Artificial intelligence has pervaded clinical decision support systems driven by algorithms. In healthcare, algorithmic decision-making is said to outperform clinicians in their treatment choices and recommendations. Yet, relying on an algorithm outcome for clinical decisions comes with uncertainties resulting from the opacity, inconsistency and inaccuracy usurping the epistemic authority of clinicians and ultimately creating aversion. This study explores latent factors that constitute aversion to using algorithm-augmented mobile clinical decision support (mCDS) applications (apps) by clinicians at the point of care. Given that aversion encompasses a tendency to avoid potential risks, we modelled clinicians’ preferences using choice-based conjoint analysis with Hierarchical Bayes (HB) estimation methods to examine 362 clinicians’ aversion algorithm-augmented mCDS apps. In order of magnitude, the findings indicate that clinicians using mCDS apps are averse to algorithms undermining their epistemic authority with persistently inconsistent results at the point of care. In essence, clinicians will at the point-of-care resist algorithms that restrict them from modifying the result in line with their professional responsibilities. The study has several implications for both research and practice by helping improve potential drawbacks in clinical algorithms while promoting effective frameworks to enhance decision-making in medical practice.

The well-known Maclaurin symmetric mean (MSM) and the dual MSM (DMSM) are introduced as important operators to handle multiattribute group decision making (MAGDM) information. The MSM and the DMSM operators have the prominent characteristic of accurately describing the interdependence of multi-input arguments. Due to their advantage, we extend the MSM and the DMSM into the dual hesitant fuzzy environment to aggregate uncertain information. Particularly, we propose some novel aggregation operators, namely dual hesitant fuzzy MSM, weighted dual hesitant fuzzy MSM, dual hesitant fuzzy dual MSM, and weighted dual hesitant fuzzy dual MSM operators. Moreover, we study some properties and special remarks regarding different values of the parameter. With an extension of the complex proportional assessment method, we formulate a new approach for the dual hesitant fuzzy MAGDM. Finally, we test the applicability and feasibility of our proposed method by solving a mobile payment platform selection problem in Ghana.

As an extension of Pythagorean fuzzy sets, the q-rung orthopair fuzzy sets (q-ROFSs) can easily solve uncertain information in a broader perspective. Considering the fine property of q-ROFSs, we introduce q-ROFSs into decision-theoretic rough sets (DTRSs) and use it to portray the loss function. According to the Bayesian decision procedure, we further construct a basic model of q-rung orthopair fuzzy decision-theoretic rough sets (q-ROFDTRSs) under the q-rung orthopair fuzzy environment. At the same time, we design the corresponding method for the deduction of three-way decisions by utilizing projection-based distance measures and TOPSIS. Then, we extend q-ROFDTRSs to adapt the group decision-making (GDM) scenario. To fuse different experts’ evaluation results, we propose some new aggregation operators of q-ROFSs by utilizing power average (PA) and power geometric (PG) operators, that is, q-rung orthopair fuzzy power average, q-rung orthopair fuzzy power weighted average (q-ROFPWA), q-rung orthopair fuzzy power geometric, and q-rung orthopair fuzzy power weighted geometric (q-ROFPWG). In addition, with the aid of q-ROFPWA and q-ROFPWG, we investigate three-way decisions with q-ROFDTRSs under the GDM situation. Finally, we give the example of a rural e-commence GDM problem to illustrate the application of our proposed method and verify our results by conducting two comparative experiments.

In the real decision making, q-rung orthopair fuzzy sets ( q-ROFSs) as a novel effective tool can depict and handle uncertain information in a broader perspective. Considering the interrelationships among the criteria, this paper extends Choquet integral to the q-rung orthopair fuzzy environment and further investigates its application in multicriteria two-sided matching decision making. To determine the fuzzy measures used in Choquet integral, we first define a pair of q-rung orthopair fuzzy entropy and cross-entropy. Then, by utilizing λ-fuzzy measure theory, we propose an entropy-based method to calculate the fuzzy measures upon criteria. Furthermore, we discuss q-rung orthopair fuzzy Choquet integral operator and its properties. Thus, with the aid of q-rung orthopair fuzzy Choquet integral, we consider the preference heterogeneity of the matching subjects and further explore the corresponding generalized model and approach for the two-sided matching. Finally, a simulated example of loan market matching is given to illustrate the validity and applicability of our proposed approach.

