<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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-<italic>q</italic> dichotomization cutting off the <italic>q</italic><sup>−1</sup>-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.</p></abstract>

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.

Division-by-<italic>q</italic> 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

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Division-by- 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...

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

Online veröffentlicht: 29. Juni 2020

Seitenbereich: 125 - 155

Eingereicht: 06. Feb. 2020

Akzeptiert: 10. Apr. 2020

DOI: https://doi.org/10.2478/fcds-2020-0008

Schlüsselwörter
interval uncertainty reduction, dichotomization, cutting off parts of an interval, expert procedure, expert judgments, parameter adjustment, statistical

© 2020 Vadim Romanuke, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 29. Juni 2020

Seitenbereich: 125 - 155

Eingereicht: 06. Feb. 2020

Akzeptiert: 10. Apr. 2020

DOI: https://doi.org/10.2478/fcds-2020-0008

Schlüsselwörterinterval uncertainty reduction, dichotomization, cutting off parts of an interval, expert procedure, expert judgments, parameter adjustment, statistical

© 2020 Vadim Romanuke, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
interval uncertainty reduction, dichotomization, cutting off parts of an interval, expert procedure, expert judgments, parameter adjustment, statistical