Characterizing uncertainty remains a critical challenge in group decision-making, which has prompted the development of diverse linguistic expressions with flexibility and applicability. However, they predominantly use adaptive linguistic terms coupled with numerical importance degrees, which inherently embed subjective cognitive biases. To address this gap, this study originally introduces hesitant fuzzy ordered linguistic term sets (HFOLTSs), which systematically address dual uncertainties: cognitive ambiguity quantified through multiple linguistic term sets, and importance indeterminacy mitigated by ordered relations. To enhance interpretability and visualization, this study proposes a graphical formalization of HFOLTSs, where hierarchical levels are embedded to reflect the structured nature of ordered relations. In addition, the fundamental operations associated with HFOLTSs, complement, expectation-based comparison, and distance measurement, are established to handle complex linguistic uncertainty. Two aggregation operators, namely, hesitant fuzzy ordered linguistic weighted averaging (HFOLWA) and hesitant fuzzy ordered linguistic ordered weighted averaging (HFOLOWA), are introduced to support group decision-making. To facilitate group consensus, we further devise a mechanism that harmoniously balances consensus efficiency with the willingness of experts. Finally, the proposed method is applied to a contractor selection problem in community renewal, which demonstrates its practicality and effectiveness in managing complex group decision scenarios.

Recently, the minimum cost consensus model (MCCM) has received considerable attention in group decision-making. However, the prevailing trend in early MCCMs is mainly centered on model-centric approaches, which would produce unit adjustment cost and individual options. With the widespread adoption of information technologies and the availability of extensive datasets, accompanied by the advancement of machine learning, constructing MCCMs based on data-driven and machine learning methods, such as neural network (NN), is more practical and reliable. Additionally, considering the inherent variability in models and data, granular computing (GrC) and GrC-based NN can effectively resolve uncertainty. Therefore, this article presents an MCCM based on GrC and online reviews. First, the granular NN (GNN) is trained with online reviews to obtain decision-makers’ weights and unit adjustment costs based on the user information. Subsequently, through assessing the sentiment polarity of decision-makers’ reviews, their opinions are elicited and dynamically adjusted in the form of intervals. Furthermore, the bilateral granular consensus is defined to characterize the impact of decision-makers’ preferences on consensus from both sides. Finally, group decision-making on movies and television productions on douban.com is conducted using the proposed method. Sensitivity analysis demonstrates the flexibility of our consensus approach, and the comparative analysis shows its effectiveness.

In large-scale group decision making (LSGDM), the consensus result is expected to be realized explicitly through reconciling various preferences provided by decision makers based on their personalized viewpoints. A information granule consensus-based decision brings about high flexibility and promising aspects in group decision making. The consensus reaching proposals reported so far paid little attention to the merits of Granular Computing for managing LSGDM problems. This paper concerns an extension of the well-known analytic hierarchy process to the LSGDM scenario using the optimizing information granule-based consensus reaching method. The consensus measurement is first quantified using coverage and specificity to derive the optimal cluster using the Fuzzy C-Means algorithm. Then, based on the optimization model of an information granule leading from numerical to interval representation, a novel construction model of information granule from interval representations to type-2 interval representation is developed, which yields the consistency of the obtained result instead of proceeding with an extra revision. To achieve the desired consensus, a preference modification algorithm is designed to detect the adjusted decision maker and further provide adjustment suggestions following the reference decision maker. Finally, a numeric study illustrates the effectiveness and flexibility of the proposed method.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.

Quantifying the uncertainty of information distributions containing randomness, imprecision, and fuzziness is the premise of processing them. A useful information representation in the field of intelligent computing are information granules, which optimize data from the perspective of specificity and coverage. We introduce information granularity into evidential information and model the basic probability assignment (BPA) as a weighted information granules model. Based on the proposed model, a new uncertainty measure of BPA is derived from the quality evaluation of granules. In addition, the proposed measure is extended to fuzzy evidential information distributions. When the Fuzzy BPA (FBPA) degenerates into the Probability Mass Function (ProbMF) and Possibility Mass Function (PossMF), the proposed method degenerates to Gini entropy and Yager's specificity measure, respectively. We use a refined belief structure to interpret the meaning of FBPA in the transfer belief model, and verify the validity of the proposed method by analyzing its properties and presenting numerical examples. The concept of information granule is used for the first time to model focal set and beliefs. Compared with Shannon entropy based information measures, the proposed method provides a novel perspective on the relationship between randomness, imprecision, and fuzziness in FBPA.

In this article, we deal with an improved fusion method for multigranular linguistic distribution assessments, a flexible and practical expression, in a multiple criteria group decision-making problem, which permits an accurate transformation between two linguistic labels without the loss of information. The importance degree of decision makers is allocated on the basis of individual consistency elicited from evaluation assessments represented by linguistic distributions, which is described with the aid of the proposed dominance degree of linguistic distribution. To intuitively reflect the group consensus level, a new measure, Jaccard consensus of linguistic distribution, is introduced. Based on this, a consensus-based multicriteria group preference analysis model with multigranular linguistic distribution information is established within a unified framework, which is developed by means of additive multiattribute value utility theory. Furthermore, a corresponding algorithm is matched to obtain a representative value function for decision analysis and an illustrative example is developed to illustrate the effectiveness of the proposed model.