If you wish to contribute or participate in the discussions about articles you are invited to join SKYbrary as a registered user

 Actions

Generic Error-Modelling System (GEMS)

From SKYbrary Wiki

Article Information
Category: Human Performance Modelling Human Performance Modelling
Content source: SKYbrary About SKYbrary
Content control: SKYbrary About SKYbrary

Description

The Generic Error-Modelling System (GEMS) integrates, within the same framework, the different error mechanisms (slips, lapses and mistakes) and the three levels of performance (Skill, Rule, Knowledge (SRK)). The integration of these two dimensions allows us to:

  1. Gain a deeper understanding the nature of mistakes: Indeed, we can distinguish between rule-based mistakes and knowledge-based mistakes
  2. Appreciate the details of the differences among error types
  3. Appreciate how errors can be considered the "other side of the coin" of those cognitive processes that allow us to act quickly or find creative solutions.
  4. Anticipate when, and in what conditions, a certain type of error may occur.


EURCONTROL describes GEMS as "an error classification scheme developed by [Dr. James] Reason that focuses on cognitive factors in human error as opposed to environmental or other context-related factors. GEMS is based heavily on Rasmussen’s three major categories of errors: skill-based slips and lapses, rule-based mistakes, and knowledge-based mistakes (SRK). GEMS is a more general description of the cognitive “black box”, which can be used to address the mechanisms of both slips and mistakes. GEMS taxonomy of error types is a useful method to assess cognitive determinants in complex technological environments."

GEMS and errors

Errors can occur at each level of performance:

  • Skill-based (SB): slips and lapses
    • usually errors of inattention or misplaced attention
  • Rule-based (RB): mistakes
    • usually a result of picking an inappropriate rule
    • caused by misconstrued view of state, over-zealous pattern matching, frequency gambling, deficient rules
  • Knowledge-based (KB): mistakes
    • due to incomplete/inaccurate understanding of system, confirmation bias, overconfidence, cognitive strain, ...

Errors can result from operating at wrong level:

  • humans are reluctant to move from a RB to KB level even if rules aren’t working

Related Articles

Further Reading

  • Developing a Human Error Modeling Architecture (HEMA) by Michael E. Fotta, Michael D. Byrne and Michael S. Luther, June 21, 2005. SBIR Phase I Contract: N00014-03-M-0357 for the Office of Naval Research, U.S. Navy.