Incomplete Decision Information Research Articles

An Improved Laplacian Gravity Centrality-Based Consensus Method for Social Network Group Decision-Making with Incomplete ELICIT Information

With the advancement of information technology, social media has become increasingly prevalent. The complex networks of social relationships among decision-makers (DMs) have given rise to the problem of social network group decision-making (SNGDM), which has garnered considerable attention in recent years. However, most existing consensus-reaching methods in SNGDM only consider local network information when determining the influence of DMs within the social network. This approach fails to adequately reflect the crucial role of key DMs in regulating information propagation during the consensus-reaching process. Additionally, the partial absence of linguistic evaluations in the decision-making problems also poses obstacles to identifying the optimal alternative. Therefore, this paper proposes an improved Laplacian gravity centrality-based consensus method that can effectively handle incomplete decision information in social network environments. First, the extended comparative linguistic expressions with symbolic translation (ELICIT) are utilized to describe DMs’ linguistic evaluations and construct the incomplete decision matrix. Second, the improved Laplacian gravity centrality (ILGC) is proposed to quantify the influence of DMs in the social network by considering local and global topological structures. Based on the ILGC measure, we develop a trust-driven consensus-reaching model to enhance group consensus, which can better simulate opinion interactions in real-world situations. Lastly, we apply the proposed method to a smart city evaluation problem. The results show that our method can more reasonably handle incomplete linguistic evaluations, more comprehensively capture the influence of DMs, and more effectively improve group consensus.

A two-stage decision model for sustainable-resilient supplier selection and order allocation under uncertain environment

PurposeThe paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.Design/methodology/approachFirst, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.FindingsThe paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.Originality/valueThe paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

A three-way decision approach with a probability dominance relation based on prospect theory for incomplete information systems

The processing scheme of typical incomplete information systems has been widely explored nowadays, however most of these approaches may change the original data information and can not guarantee the integrity of original information systems. Meanwhile, psychological behaviors of decision makers usually have an influence on the decision-making results. In light of these facts, the paper explores a new three-way decision approach by using prospect theory for an incomplete information system, which handles the default information of an incomplete information system by virtue of a probability dominance relation. First, a probability dominance relation of an incomplete information system is defined to process a binary relation between evaluation values, and the way for calculating the relative value function in the behavior decision-making process is also given. Moreover, the conditional probability with a decision attribute in an incomplete decision information system is put forward, and an objective weight function via prospect theory is further obtained. On this basis, a prospect-theory-based three-way decision approach with a probability dominance relation for an incomplete information system is proposed. Finally, the stability, effectiveness and superiority of the presented approach are validated via corresponding experimental studies and comparative analysis with two realistic cases.

Distributed Nash Equilibrium Seeking in a Multi-Coalition Noncooperative Game Under Incomplete Decision Information

This brief studies a distributed Nash equilibrium (NE) seeking strategy for a multi-coalition noncooperative game with local decision sets. In the game model, each coalition consists of some agents and its cost funciton is determined by the sum of the local cost functions subject to the agents in the coalition, where each coalition serves as a virtual player and the actual decision-makers are the agents in the coalition. The cost of each coalition is to minimize its own cost function under the case that the agents in each coalition only know their local information and can not directly access the opponent’s decision information in other coalitions. To this end, a distributed seeking strategy that can be viewed a two-time scale system is developed to search the NE of the formulated multi-coalition game. The fast time-scale system is used to estimate each coalition’s pseudo-gradient by a connected interference graph that illustrates the interactions among the agents in each coalition. The slow time-scale system based on a projected psedudo-gradient dynamics is implemented to seek the NE, where the coalitions estimate other coalitions’ decisions by interacting only with their neighbors via a weight-balanced digraph. For this distributed NE seeking strategy, asymptotic convergence result is given by Lyapunov stability analysis. Finally, the theoretical results is demonstrated via a numerical example.

A Dempster-Shafer-theory-based entry screening mechanism for small and medium-sized enterprises under uncertainty

The evaluation of star-up small and medium-sized enterprises (SMEs) is an important part of ensuring the success of development, but it is not an easy task because of the multiple criteria to be considered and uncertainties caused by incomplete decision information, limited individual perceptions and differences in perceptions of experts. By integrating Dempster-Shafer theory into a probabilistic linguistic setting that takes into account the uncertainties in the evaluation process by combining linguistic terms with subjective probabilities, this study proposes a multi-criteria decision-making model for the management of SMEs. We incorporate different decision factors regarding (a) the risk attitude of decision makers, (b) the relative importance of criteria, (c) the unreliability and incompleteness of evaluation on alternatives in one preference model of multi-criteria decision making. The applicability of the proposed preference model is validated by a case study of the entry screening of SMEs for business incubators. Through comparative analysis and sensitive analysis, the reliability of the proposed model is verified, which can not only express uncertain evaluation information, but also avoid information loss and defuzzification in the aggregation process.

