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Title: Markov Modeling with Soft Aggregation for Safety and Decision Analysis

Abstract

The methodology in this report improves on some of the limitations of many conventional safety assessment and decision analysis methods. A top-down mathematical approach is developed for decomposing systems and for expressing imprecise individual metrics as possibilistic or fuzzy numbers. A ''Markov-like'' model is developed that facilitates combining (aggregating) inputs into overall metrics and decision aids, also portraying the inherent uncertainty. A major goal of Markov modeling is to help convey the top-down system perspective. One of the constituent methodologies allows metrics to be weighted according to significance of the attribute and aggregated nonlinearly as to contribution. This aggregation is performed using exponential combination of the metrics, since the accumulating effect of such factors responds less and less to additional factors. This is termed ''soft'' mathematical aggregation. Dependence among the contributing factors is accounted for by incorporating subjective metrics on ''overlap'' of the factors as well as by correspondingly reducing the overall contribution of these combinations to the overall aggregation. Decisions corresponding to the meaningfulness of the results are facilitated in several ways. First, the results are compared to a soft threshold provided by a sigmoid function. Second, information is provided on input ''Importance'' and ''Sensitivity,'' in order to knowmore » where to place emphasis on considering new controls that may be necessary. Third, trends in inputs and outputs are tracked in order to obtain significant information% including cyclic information for the decision process. A practical example from the air transportation industry is used to demonstrate application of the methodology. Illustrations are given for developing a structure (along with recommended inputs and weights) for air transportation oversight at three different levels, for developing and using cycle information, for developing Importance and Sensitivity measures for soil aggregation, for developing dependence methodology, for constructing early alert logic, for tracking trends, for relating the Markov model to other (e.g., Reason) models, for developing and demonstrating rudimentary laptop software, and for developing an input/output display methodology.« less

Authors:
Publication Date:
Research Org.:
Sandia National Labs., Albuquerque, NM (US); Sandia National Labs., Livermore, CA (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
13062
Report Number(s):
SAND99-2461
TRN: AH200135%%310
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 1 Sep 1999
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; AIR; METRICS; SAFETY; SENSITIVITY; SOILS

Citation Formats

COOPER,J. ARLIN. Markov Modeling with Soft Aggregation for Safety and Decision Analysis. United States: N. p., 1999. Web. doi:10.2172/13062.
COOPER,J. ARLIN. Markov Modeling with Soft Aggregation for Safety and Decision Analysis. United States. doi:10.2172/13062.
COOPER,J. ARLIN. Wed . "Markov Modeling with Soft Aggregation for Safety and Decision Analysis". United States. doi:10.2172/13062. https://www.osti.gov/servlets/purl/13062.
@article{osti_13062,
title = {Markov Modeling with Soft Aggregation for Safety and Decision Analysis},
author = {COOPER,J. ARLIN},
abstractNote = {The methodology in this report improves on some of the limitations of many conventional safety assessment and decision analysis methods. A top-down mathematical approach is developed for decomposing systems and for expressing imprecise individual metrics as possibilistic or fuzzy numbers. A ''Markov-like'' model is developed that facilitates combining (aggregating) inputs into overall metrics and decision aids, also portraying the inherent uncertainty. A major goal of Markov modeling is to help convey the top-down system perspective. One of the constituent methodologies allows metrics to be weighted according to significance of the attribute and aggregated nonlinearly as to contribution. This aggregation is performed using exponential combination of the metrics, since the accumulating effect of such factors responds less and less to additional factors. This is termed ''soft'' mathematical aggregation. Dependence among the contributing factors is accounted for by incorporating subjective metrics on ''overlap'' of the factors as well as by correspondingly reducing the overall contribution of these combinations to the overall aggregation. Decisions corresponding to the meaningfulness of the results are facilitated in several ways. First, the results are compared to a soft threshold provided by a sigmoid function. Second, information is provided on input ''Importance'' and ''Sensitivity,'' in order to know where to place emphasis on considering new controls that may be necessary. Third, trends in inputs and outputs are tracked in order to obtain significant information% including cyclic information for the decision process. A practical example from the air transportation industry is used to demonstrate application of the methodology. Illustrations are given for developing a structure (along with recommended inputs and weights) for air transportation oversight at three different levels, for developing and using cycle information, for developing Importance and Sensitivity measures for soil aggregation, for developing dependence methodology, for constructing early alert logic, for tracking trends, for relating the Markov model to other (e.g., Reason) models, for developing and demonstrating rudimentary laptop software, and for developing an input/output display methodology.},
doi = {10.2172/13062},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {9}
}