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The stock investment selection could be deemed as a classic multiple attribute group decision making (MAGDM) problem involving multiple conflicts and interleaved qualitative and quantitative attributes. Spherical fuzzy sets (SFSs) can excavate the potential vagueness and intricacy in MAGDM more effectively and deeply. This article we propose an integrated decision support system (IDSS) based on SFSs, prospect theory (PT), distance from average solution (EDAS) method and the MEthod based on the Removal Effects of Criteria (MEREC). The proposed IDSS, called SF-PT-EDAS-MEREC model, uses SFSs to describe the uncertain and obscure assessment information of DMs. The combination of PT and EDAS (PT-EDAS) method adequately captures DMs' psychological behavior characteristics to execute more reasonable alternative evaluation. The MEREC is utilized to efficaciously obtain unknown attribute weights. In addition, this paper also presents a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Eventually, in order to illustrate the practicability of the proposed IDSS, two numerical examples of stock investment selection are employed to achieve this. Meanwhile, the comparative study with existing approach further demonstrates the effectiveness and superiority of SF-PT-EDAS-MEREC model. [ABSTRACT FROM AUTHOR]

Probabilistic dual hesitant fuzzy set (PDHFS) is a more powerful and important tool to describe uncertain information regarded as generalization of hesitant fuzzy set (HFS) and dual HFS (DHFS), not only reflects the hesitant attitude of decision-makers (DMs), but also reflects the probability information of DMs. Score function of fuzzy number and weighting method are very important in multi-attribute group decision-making (MAGDM) issues. In many fuzzy environments, the score function and entropy measure have been proposed one after another. Firstly, based on the detailed analysis of the existed score function of PDHF element (PDHFE) and with the help of previous references, we build a novel score function for PDHFE. Secondly, a combined weighting method is built based on the minimum identification information principle by fusing PDHF entropy and Criteria Importance Through Intercriteria Correlation (CRITIC) method. Thirdly, a novel PDHF MAGDM approach (PDHF-EDAS) is built by extending evaluation based on distance from average solution (EDAS) approach to the PDHF environment to solve the issue that the decision attribute information is PDHFE. Finally, the practicability and effectiveness of the PDHF MAGDM technique is verified by suppliers selection (SS) and comparing analysis with existing methods. [ABSTRACT FROM AUTHOR]