A Mathematically Guided Strategy For Risk Assessment And Management
Free (open access)
Strategies for risk assessment and management of high consequence operations are often based on factors such as physical analysis, analysis of software and other logical processing, and analysis of statistically determined human actions. Conventional analysis methods work well for processing objective information. However, in practical situations, much or most of the data available are subjective. Also, there are potential resultant pitfalls where conventional analysis might be unrealistic, such as improperly using event tree and fault tree failure descriptions where failures or events are soft (partial) rather than crisp (binary), neglecting or misinterpreting dependence (positive, negative, correlation), and aggregating nonlinear contributions linearly. There are also personnel issues that transcend basic human factors statistics. For example, sustained productivity and safety in critical operations can depend on the morale of involved personnel. In addition, motivation is significantly influenced by \“latent effects,” which are pre-occurring influences. This paper addresses these challenges and proposes techniques for subjective risk analysis, latent effects risk analysis and a hybrid analysis that also includes objective risk analysis. The goal is an improved strategy for risk management. Keywords: risk analysis, assessment and management, safety engineering, latent effects, hybrid analysis. 1 Introduction Risk assessment and risk management can be facilitated by appropriate quantitative analysis techniques. Unfortunately, \“conventional” probabilistic and statistical analyses usually depend on objective inputs, and these are generally scarce. Classical techniques help understand how to solve problems analogous
risk analysis, assessment and management, safety engineering, latent effects, hybrid analysis.