Cheri Ostroff - US grants

Nontechnical Description
Catastrophic events such as Fukushima and Katrina have made it clear that integrating physical and social causes of failure into a cohesive modeling framework is critical in order to prevent complex technological accidents and to maintain public safety and health. In this research, experts in Probabilistic Risk Assessment (PRA), Organizational Behavior and Information Science and Data Analytics disciplines will collaborate to provide answers to the following key questions: (a) what social and organizational factors affect technical system risk? (b) how and why do these factors influence risk? and (c) how much do they contribute to risk? Existing PRA models do not include a complete range of organizational factors. This research investigates organizational root causes of failure and models their paths of influence on technical system performance, resulting in more comprehensive incorporation of underlying organizational failure mechanisms into PRA. The field of PRA has progressed the quantification of equipment failure and human error for modeling risk of complex systems; however, the current organizational risk contributors lack reliable data analytics that go beyond safety climate and safety culture surveys. This research fills that gap by developing predictive causal modeling and big-data theoretic technologies for PRA, expanding the classic approach of data management for risk analysis by utilizing techniques such as text mining, data mining and data analytics. In addition to scientific contributions to organizational science, PRA, and data analytics, this research provides regulatory and industry decision-makers with important organizational factors that contribute to risk and leads to optimized decision making. Other applications include real-time monitoring of organizational safety indicators, efficient safety auditing, in-depth root cause analysis, and risk-informed emergency preparedness, planning and response. The multidisciplinary approach of this project can serve as an educational model, empowering students to pursue research across disciplinary boundaries. Finally, the proposed research represents a successful model of industry-academia collaboration. A nuclear power plant has committed to this project and provides unique access to data and information necessary to complete the research. The proposed methodology is generic and applicable for any high-risk industry (e.g., aviation, healthcare, oil and gas), and will be used for the improvement of organizational safety performance in order to protect workers, the public and the environment.

Technical Description
Organizations produce, process and store a large volume of wide-ranging, unstructured data as a result of business activities and compliance requirements (i.e., corrective action programs, root cause analysis reports, oversight and inspection data, etc.). This research leverages those data resources for the quantification of organizational failure mechanisms and their integration with the technical system risk scenarios generated by PRA. The research is based on a socio-technical risk theory to prevent misleading results from solely data-informed approaches. Combining socio-technical risk theory, systematic modeling and semantic data analytics strategies will greatly enhance risk analysis of complex systems. We will conduct our research based on following steps: (1) Expand factors, sub-factors, and causal relationships in the Socio-Technical Risk Analysis (SoTeRiA) framework, (2) Develop measurement techniques for factors, sub-factors and their causal relationships in SoTeRiA (e.g., integrating text mining with the Bayesian Belief Network; conducting scientific reduction to identify important factors; measuring of important factors), (3) Establish a dynamic, predictive socio-technical causal modeling technique, (4) Perform uncertainty analysis, (5) Conduct verification and validation, (6) Integrate the quantitative socio-technical causal model with PRA, and (7) Conduct sensitivity and importance measure analyses. As the pioneer study on the integration of big data with PRA, this research addresses and quantifies risk emerging from the interface of social and technical systems.