An approach to probabilistic hesitant fuzzy risky multiattribute decision making with unknown probability information

2021 ◽  
Author(s):  
Xiaodi Liu ◽  
Zengwen Wang ◽  
Shitao Zhang ◽  
Harish Garg
Keyword(s):  
Decision Making ◽  
Unknown Probability ◽  
Probability Information

Transforming and decision-making based on probabilistic linguistic term sets with comparative linguistic expressions and incomplete assessments

2021 ◽  
Vol 40 (1) ◽  
pp. 491-506
Author(s):  
Ao Shen ◽  
Shuling Peng ◽  
Gaofei Liu
Keyword(s):  
Decision Making ◽  
Probability Distribution ◽  
Common Sense ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets ◽  
Probability Information ◽  
Incomplete Assessments

The probabilistic linguistic term sets (PLTSs) are widely used in decision-making, due to its convenience of evaluation, and allowances of probability information. However, there are still some cases where it is not convenient to give an evaluation using the PLTS gramma. Sometimes the evaluators can only give a comparative relationship between alternatives, sometimes evaluators may have difficulty understanding all the alternatives and cannot give a complete assessment. Therefore, we propose a method to transform the comparative linguistic expressions (CLEs) into PLTSs, and the comparison objects of CLEs are alternatives evaluated by PLTSs. And the probability distribution has been adjusted to make the transformation more in line with common sense. Then, a method to correct the deviation is proposed, allowing alternatives to be compared in the case of incomplete assessment. Combining the above two methods, we propose a decision-making method when both CLEs and incomplete assessments coexist. With the study in this paper, the limitations of PLTS-based evaluation and decision-making are reduced and the flexibility of using PLTS is improved.

Do karate fighters use situational probability information to improve decision-making performance during on-mat tasks?

2015 ◽  
Vol 34 (16) ◽  
pp. 1547-1556 ◽  
Author(s):  
Nicolas Milazzo ◽  
Damian Farrow ◽  
Alexis Ruffault ◽  
Jean F. Fournier
Keyword(s):  
Decision Making ◽  
Probability Information

scholarly journals Arithmetic computation with probability words and numbers

2019 ◽  
Author(s):  
David R. Mandel ◽  
Mandeep K. Dhami ◽  
Serena Tran ◽  
Daniel Irwin
Keyword(s):  
Decision Making ◽  
End Users ◽  
Strategy Use ◽  
Mental Computation ◽  
Point Estimates ◽  
Computational Advantage ◽  
Arithmetic Computation ◽  
Probability Information ◽  
Support Decision Making

Probability information is regularly communicated to experts who must fuse multiple estimates to support decision-making. Such information is often communicated verbally (e.g., “likely”) rather than with precise numeric (point) values (e.g., “.75”), yet people are not taught to perform arithmetic on verbal probabilities. We hypothesized that the accuracy and logical coherence of averaging and multiplying probabilities will be poorer when individuals receive probability information in verbal rather than numerical point format. In four experiments (N = 213, 201, 26, and 343, respectively), we manipulated probability communication format between-subjects. Participants averaged and multiplied sets of four probabilities. Across experiments, arithmetic accuracy and coherence was significantly better with point than with verbal probabilities. These findings generalized between expert (intelligence analysts) and non-expert samples and when controlling for calculator use. Experiment 4 revealed an important qualification: whereas accuracy and coherence were better among participants presented with point probabilities than with verbal probabilities, imprecise numeric probability ranges (e.g., “.70 to .80”) afforded no computational advantage over verbal probabilities. Experiment 4 also revealed that the advantage of the point over the verbal format is partially mediated by strategy use. Participants presented with point estimates are more likely to use mental computation than guesswork, and mental computation was found to be associated with better accuracy. Our findings suggest that where computation is important, probability information should be communicated to end users with precise numeric probabilities.

scholarly journals Using Fishburne’s sequences in suitable modeling used for sample data

2021 ◽  
Vol 15 (4) ◽  
pp. 50-60
Author(s):  
Anatoliy Sigal
Keyword(s):  
Decision Making ◽  
Probability Distribution ◽  
Decision Maker ◽  
Linear Order ◽  
Order Relation ◽  
Economic Environment ◽  
Statistical Estimates ◽  
The Third ◽  
Unknown Probability ◽  
Sample Data

This article deals with probabilistic and statistical modeling of managerial decision-making in the economy based on sample data for the previous periods of time. For better definition, the study is limited to Markowitz’s models in the problem of finding an effective portfolio of the field in the third information situation. The third information situation is a widespread decision-making situation and is characterized by the fact that the decision-maker sets, according to his opinion, are a linear order relation on the components of an unknown probabilistic distribution of the states of the economic environment. Often, from the point of view of the decision-maker, the components of an unknown probability distribution of the states of the economic environment must satisfy a partially reinforced linear order relation. As a result, the use of traditional statistical estimates turns out to be impossible, while the following question arises, which is practically not studied in the scientific literature. In this case, what formulas should be used to find statistical estimates and, above all, estimates of unknown probabilities of the state of the economic environment? As an estimate of an unknown probability distribution, we proposed to use the Fishburne sequence that satisfies all available constraints, while corresponding to the opinion of the decision maker and the linear order relation given by him. Fishburne sequences are a generalization of the well-known Fishburne formulas. It is fundamentally important that any Fishburne sequence satisfies a simple linear order relation, and under certain conditions, a partially strengthened linear order relation. Particular attention is paid to the entropic properties of generalized Fishburne progressions, which represent the most important class of Fishburne sequences, as well as the use of generalized Fishburne progressions to take into account the opinion of the decision maker. Such a scheme for estimating an unknown probability distribution has been developed, which makes it possible to achieve the correctness of probabilistic and statistical modeling, as well as appropriate consideration of the opinion of the decision-maker, uncertainty and risk.

scholarly journals Reading Keynes’s policy papers through the prism of his Treatise on Probability: information, expectations and revision of probabilities in economic policy

2021 ◽  
Author(s):  
Sylvie Rivot
Keyword(s):  
Decision Making ◽  
Probability Theory ◽  
Economic Policy ◽  
Financial Markets ◽  
The Self ◽  
The Other ◽  
Policy Recommendations ◽  
The Great Depression ◽  
The One ◽  
Probability Information

When scholars investigate the legacy of Keynes’s Treatise on Probability (1921) for the development of Keynes’s thinking, the attention usually focuses on the connections between Keynes’s probability theory, his conception of decision-making under uncertainty and the theory of the functioning of the macroeconomic system that derives from it - through the marginal efficiency of capital, the preference for liquidity and the self-referential functioning of financial markets. By contrast, the paper aims to investigate the connections between Keynes’s probability theory on the one hand, and his economic policy recommendations on the other. It concentrates on the policy recommendations defended by Keynes during the Great Depression but also after the General Theory. Keynes’s economic policy can be understood as a framework for decision-making in situations of uncertainty: fiscal policy aims to induce private agents to change their “rational” probability statements, while monetary policy aims to allow more weight to these statements.

Sign in / Sign up

Export Citation Format

Share Document