An extended WASPAS approach for teaching quality evaluation based on pythagorean fuzzy reducible weighted Maclaurin symmetric mean

Teaching quality evaluation (TQE) can not only improve teachers’ teaching skills, but also provide an important reference for school teaching management departments to formulate teaching reform measures and strengthen teaching management. TQE is a process of grading and ranking a given teachers based on the comprehensive consideration of multiple evaluation criteria by expert. The Maclaurin symmetric mean (MSM), as a powerful aggregation function, can capture the correlation among multiple input data more efficient. Although multitude weighted MSM operators have been developed to handle the Pythagorean fuzzy decision issues, these above operators do not possess the idempotency and reducibility during the procedure of information fusion. To conquer these defects, we present the Pythagorean fuzzy reducible weighted MSM (PFRWMSM) operator and Pythagorean fuzzy reducible weighted geometric MSM (PFRWGMSM) operator to fuse Pythagorean fuzzy assessment information. Meanwhile, several worthwhile properties and especial cases of the developed operators are explored at length. Afterwards, we develop a novel Pythagorean fuzzy entropy based upon knowledge measure to ascertain the weights of attribute. Furthermore, an extended weighted aggregated sum product assessment (WASPAS) method is developed by combining the PFRWMSM operator, PFRWGMSM operator and entropy to settle the decision problems of unknown weight information. The efficiency of the proffered method is demonstrated by a teaching quality evaluation issue, as well as the discussion of sensitivity analysis for decision outcomes. Consequently, a comparative study of the presented method with the extant Pythagorean fuzzy approaches is conducted to display the superiority of the propounded approach.

Visualizing Risky Behaviors Induces a Stronger Neural Response in Brain Areas Responsible for Mental Imagery and Emotions Than Visualizing Neutral Behaviors

In the present study, we used a neuroimaging technique (fMRI) to test the prediction that visualizing risky behaviors induces a stronger neural response in brain areas responsible for emotions and mental imagery than visualizing neutral behaviors. We identified several brain regions that were activated when participants produced mental images of risky versus neutral behaviors and these regions overlap with brain areas engaged in visual mental imagery, speech imagery and movement imagery. We also found that producing mental images of risky behaviors, in contrast to neutral behaviors, increased neural activation in the insula – a region engaged in emotional processing. This finding is in line with previous results demonstrating that the insula is recruited by tasks involving induction of emotional recall/imagery. Finally, we observed an increased BOLD signal in the cingulate gyrus (mid-cingulate area), which is associated with reward-based decision making and monitoring of decision outcomes. In summary, we demonstrated that mental images of risky behaviors, compared to risk-free behaviors, increased neural activation in brain areas engaged in mental imagery processes, emotional processing and decision making. These findings imply that the evaluation of everyday risky situations may originate in visualizing the potential consequences of risk taking and may be driven by emotional responses that result from mental imagery.

Relationships among Antecedents, Processes, and Outcomes for Shared Decision Making: A Cross-Sectional Survey of Patients with Lumbar Degenerative Disease

Background Among musculoskeletal disorders, lumbar degenerative disease (LDD) is the leading cause of total disability-adjusted life years globally. Clinical guidelines for LDD describe multiple treatment options in which shared decision making becomes appropriate. Objectives To explore the relationships among measures of decision antecedents, process, and outcomes in patients with LDD. Methods Patients with LDD were recruited from outpatient clinics in a teaching hospital in Taiwan and administered surveys to collect measures of decision antecedents, processes, and outcomes. Multiple linear regression was conducted to assess the association between decision antecedents and the decision making process. Hierarchical linear regression was conducted to assess the relationships among decision antecedents, the decision making process, and decision outcomes. Results A total of 132 patients (mean age, 61 years) completed the survey. After adjustment for personal factors, 2 decision antecedents (namely, decision making self-efficacy and readiness) significantly predicted patients’ experiences of engaging in shared decision making (SDM). Decision making readiness and process were associated with fewer decisional conflicts and greater decision satisfaction. Limitations Models derived from cross-sectional surveys cannot establish causal relationships among decision antecedents, decision making processes, and decision outcomes. Conclusions Our results support the SDM framework, which proposes relationships among decision antecedents, the decision making process, and decision outcomes.