Owing to the information insufficiency, it might be difficult for decision makers to precisely evaluate their assessments in real decision-making. As a new extension of the Pythagorean fuzzy sets, the interval-valued Pythagorean fuzzy sets (IVPFSs) can availably provide enough input space for decision makers to evaluate their assessments with interval numbers. By extending the Bonferroni mean to model the heterogeneous interrelationship among attributes, the extended Bonferroni mean (EBM) was examined. Considering the partition structure of relationship among the attributes, we introduce the EBM into the interval-valued Pythagorean fuzzy environment and develop two new aggregation operators, namely, interval-valued Pythagorean fuzzy extended Bonferroni mean and weighted interval-valued Pythagorean fuzzy extended Bonferroni mean (WIVPFEBM) operators. Meanwhile, some of their special cases and properties are also deeply discussed. Subsequently, by employing the WIVPFEBM operator, we propose an approach for multiple attribute decision making with IVPFSs. Finally, a practical illustration of the E-commerce project selection problem is investigated by our proposed method, which successfully demonstrates the applicability of our results.

The interval-valued Pythagorean fuzzy sets can easily handle uncertain information more flexibly in the process of decision making. Considering the interrelationship among the input arguments, we extend the Bonferroni mean and the geometric Bonferroni mean to the interval-valued Pythagorean fuzzy environment and solve its practical application problems. First, we develop the interval-valued Pythagorean fuzzy Bonferroni mean and the weighted interval-valued Pythagorean fuzzy Bonferroni mean (WIVPFBM) operators. The properties of these aggregation operators are investigated. Then, we also develop the interval-valued Pythagorean fuzzy geometric Bonferroni mean and the weighted interval-valued Pythagorean fuzzy geometric Bonferroni mean (WIVPFGBM) operators and analyze their properties. Third, we utilize the WIVPFBM and WIVPFGBM operators to fuse the information in the interval-valued Pythagorean fuzzy multicriteria group decision making (IVPFMCGDM) problem, which can obtain much more information in the process of group decision making. With the aid of the linear assignment method, we present its extension and further design a new algorithm for the application of IVPFMCGDM. Finally, an example is given to elaborate our proposed algorithm and validate its excellent performance.

In this paper, we study the well-known Bonferroni mean and develop its generalized aggregation operators in the Pythagorean fuzzy environment. More specifically, by considering the interrelationship between arguments with Pythagorean fuzzy information, we develop the Pythagorean fuzzy Bonferroni mean (PFBM) and some special properties and cases of them are also discussed. Furthermore, taking the multicriteria decision making environment into consideration, we extend the results of PFBM and develop the weighted Pythagorean fuzzy Bonferroni mean (WPFBM). Meanwhile, we also propose an approach for the application of WPFBM. However, during the application of the WPFBM operator, the calculation is very complex and time consuming. Hence, we introduce the multithreading into the application of the WPFBM operator and develop an accelerative calculating algorithm for it. To validate the performance of the accelerative calculating algorithm, we further design the corresponding experimental analysis.

As a new generalization of fuzzy sets, Pythagorean fuzzy sets (PFSs) can availably handle uncertain information more flexibly in the process of decision making. Through synthesizing the Bonferroni mean and the geometric mean, the geometric Bonferroni mean (GBM) captures the interrelationship of the input arguments. Considering the interrelationship among the input arguments, we introduce GBM into Pythagorean fuzzy situations and expand its applied fields. Under the Pythagorean fuzzy environment, we develop the Pythagorean fuzzy geometric Bonferroni mean and weighted Pythagorean fuzzy geometric Bonferroni mean (WPFGBM) operators describing the interrelationship between arguments and some special properties of them are also investigated. Then, we employ the WPFGBM operator to fuse the information in the Pythagorean fuzzy multicriteria group decision making (PFMCGDM) problem, which can obtain much more information in the process of group decision making. With the aid of the projection model, we present its extension and further design a new method for the application of PFMCGDM. Finally, an example is given to elaborate on the performance of our proposed method.