Name
Title | ||
|---|---|---|
Towards the most robust way of assigning numerical degrees to ordered labels, with possible applications to dark matter and dark energy |
Abstract | ||
|---|---|---|
Experts often describe their estimates by using words from natural language, i.e., in effect, sorted labels. To efficiently represent the corresponding expert knowledge in a computer-based system, we need to translate these labels into a computer-understandable language, i.e., into numbers. There are many ways to translate labels into numbers. In this paper, we propose to select a translation which is the most robust, i.e., which preserves the order between the corresponding numbers under the largest possible deviations from the original translation. The resulting formulas are in good accordance with the translation coming from the Laplace's principle of sufficient reason, and - somewhat surprisingly - with the current estimates of the proportion of dark matter and dark energy in our Universe. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/NAFIPS.2016.7851617 | 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) |
Keywords | Field | DocType |
sufficient reason,Laplace's principle,computer-understandable language,natural language,dark energy,dark matter,ordered labels,numerical degrees | Discrete mathematics,Laplace transform,Dark energy,Computer science,Algorithm,Theoretical computer science,Principle of sufficient reason,Natural language,Universe | Conference |
ISBN | Citations | PageRank |
978-1-5090-4493-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olga Kosheleva | 1 | 97 | 54.24 |
| Vladik Kreinovich | 2 | 1091 | 281.07 |
| Martha Osegueda Escobar | 3 | 0 | 0.68 |
| Kimberly Kato | 4 | 0 | 0.34 |