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SGER: Modeling Uncertainty in Engineering Design: An Investigation of Measure Theoretic Principles CharacterizingPreferences Under Imprecise Risk

SGER：工程设计中的不确定性建模：表征不精确风险下偏好的测度理论原理的研究

基本信息

批准号：
9726852
负责人：
Martin Wortman
金额：
$ 10万
依托单位：
Texas A&M Engineering Experiment Station
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
1997
资助国家：
美国
起止时间：
1997-09-01 至 2000-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9726852&HistoricalAwards=false
关键词：
SGER Modeling Uncertainty Engineering Design

项目摘要

The exploratory research addressed by this project focuses upon development of a theory of imprecise risk that is consistent with well-established engineering design mathematics, yet allows subjectively specified probability measure. Engineering design activity often requires the a priori subjective assignment of a probability measure to all uncertain design inputs. While subjective a priori assignment of probability measure eliminates imprecision in the characterization of risk, experience with such exercises shows that decision makers are not necessarily self-consistent and often do not satisfy the axioms of probability measure and/or utility theory when attempting to assign probabilities. The objectives of this research activity are: (1) to investigate imprecise risk associated with decision making in engineering design using measure theoretic principles; (2) to develop, from measure-based principles probability bounds on risk using subjectively assigned probability measure. Should this research effort prove successful, it will provide an analytical framework for quantifying imprecise risk arising from often inconsistent, subjectively assigned event probabilities. This will enable substantially improved decision making in virtually all engineering design.

该项目的探索性研究侧重于开发一种与成熟的工程设计数学相一致的不精确风险理论，但允许主观指定的概率度量。工程设计活动往往需要一个先验的主观分配的概率措施，所有不确定的设计输入。虽然主观的先验概率测度分配消除了风险特征描述的不精确性，但此类实践的经验表明，决策者在试图分配概率时不一定自洽，而且往往不满足概率测度和/或效用理论的公理。这项研究活动的目标是：（1）调查不精确的风险与决策在工程设计中使用测量理论的原则;（2）开发，从测量为基础的原则概率界的风险使用主观分配的概率措施。如果这项研究工作被证明是成功的，它将提供一个分析框架，用于量化因经常不一致、主观分配的事件概率而产生的不精确风险。这将大大改善几乎所有工程设计中的决策。