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Operationalizing Normal Accident Theory for Safety-Related Computer Systems

Public Domain
File Language:
English


Details

  • Personal Author:
    Sammarco, John J.
  • Description:
    Computer-related accidents have caused injuries and fatalities in mining as well as other industries. Normal accident theory (NAT) explains that some accidents are inevitable because of system complexity. NAT is a classic argument in organizational sociology although it has been criticized as having imprecise definitions and lacking criteria for quantifying complexity. These limitations are addressed by a unique approach that recasts this organizational theory into an engineering-based methodology to quantify NAT complexities of computer-based systems. In this approach complexity is categorized as external or internal. External complexity is defined by the external behavior of a system, and is quantified by these dependent variables: system predictability, observability, and usability. Dependent variable data contain the perceptions of 32 subjects running simulations of a system. The system’s internal complexity is characterized by modeling system-level requirements with the software cost reduction (SCR) formal method. Model attributes are quantified using 15 graph-theoretical metrics—the independent variables. Five of 15 metrics are correlated with the dependent variables as evidenced by structure correlations exceeding 0.25, with standard errors <0.10 and a 95% confidence interval. The results also show that the system predictability, observability, and usability decreased as NAT complexities increased. This research takes a step forward in operationalizing NAT for computerized systems. The research benefits mining and other industries as well.
  • Subjects:
  • Document Type:
    Text
  • CIO:
    National Institute for Occupational Safety and Health (NIOSH)
  • NIOSHTIC Number:
    nn:20028811
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:78a8f783b4b0bdb00e47a6ad10a4ca37f3fa39cffc75c6895c5ce2636424f3fc77d2bbfdd7b9922182e011dba7d9bfd58b48627ed828fe55510fb0be7e096f7e
  • Download URL:
  • File Type:
    Filetype[PDF - 279.09 KB ]
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