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  • Print publication year: 2018
  • Online publication date: February 2018

11 - Further Sources of Expert Failure

from PART IV - EXPERT FAILURE

Summary

NORMAL ACCIDENTS OF EXPERTISE

Turner (2010) points out:

Charles Perrow [1984] used the term ‘normal accidents’ to characterize a type of catastrophic failure that resulted when complex, tightly coupled production systems encountered a certain kind of anomalous event. These were events in which systems failures interacted with one another in a way that could not be anticipated, and could not be easily understood and corrected. Systems of the production of expert knowledge are increasingly becoming tightly coupled.

(p. 239)

Turner builds a theory of expert failure based on this idea of “normal accidents of expertise.” Others have applied Perrow's theory somewhat more narrowly to problems in forensic science (Cole 2005; Thompson 2008; Koppl and Cowan 2010). James Reason (1990) builds on Perrow.

Following Charles Perrow (1984), Reason (1990) notes that latent errors are more likely to exist and create harm in a complex, tightly coupled system than in a simple, loosely coupled system. Perrow borrowed the vocabulary of “tightly” and “loosely” coupled systems from mechanical engineering. In that context, Perrow explains, “tight coupling is a mechanical term meaning there is no slack or buffer between two items” (1984, pp. 89–90). In the context of social processes, a tightly coupled system is one in which failure in any one component or process may disrupt the function of others, thus generating an overall system failure.

Perrow lists four characteristic features of tightly coupled systems: (1) “Tightly coupled systems have more time-dependent processes: they cannot wait or stand by until attended to”; (2) “The sequences in tightly coupled systems are more invariant”; (3) “In tightly coupled systems, not only are the specific sequence invariant, but the overall design of the process allows only one way to reach the production goal”; (4) “Tightly coupled systems have little slack” (pp. 93–4). Perrow (1984, p. 79) calls a system “complex” when it has many “hidden interactions” whereby “jiggling unit D may well affect not only the next unit, E, but A and H also.” Systems for the production of expert knowledge, “expert systems” as Turner (2010) dubs them, can exhibit these qualities in varying degrees.

Forensic science today is a good example of a complex, tightly coupled “expert system.” For example, the Houston Crime Lab in past years invited cross-contamination of evidence from one type of testing to another.

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