Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations
A problem of reducing interval uncertainty is considered by an approach of cutting off equal parts from the left and right. The interval contains admissible values of an observed object’s parameter. The object’s parameter cannot be measured directly or deductively computed, so it is estimated by exp...
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| Format: | Article |
| Language: | English |
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Sciendo
2020-06-01
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| Series: | Foundations of Computing and Decision Sciences |
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| Online Access: | https://doi.org/10.2478/fcds-2020-0008 |
| _version_ | 1798039740899917824 |
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| author | Romanuke Vadim |
| author_facet | Romanuke Vadim |
| author_sort | Romanuke Vadim |
| collection | DOAJ |
| description | A problem of reducing interval uncertainty is considered by an approach of cutting off equal parts from the left and right. The interval contains admissible values of an observed object’s parameter. The object’s parameter cannot be measured directly or deductively computed, so it is estimated by expert judgments. Terms of observations are short, and the object’s statistical data are poor. Thus an algorithm of flexibly reducing interval uncertainty is designed via adjusting the parameter by expert procedures and allowing to control cutting off. While the parameter is adjusted forward, the interval becomes progressively narrowed after every next expert procedure. The narrowing is performed via division-by-q dichotomization cutting off the q−1-th parts from the left and right. If the current parameter’s value falls outside of the interval, forward adjustment is canceled. Then backward adjustment is executed, where one of the endpoints is moved backwards. Adjustment is not executed when the current parameter’s value enclosed within the interval is simultaneously too close to both left and right endpoints. If the value is “trapped” like that for a definite number of times in succession, the early stop fires. |
| first_indexed | 2024-04-11T21:57:55Z |
| format | Article |
| id | doaj.art-c748c5b9525040a2902daa3e31759900 |
| institution | Directory Open Access Journal |
| issn | 2300-3405 |
| language | English |
| last_indexed | 2024-04-11T21:57:55Z |
| publishDate | 2020-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Foundations of Computing and Decision Sciences |
| spelling | doaj.art-c748c5b9525040a2902daa3e317599002022-12-22T04:01:02ZengSciendoFoundations of Computing and Decision Sciences2300-34052020-06-0145212515510.2478/fcds-2020-0008fcds-2020-0008Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observationsRomanuke Vadim0Polish Naval Academy, Gdynia, PolandA problem of reducing interval uncertainty is considered by an approach of cutting off equal parts from the left and right. The interval contains admissible values of an observed object’s parameter. The object’s parameter cannot be measured directly or deductively computed, so it is estimated by expert judgments. Terms of observations are short, and the object’s statistical data are poor. Thus an algorithm of flexibly reducing interval uncertainty is designed via adjusting the parameter by expert procedures and allowing to control cutting off. While the parameter is adjusted forward, the interval becomes progressively narrowed after every next expert procedure. The narrowing is performed via division-by-q dichotomization cutting off the q−1-th parts from the left and right. If the current parameter’s value falls outside of the interval, forward adjustment is canceled. Then backward adjustment is executed, where one of the endpoints is moved backwards. Adjustment is not executed when the current parameter’s value enclosed within the interval is simultaneously too close to both left and right endpoints. If the value is “trapped” like that for a definite number of times in succession, the early stop fires.https://doi.org/10.2478/fcds-2020-0008interval uncertainty reductiondichotomizationcutting off parts of an intervalexpert procedureexpert judgmentsparameter adjustmentstatistical |
| spellingShingle | Romanuke Vadim Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations Foundations of Computing and Decision Sciences interval uncertainty reduction dichotomization cutting off parts of an interval expert procedure expert judgments parameter adjustment statistical |
| title | Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations |
| title_full | Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations |
| title_fullStr | Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations |
| title_full_unstemmed | Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations |
| title_short | Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations |
| title_sort | division by q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short termed observations |
| topic | interval uncertainty reduction dichotomization cutting off parts of an interval expert procedure expert judgments parameter adjustment statistical |
| url | https://doi.org/10.2478/fcds-2020-0008 |
| work_keys_str_mv | AT romanukevadim divisionbyqdichotomizationforintervaluncertaintyreductionbycuttingoffequalpartsfromtheleftandrightbasedonexpertjudgmentsundershorttermedobservations |