressions, as well as task behaviors. Sixteen members of a real world organization engaged four repeated rounds of a group decision making task with new team members each time. We found participants made novel attributions of emergent leadership rather than relying solely on existing assigned leadership. While assigned leadership did influence leadership attributions, communication style, including amount of speech but also variability in facial expressions, played a larger role. The behavior of these novel emergent leaders was also more consistent with expectations of leadership behavior: they spoke earlier, more often, and focused more on the correct decision than did assigned leaders. These findings suggest that, even within existing social networks, virtual contexts promote flexible group structures that depend more on communication style and task performance than assigned leadership.

Rural sewage treatment in the Yangtze River basin is an important link to achieve the great protection of the Yangtze River, but the existing treatment technologies are difficult to choose and have various techniques and a lack of evaluation. Therefore, this study researched and collected the case information of rural sewage treatment projects in the middle reaches of the Yangtze River, analyzed the application of each treatment technology under different collection modes and discharge standards, constructed the evaluation index system of rural sewage treatment technologies, and then conducted a classification evaluation of processing technologies for each application scenario based on group decision making and the Analytic Hierarchy Process. The results showed that:the collection modes of rural sewage in the middle reaches of the Yangtze River were mainly single village or small-scale joint village, the treatment scale was mainly concentrated below 200 m3·d-1, the treatment technology was most applied by the anaerobic+ecological process (28.05%), and the application of integrated equipment was also more widely used (22.47%). The technical performance in the criterion layer had the largest weight (0.5039) in the evaluation index system, followed by those of economic benefits (0.2474), operation and management (0.1559), and environmental impact (0.0928), respectively; the four indicators of TP removal rate, ton water operation cost, ammonia nitrogen removal rate, and maintenance difficulty had higher weights. The evaluation and optimization results showed that the enhanced ecological and anaerobic+ecological treatment technologies were suitable for promotion and application in the rural areas of the middle reaches of the Yangtze River. The results of this study can provide scientific basis and reference for the selection of rural sewage treatment technologies in the middle reaches of the Yangtze River.

Group decision making under uncertainty often requires groups to balance exploration of their environment with exploitation of the seemingly best option. In order to succeed at this collective induction, groups need to merge the knowledge of all group members and combine goal-oriented and social motivations (i.e., group cohesion). This paper presents three studies that investigate whether more cohesive groups perform worse at collective induction tasks as they spend less time exploring possible options. Study 1 simulates group decision making with the ε-greedy algorithm in order to identify suitable manipulations of group cohesion and investigate how differing exploration lengths can affect outcomes of group decisions. Study 2 (N = 108, 18 groups á 6 participants) used an experimental manipulation of group cohesion in a simple card choice task to investigate how group cohesion might affect group decision making when only limited social information is available. Study 3 (N = 96, 16 groups á 6 participants) experimentally manipulated group cohesion and used the HoneyComb paradigm, a movement-based group experiment platform, to investigate which group processes would emerge during decision making and how these processes would affect the relationships between group cohesion, exploration length, and group decision making. Study 1 found that multiplicative cohesion rewards have detrimental effects on group decision making, while additive group rewards could ameliorate negative effects of the cohesion reward, especially when reported separately from task rewards. Additionally, exploration length was found to profoundly affect decision quality. Studies 2 and 3 showed that groups could identify the best reward option successfully, regardless of group cohesion manipulation. This effect is interpreted as a ceiling effect as the decision task was likely too easy to solve. Study 3 identified that spatial group cohesion on the playing field correlated with self-reported entitativity and leader-/followership emerged spontaneously in most groups and correlated with self-reported perceptions of leader-/followership in the game. We discuss advantages of simulation studies, possible adaptations to the ε-greedy algorithm, and methodological aspects of measuring behavioral group cohesion and leadership to inform empirical studies investigating group decision making under uncertainty.