The Markov Latent Effects Approach to Safety and Decision -Making
The methodology in this report addresses the safety effects of organizational and operational factors that can be measured through ''inspection.'' The investigation grew out of a preponderance of evidence that the safety ''culture'' (attitude of employees and management toward safety) was frequently one of the major root causes behind accidents or safety-relevant failures. The approach is called ''Markov latent effects'' analysis. Since safety also depends on a multitude of factors that are best measured through well known risk analysis methods (e.g., fault trees, event trees, FMECA, physical response modeling, etc.), the Markov latent effects approach supplements conventional safety assessment and decision analysis methods. A top-down mathematical approach is developed for decomposing systems, for determining the most appropriate items to be measured, and for expressing the measurements as imprecise subjective metrics through possibilistic or fuzzy numbers. A mathematical model is developed that facilitates combining (aggregating) inputs into overall metrics and decision aids, also portraying the inherent uncertainty. A major goal of the modeling is to help convey the top-down system perspective. Metrics are weighted according to significance of the attribute with respect to subsystems and are aggregated nonlinearly. Since the accumulating effect responds less and less to additional contribution, it is termed ''soft'' mathematical aggregation, which is analogous to how humans frequently make decisions. Dependence among the contributing factors is accounted for by incorporating subjective metrics on commonality and by reducing the overall contribution of these combinations to the overall aggregation. Decisions derived from the results are facilitated in several ways. First, information is provided on input ''Importance'' and ''Sensitivity'' (both Primary and Secondary) in order to know where to place emphasis on investigation of root causes and in considering new controls that may be necessary. Second, trends in inputs and outputs are tracked in order to obtain significant information, including cyclic information, for the decision process. Third, Early Alerts are provided in order to facilitate pre-emptive action. Fourth, the outputs are compared to soft thresholds provided by sigmoid functions. The methodology has been implemented in a software tool.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 787360
- Report Number(s):
- SAND2001-2229; TRN: AH200133%%388
- Resource Relation:
- Other Information: PBD: 1 Sep 2001
- Country of Publication:
- United States
- Language:
- English
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