Psychological capital and strategic decision outcomes

PurposeA large body of research has analyzed individual psychological characteristics as antecedents of strategic decision-making. However, this research has mainly focused on trait-based characteristics that explain impaired strategic decision outcomes. Recently, PsyCap has been proposed as an alternative driver of strategic decision outcomes that, in contrast to other drivers, can be influenced by management.Design/methodology/approachDrawing on research on psychological capital (PsyCap), a psychological construct conceptualized as a state-like individual strength that is malleable, the authors argue that PsyCap exerts an inverted curvilinear effect on strategic decision outcomes. The authors use a computerized strategic decision simulation involving 102 managers to empirically test our hypotheses.FindingsThe authors show that PsyCap improves strategic decision outcomes up to an inflection point, after which it negatively affects those outcomes. The authors also show that this effect is mediated by heuristic information processing.Research limitations/implicationsFor the empirical study the authors relied on a sample of 102 practicing managers from the financial services industry in Germany.Practical implicationsPsyCap has been shown to be malleable through, for instance, micro-interventions and dedicated web-based trainings. Therefore, depending on managers' PsyCap levels, either further increases in PsyCap or a regulation of this characteristic might be appropriate in order to optimize strategic decision outcomes.Social implicationsAs a state-like individual strength that is malleable, PsyCap might serve as a management characteristic that is particularly important in challenging situations such as the COVID-19 pandemic.Originality/valueThis paper contributes to research on strategic decision making by introducing PsyCap as an important antecedent of strategic decision outcomes that – in contrast to other individual characteristics – is state-like and, hence, malleable.

Dynamical system model predicts when social learners impair collective performance

A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that, when the proportion of social learners exceeds a critical threshold, a bistable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics.

Chameleonic knowledge: a study of ex ante analysis in large infrastructure policy processes

Using ex ante analysis to predict policy outcomes is common practice in the world of infra- structure planning. However, accounts of its uses and merits vary widely. Advisory agencies and government think tanks advocate this practice to prevent cost overruns, short-term decision-making and suboptimal choices. Academic studies on knowledge use, on the other hand, are critical of how knowledge can be used in decision making. Research has found that analyses often have no impact at all on decision outcomes or are mainly conducted to provide decision makers with the confidence to decide rather than with objective facts. In this paper, we use an ethnographic research design to understand how it is possible that the use of ex ante analysis can be depicted in such contradictory ways. We suggest that the substantive content of ex ante analysis plays a limited role in understanding its depictions and uses. Instead, it is the process of conducting an ex ante analysis itself that unfolds in such a manner that the analysis can be interpreted and used in many different and seemingly contradictory ways. In policy processes, ex ante analysis is like a chameleon, figuratively changing its appearance based on its environment.

Under Careful Construction: Combining Findings, Arguments, and Values Into Robust Health Care Coverage Decisions

Background: Health care coverage decisions deal with health care technology provision or reimbursement on a national level. The coverage decision outcome, i.e., the publicly available document with reasons for the decision, may contain various elements: quantitative calculations like cost and clinical effectiveness analyses and formalised and non-formalised qualitative considerations. We know little about the process of combining these heterogeneous elements into robust decision outcomes.Methods: In this study, we describe a model for combining different elements into coverage decision outcomes. We build on two qualitative cases of coverage appraisals at the Dutch National Health Care Institute, for which we analysed observations at committee meetings (n=2, with field notes taken) and analysis of audio files (n=3), interviews with appraisal committee members (n=10 in seven interviews) and with Institute employees (n=5 in three interviews).Results: We conceptualise decision outcomes as combinations of elements, specifically (quantitative) findings and (qualitative) arguments and values. Our model contains three steps: 1) identifying elements; 2) designing the combinations of elements, which entails articulating links, broadening the scope of designed combinations, and black-boxing links; and 3) testing these combinations and choosing one as the final decision outcome. Conclusions: The proposed model highlights decision makers’ expertise in composing both elements and combinations. It also provides additional rationales for facilitating appeals and engaging patients and the public. Future research efforts could further explore the relationship between robustness and decision combination strength.