Big Data-Theoretic Approach to Quantify Organizational Failure Mechanisms in Probabilistic Risk Assessment

TIME FRAME
2015 – present
TOTAL FUNDING TO DATE
$899,663

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 collaborate to provide answers to the following key questions: what social and organizational factors affect technical system risk; how and why do these factors influence risk; and how much do they contribute to risk? 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 lead 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.

Big Data-Theoretic Approach to Quantify Organizational Failure Mechanisms in Probabilistic Risk Assessment
Photo by Greg Webb / IAEA via Wikimedia Commons

Personnel

Big Data-Theoretic Approach to Quantify Organizational Failure Mechanisms in Probabilistic Risk Assessment
Zahra Mohaghegh
Principal Investigator (PI)
Big Data-Theoretic Approach to Quantify Organizational Failure Mechanisms in Probabilistic Risk Assessment
Catherine Blake
CO-Principal Investigator (CO-PI)


CO-PI Cheri Ostroff (University of South Australia)

Funding Agencies

National Science Foundation — 2015 — $899,663

Research Areas

Data Analytics, Informetrics