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The crypto and digital assets ecosystems have attracted investment, regulators, and speculators to their environment. As the blockchain-based framework can reduce transaction costs, generate distributed trust, and enable decentralized platforms, it has become a potential new base for decentralized business models. Previous studies have highlighted the advantages and drawbacks of each platform, such as interest rates, cost concerns, transparency issues, hacking issues, and hazards. Consequently, it is challenging for investors to evaluate the cryptocurrency trading system which determines the optimum exchanges and crucial aspects. Therefore, in order to rank the optimal digital token trading system, this paper develops an evaluation architecture to determine the various token trading systems. The developed architecture integrates fuzzy theory and the best-worst method (BWM) into the decision-making process to assess decision behaviors regarding preference for digital token trading systems in investors in Taiwan. First, this work establishes the views and parameters by modifying the Delphi method based on a literature review and survey. Second, the fuzzy-BWM is applied to obtain the fuzzy weights of the views and parameters. Then, defuzzification and BWM are used to rank the optimal alternatives of the digital token trading systems for investors. The results indicate that the optimal digital token trading system is the decentralized platform, and the critical parameters are gas fees, interest rates, and the mechanism of savings under fuzzy uncertain scenarios. This means that when considering the uncertain and ambiguous characteristics of the expert decision process in digital token trading systems, the evaluation is decentralized and the gas fees are the most important parameter in the digital token investment platform. Academically, the fuzzy BWM-based decision-making architecture can provide corporations and investors with valuable guidance to rank the optimal digital token trading systems based on fuzzy uncertain scenarios. Commercially, the proposed architecture could provide corporations and investors with a useful model to measure the optimal digital token trading system. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) methods are used in the selection and evaluation of alternatives. However, too many decision criteria and numerical calculations will increase the computational complexity and make the calculation process difficult to understand. In this paper, a weighted 2-tuple fuzzy linguistic representation model is proposed. The contributions of this study are as follows: (1) Feature selection method was used to remove the redundant or irrelevant feature attributes, thereby simplifying calculations and reducing calculation complexities. (2) The integration of the 2-tuple linguistic representation model simplifies the complexity of numerical calculations. The calculation of qualitative scales can be closer to the human thinking model, and loss of information can be avoided during calculations through the appropriate model. (3) Information fusion technology, i.e., ordered weighted average operator (OWA), was used. The method simplifies the traditional OWA calculation and can be calculated according to the priority order of the indicators. (4) Four major shareholding companies in Taiwan 50 ETF stocks were selected as experimental cases. In total, 992 tuples were obtained and 29 technical indicators were analyzed. The results indicate that case A1 is the most stable among the four stocks considered under different decision-making situations, and it has the first priority ranking. [ABSTRACT FROM AUTHOR]

This paper uses the grey fuzzy multiobjective programming to aid in decision making for the allocation of waste load in a river system under versatile uncertainties and risks. It differs from previous studies by considering a multicriteria objective function with combined grey and fuzzy messages under a cost benefit analysis framework. Such analysis technically integrates the prior information of water quality models, water quality standards, wastewater treatment costs, and potential benefits gained via in-stream water quality improvement. While fuzzy sets are characterized based on semantic and cognitive vagueness in decision making, grey numbers can delineate measurement errors in data collection. By employing three distinct set theoretic fuzzy operators, the synergy of grey and fuzzy implications may smoothly characterize the prescribed management complexity. With the aid of genetic algorithm in the solution procedure, the modeling outputs contribute to the development of an effective waste load allocation and reduction scheme for tributaries in this subwatershed located in the lower Tseng-Wen River Basin, South Taiwan. Research findings indicate that the inclusion of three fuzzy set theoretic operators in decision analysis may delineate different tradeoffs in decision making due to varying changes, transformations, and movements of waste load in association with land use pattern within the watershed. [ABSTRACT FROM AUTHOR]