A multidimensional decision with nested probabilistic linguistic term sets and its application in corporate investment

Wang, Xinxin; Zhang, Xu; Wen, Qiang; Li, Honghui

doi:10.1080/1331677x.2021.1875255

Cited by 14 publications

(7 citation statements)

References 39 publications

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“…The researchers presented a case study of corporate investment, which is based on the proposed new VIKOR method with nested probabilistic linguistic information. Wang et al ( 2021 ) used the VIKOR method with the nested probabilistic linguistic term set (NPLTS) to study how firms improve their investment decisions.…”

Section: Artificial Intelligencementioning

confidence: 99%

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Černevičienė

Kabašinskas

2022

Front. Artif. Intell.

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The influence of Artificial Intelligence is growing, as is the need to make it as explainable as possible. Explainability is one of the main obstacles that AI faces today on the way to more practical implementation. In practise, companies need to use models that balance interpretability and accuracy to make more effective decisions, especially in the field of finance. The main advantages of the multi-criteria decision-making principle (MCDM) in financial decision-making are the ability to structure complex evaluation tasks that allow for well-founded financial decisions, the application of quantitative and qualitative criteria in the analysis process, the possibility of transparency of evaluation and the introduction of improved, universal and practical academic methods to the financial decision-making process. This article presents a review and classification of multi-criteria decision-making methods that help to achieve the goal of forthcoming research: to create artificial intelligence-based methods that are explainable, transparent, and interpretable for most investment decision-makers.

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Section: Artificial Intelligencementioning

confidence: 99%

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Černevičienė

Kabašinskas

2022

Front. Artif. Intell.

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“…Obviously, HFLTS cannot meet the needs of DMs. In contrast, multi-granularity probabilistic linguistic term set (MGPLTS) (Liu et al, 2021) provides linguistic terms with different granularity levels (similar to Likert scale of different levels) and corresponding probability values, which not only takes into account the advantages of probabilistic linguistic term set (Pang et al, 2016;Wang et al, 2021b), but also reflects the linguistic preference of DMs. For example, the evaluation information from DM e 1 is {l 1 5 (0.7), l 2 5 (0.3)}, and the evaluation information from DM e 2 is {l 2 7 = very good (0.5), l 3 7 = extremely good (0.5)}.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Medical Supplier Selection Based on Multi-Granularity Probabilistic Linguistic Term Sets

Лю

Wang

et al. 2022

Technological and Economic Development of Economy

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The sustainable medical supplier selection (SMSS) is an important issue facing the medical industry in the context of sustainable development, which can be regarded as a typical multi-attribute group decision making (MAGDM) problem. In the MAGDM process, linguistic term set (LTS) is particularly natural and convenient for decision makers (DMs) to express evaluation information. Especially, probabilistic linguistic term set (PLTS) is a very critical and effective tool, which can reflect the importance of different linguistic terms. Due to the different preferences and experience of different DMs, they may use multi-granularity probabilistic linguistic term sets (MGPLTSs) to represent different linguistic information. In this article, in order to study the comparison method of MGPLTSs, a new possibility degree formula is firstly proposed and its properties is proved. Then, in order to build a weight model, a possibility degree-based Best-Worst method (BWM) and a probability degree based-maximizing deviation method are established to calculate the subjective weights and objective weights of attributes, respectively. Where after, a MAGDM method is proposed by combining the ELimination Et Choix Traduisant la REalite (ELECTRE) method with Evaluation based on Distance from Average Solution (EDAS) method in the multi-granularity probabilistic linguistic information environment. Finally, the created MAGDM method is applied to the SMSS, and its effectiveness and advantages compared with other existing methods are verified.

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“…Li et al (2020) provided a multi-stage medical scheme selection process related to referral system under probabilistic linguistic environment. Wang et al (2021) developed a multi-dimensional methodology with nested PLTSs and applied it to corporate investment. Xue et al (2021) demonstrated a dynamic reference point method with probabilistic linguistic information based on the regret theory for public health emergency decision-making.…”

Section: Introductionmentioning

confidence: 99%

FUCOM-MARCOS based Group Decision-making using Dombi Power Aggregation of Dual Probabilistic Linguistic Information

Saha

Mishra²,

Rani

2021

Preprint

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The dual probabilistic linguistic (DPL) term sets are considered superior to probabilistic linguistic term sets. The power average operator can lessen the effects of the extreme assessing data from some decision-makers with prejudice. Further, the Dombi operators are quite flexible with the general parameter during the aggregation process. Moreover, based on deviation from the maximum consistency by the exclusion of the concern of the redundancy of the comparisons made in criteria pairs, FUCOM (full consistency method) is utilized as a subjective criteria weight computing model. Besides, MARCOS (Measurements alternatives and ranking according to compromise solution) method is based on the determination of utility degrees according to the distance from anti-ideal and ideal solutions and their aggregations. In this study, we combine the merits of power average operator, Dombi operator, FUCOM technique and MARCOS for dealing with multi-criteria group decision-making (MCGDM) problems under a DPL setting where rank of the alternatives are obtained through MARCOS method. For aggregating decision-experts preferences, we propose two types of operators, namely- DPL Dombi power weighted averaging and DPL Dombi power weighted geometric aggregation operators. We discuss the elegant properties of these proposed aggregation operators. We provide a case study regarding open source software learning management system selection to focus the practicability and usefulness of the proposed approach. Furthermore, we perform a sensitivity assessment on diverse criteria weight sets in order to test the stability of our developed intriguing approach. To this effect, we also provide a comparison between our approach with various extant methods.

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A multidimensional decision with nested probabilistic linguistic term sets and its application in corporate investment

Cited by 14 publications

References 39 publications

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Sustainable Medical Supplier Selection Based on Multi-Granularity Probabilistic Linguistic Term Sets

FUCOM-MARCOS based Group Decision-making using Dombi Power Aggregation of Dual Probabilistic Linguistic Information

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