Offshore wind power station (OWPS) site selection using a two-stage MCDM-based spherical fuzzy set approach

In response to challenges from the COVID-19 pandemic and climate change to achieve the goal of ensuring sustainable economic growth, offshore wind power development not only provides a clean and sustainable source of energy but also provides opportunities for economic growth and job creation. Offshore wind energy projects have been promptly suggested in Vietnam due to policy advancement, with the country's excellent wind resources. The success of an offshore wind energy project is decided mainly by choosing the best location for offshore wind power station (OWPS) construction, which is a complex multicriteria decision-making (MCDM) problem with the coexistence of conflicting factors. There is a problem with incomplete decision information use and information loss during the decision-making process, and it is easy to overlook the interaction difficulty in a fuzzy environment. To address the complex nature of the prioritization problem posed, this study proposes a hybrid MCDM framework combining the spherical fuzzy analytical hierarchy process (SF-AHP) and weighted aggregated sum product assessment (WASPAS). SF-AHP is used in the first stage to determine the significance levels of OWPS evaluation criteria. WASPAS is then utilized to rank locations of OWPS. A comprehensive set of evaluation criteria developed based on the concept of sustainable development has been recognized by reviewing the literature review and interviewing experts to practice the two-stage MCDM model. A real case study for Vietnam is conducted to test the effectiveness of the proposed method. The best location schemes have been determined by using the decision framework. The results of the sensitivity analysis and a comparison analysis demonstrate that the decision framework is practical and robust. The proposed methodology can be used to attain a decision-making process at the regional level for offshore wind farm planning and coastal development, and the study results encourage the establishment of renewable energy development policies.

Sustainable Management for the Architectural Heritage in Intelligent Cities using MCDM methods

The success of sustainable management of the heritage building in an intelligent city is a difficult multi-criteria decision-making (MCDM) issue including the coexistence of conflicting elements. There is an issue with incomplete decision information utilization and information loss throughout the decision-making process, and the interaction difficulty in a fuzzy environment is easy to miss. This paper provides a hybrid MCDM framework that combines the spherical fuzzy analytical hierarchy process (SF-AHP). The SF-AHP is used to assess the significance levels of building heritage. To use the stage MCDM model, a thorough set of assessment criteria based on the notion of sustainable development has been identified via literature research and expert interviews. To assess the efficacy of the suggested strategy, an application is done in this paper. Using the decision framework, the building heritage in intelligent cities has been identified. The suggested technique may be utilized to achieve management of the building heritage in intelligent cities.

Statistics-based approach for large-scale group decision-making under incomplete Pythagorean fuzzy information with risk attitude

As loss in decision sample information occurs during large-scale group decision-making (LSGDM), this paper proposes a statistical estimation approach for handling Pythagorean fuzzy information under the risk attitude of decision-makers (DMs). The DMs are partitioned by risk attitudes (hesitancy degrees) into subgroups. A five-number summary for the subgroups from the incomplete decision information given by the DMs is obtained. The Cornish–Fisher expansion is then applied to estimate the mean, standard variance, and skewness of the decision sample information from the five-number summary. The confidence interval constructed by the skewness is used to obtain the interval-valued Pythagorean fuzzy number (IVPFN) evaluation information of the subgroups. An optimization model based on minimizing the conflicts between the subgroups and the overall group is used to derive the weights of the subgroups. A sorting function of the IVPFNs is used to rank the alternatives. A case study on green credit and a comparison analysis are applied to validate the proposed method.

BW-MaxEnt: A Novel MCDM Method for Limited Knowledge

With the development of the social economy and an enlarged volume of information, the application of multiple-criteria decision making (MCDM) has become increasingly wide and deep. As a brilliant MCDM technique, the best–worst method (BWM) has attracted many scholars’ attention because it can determine the weights of criteria with less comparison time and higher consistency between judgments than analytic hierarchy process. However, the effectiveness of the BWM is based on complete comparison information among criteria. Considering the fact that the decision makers may have limited time and energy to study all criteria, they cannot construct a complete comparison system. In this paper, we propose a novel MCDM method named BW-MaxEnt that combines BWM and the maximum entropy method (MaxEnt) to identify the weights of unfamiliar criteria with incomplete decision information. The model can be translated into a convex optimization problem that can be solved effectively and has an overall optimal solution. Finally, a practical application concerning the procurement of GPU workstations illustrates the feasibility of the proposed BW-MaxEnt method.

Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report.

BackgroundMany older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level.ObjectiveThe aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation.MethodsThis longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity.ResultsOver time, the monitoring reports showed evolving trends in the care recipient’s daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation.ConclusionsOverall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse’s questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making.

Attribute Reduction of Coal Mine Fire Incidents Based on Finding Maximum Mutual Information

There are many redundant attributes for the original data of the emergency system with uncertainty and data loss, and the existing incomplete information system attribute reduction algorithms have the disadvantages of high time complexity and completeness of the reduction result, an attribute reduction method for incomplete emergencies based on finding the maximum mutual information is proposed. Firstly, the original data of the emergency was preprocessed, and the situation and the consequences were regarded respectively as conditional attributes and decision attributes, thereby an incomplete decision information table was constructed; then, according to the characteristic that the mutual information of the conditional attribute set and the decision attribute is equal to the information entropy of the decision attribute in the consistency incomplete information system, the attribute reduction algorithm based on finding the maximum mutual information was obtained; finally, this method was used to reduce the attribute of coal mine fire data and compared it with other three methods. The results indicate that this method can effectively reduce the redundant attributes of coal mine fire emergencies, and can ensure that the reduction results have strong completeness when the algorithm time is low, thus decision-making for the emergency department is provided.

Multi-Criteria Supplier Evaluation/ Selection in Fuzzy Environment With Incomplete Decision Information

Multi-Criteria Supplier Evaluation/ Selection in Fuzzy Environment With Incomplete Decision Information

A hybrid hierarchical fault diagnosis method under the condition of incomplete decision information system

Processing attribute reduction plays a key role in the fault diagnosis of incomplete decision information system (IDIS), and it improves the efficiency and accuracy of fault diagnosis. Tolerance relation-based attribute reduction is widely used in the IDIS. However, the fuzziness of relation-based classification always exists in the practical attribute reduction problems of fault diagnosis as the incompleteness and uncertainty of data information, and the traditional tolerance relation-based attribute reduction methods are not suitable for fault diagnosis of IDIS. Therefore, this paper proposes a hybrid hierarchical fault diagnosis method with the combination of tolerance relation-based attribute reduction method and integrated logarithmic fuzzy preference programming (LFPP) based methodology. The method utilizes both qualitative and quantitative data information and constructs the hierarchical structure of fault diagnosis in IDIS. The integrated LFPP based methodology obtains the unique normalized optimal significance priorities vector for attribute fuzzy pairwise comparison matrices simultaneously and directly as the sorting part of proposed method. The tolerance relation-based attribute reduction method decomposes the fault attributes reduction problem into multiple sub-problems, which is the decomposing part of proposed method. Hence, the proposed hybrid method can handle the fuzziness of relation-based classification and mitigate complexity attribute reduction for fault diagnosis of IDIS. Finally, an engineering case for strategy reduction of fault diagnosis is provided to demonstrate the feasibility of the proposed method and obtain the reduction diagnosis strategies. Another test case is given for verifying the validity of the reduction results and for comparison between the proposed method and other different methods, which shows that the method is indeed efficient and has greater advantages at producing higher accuracy, reducing difficulty and mitigating complexity in fault diagnosis.

The Effect of Activity-Based Costing (ABC) on Managing the Efficiency of Performance in Jordanian Manufacturing Corporations – An Analytical Study

The study explored the effect of activity-based costing (ABC) on the efficiency of performance in Jordanian manufacturing corporations. A questionnaire form was designed and distributed to a sample of 72 managers in 20 manufacturing corporations, and data were collected and analyzed using EXCEL and SPSS packages. The results found that the application of ABC has a significant effect on managing resources, performance efficiency, cost reduction, and costs of unused capacity in Jordanian manufacturing corporations. The conclusion showed that ABC were able to differentiate between high and low volume products among different activities of manufacturing corporation in Jordan. The research also concluded that the main difficulties were in differentiating between value added and non value added activities, unclear strategies, and incomplete information for decision making processes, thus, whatever activity-slack existed more levels of cooperation also existed between members of the value chain (VC). The study recommended more application of the ABC in different corporations as proved its effect on pricing decisions, cost reduction and a competitive ability of corporations in different markets.

