Related Experiment Video
Updated: Jun 25, 2025
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
12.1K
A new multi-attribute group decision-making method based on Einstein Bonferroni operators under interval-valued
Siyue Lei1, Xiuqin Ma1,2, Hongwu Qin3,4
1College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.
Scientific Reports
|May 29, 2024
Summary
This study introduces new methods for multi-attribute group decision-making (MAGDM) using interval-valued Fermatean hesitant fuzzy sets (IVFHFSs). The novel approach enhances data uncertainty handling and attribute relationship consideration for better decision outcomes.
Area of Science:
- Decision Sciences
- Fuzzy Set Theory
- Artificial Intelligence
Background:
- Interval-valued Fermatean hesitant fuzzy sets (IVFHFSs) offer advanced handling of uncertain decision-making information.
- Existing multi-attribute group decision-making (MAGDM) methods lack comprehensive operational laws and flexibility, and do not consider attribute interdependencies.
Purpose of the Study:
- To propose a novel MAGDM method utilizing interval-valued Fermatean hesitant fuzzy sets (IVFHFSs) and Einstein Bonferroni operators.
- To extend the understanding of operational laws for Einstein t-norms within IVFHFSs.
- To develop flexible aggregation operators that account for attribute relationships.
Main Methods:
- Thorough examination of operational laws for Einstein t-norms under IVFHFSs.
- Introduction of interval-valued Fermatean hesitant fuzzy Einstein Bonferroni mean and weighted Bonferroni mean operators.
- Development of a new MAGDM methodology incorporating these operators.
Main Results:
- Established comprehensive operational laws for Einstein t-norms within IVFHFSs.
- Introduced novel Einstein Bonferroni aggregation operators offering enhanced flexibility and attribute interdependency consideration.
- Demonstrated the proposed method's effectiveness through a cardiovascular disease diagnosis application.
Conclusions:
- The proposed MAGDM method effectively addresses limitations of existing approaches by incorporating advanced fuzzy set theory and aggregation techniques.
- The developed Einstein Bonferroni operators provide a more robust framework for decision-making under uncertainty.
- The application in cardiovascular disease diagnosis validates the practical utility and accuracy of the new method.
Keywords:
Bonferroni meanEinstein t-normsInterval-valued Fermatean hesitant fuzzy setsMulti-attribute group decision-makingMore Related Videos
07:05Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
Published on: September 10, 2018
6.0K
08:24The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
Published on: August 25, 2023
693
Related Concept Videos
01:09Decision Making: P-value Method
5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K
01:14Decision Making: Traditional Method
4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
01:21Friedman Two-way Analysis of Variance by Ranks
185
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
185
01:21Heuristics
84
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
84
01:20Decision Making
106
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Automatic decision-making is fast, intuitive, and relies on gut feelings...
106