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Operationalizing Normal Accident Theory for Safety-Related Computer Systems

Public Domain


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  • Personal Author:
  • 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 the following 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, as well as other industries. [Description provided by NIOSH]
  • Subjects:
  • Keywords:
  • Series:
  • ISSN:
    0925-7535
  • Document Type:
  • Genre:
  • Place as Subject:
  • CIO:
  • Division:
  • Topic:
  • Location:
  • Volume:
    43
  • Issue:
    9
  • NIOSHTIC Number:
    nn:20028811
  • Citation:
    Saf Sci 2005 Nov; 43(9):697-714
  • Email:
    JSammarco@cdc.gov
  • Federal Fiscal Year:
    2006
  • Peer Reviewed:
    True
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:5e9cb0ce79a07b12542b0b532c707fbf0500daf9f442c39e4f079896f2ad608444375068f03b3a5a9a1b652bb139e722efed3c0a8be6203db2e09f5f17f752c8
  • Download URL:
  • File Type:
    Filetype[PDF - 406.44 KB ]
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