Spectrum Optimization for Cognitive Satellite Communications With Cournot Game Model

With the availability of high-throughput satellite services at affordable cost, terrestrial network providers make use of satellite links to extend their coverage to areas, where land-based communication infrastructures are prohibitively costly to implement. Due to the ever-increasing demand for bandwidth resulted from the rapid development of data-intensive services in recent years, one of the fundamental challenges for satellite communications is to continuously improve utilization efficiency of the scarce satellite spectrum. Cognitive satellite communications address the problem by providing mechanisms for terrestrial and satellite users to dynamically access idle bands of licensed satellite networks, hence enabling spectrum sharing between two satellite systems or between satellite and terrestrial systems. In this paper, we investigate a distributed technique of spectrum sharing for cognitive satellite networks. In order to cater for situations with incomplete decision information, the proposed scheme is based on the Bayesian equilibrium theory. We first develop a spectrum allocation scheme by studying the action strategy of terrestrial cognitive terminals in a distributed competition. A feasible spectrum allocation scheme that caters for cases of incomplete user information is then developed as an extension of the basic scheme by formulating the problem as a Cournot game model. By identifying the unique equilibrium in this model, optimal spectrum allocation for cognitive satellite networks can be achieved. Essential discussions and proofs for the rationality of this method and uniqueness of the equilibrium are provided. Numerical results are given to justify the claimed advantages.

Applications of D-S Evidence Theory and Comprehensive Evaluation on Mining Information

Due to the incomplete decision information of mining projects, a new interval evaluation method basing on D-S evidence theory is applied to comprehensive evaluation of mineral resources. Index dates of evaluation system are assigned by the basic probability function . The probability value related to weights and membership grade for indexes .The Bays interval is estimated through evidence combination of dates and computed in decision making. Application to assessment of mining right transaction information are presented to demonstrate the effectiveness of the method.

Acquisition and Reduction of Incomplete Decision Information System Based on Similarity and Dissimilarity Knowledge Granules

Acquisition and Reduction of Incomplete Decision Information System Based on Similarity and Dissimilarity Knowledge Granules

Consistency Simulation and Optimization for HPIBM Model in Emergency Decision Making

Emergencies in ethnic regions in China happened increasingly, and emergency decision making become more complicated than that in other regions since it involves religions, cultures and national customs etc. The data consistency and incompleteness issues are two of the hot research topics in an emergency decision matrix (EDM). The Hadamard product induced bias matrix (HPIBM) model can be used to identify the most inconsistent entry in an EDM. In this paper, we provide experimental consistency simulations for HPIBM by randomly generating decision matrices with orders 3 to 7. In addition, we propose a novel HPIBM for estimating the missing judgments in an EDM by constructing optimization problem while improving the consistency ratio. A numerical example is used to illustrate the proposed method, and the result shows that the proposed method can find the optimal solutions for missing values and help emergency manager make fast response to the emergency incident even there are incomplete decision information.

Risks to Informed Consent in Pedagogic Research

Stierer & Antoniou (2004) have described Pedagogic Research (PR) as primarily teachers undertaking research into aspects of their own teaching and learning. Consequently, those undertaking PR often occupy dual roles of teacher and researcher. Likewise the subjects being studied are often the researcher’s own students and thus also occupying dual roles of student and participant. The purpose of this article is to highlight the potential risks to valid, informed consent inherent in the nature of pedagogic research itself; due to the dual roles mentioned above and the blurred boundaries between practice development and PR. Whilst inaccurate or incomplete information for decision making is an obvious risk to informed consent, the risks to voluntary participation can be more subtle. Reference is made to a documentary analysis of feedback provided to applicants by a research ethics committee reviewing pedagogic research. Whilst this is not a research report of that study, it provides empirical evidence to support the arguments made in this article. The article concludes that the greatest risk to valid informed consent is the lack of awareness among practitioner-researchers of the risks to voluntary participation this type of research holds. The author highlights the role for academic developers in highlighting these issues on professional development programmes. It is also recommended that a clear institutional position on when teacher/researchers need to apply for ethical approval could also be useful, particularly if flexibility is built in to allow for informal discussions with the Chair of the REC.

Rough Entropy Extensions for Feature Selection under Incomplete Decision Information Systems

Rough Entropy Extensions for Feature Selection under Incomplete Decision Information Systems