Decision making under partial probability information using pair-wise comparisons

1999 ◽  
Vol 112 (1) ◽  
pp. 220-235 ◽  
Author(s):  
Celik Parkan ◽  
Lian-Fen Wang ◽  
Ming-Lu Wu
2021 ◽  
Vol 40 (1) ◽  
pp. 491-506
Author(s):  
Ao Shen ◽  
Shuling Peng ◽  
Gaofei Liu

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.


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

2019 ◽  
Author(s):  
David R. Mandel ◽  
Mandeep K. Dhami ◽  
Serena Tran ◽  
Daniel Irwin

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.


2021 ◽  
Author(s):  
Sylvie Rivot

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.


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