194
13 Tolerance and Acceptability
Risks which plot to the left and below the tolerance curve are deemed bearable.
Risks which plot to the right and above the curve are deemed unbearable and some
measures of mitigation are considered necessary to reduce their likelihood. Reducing
the likelihood of an impact may be, for example, as simple as imposing “no stop”
zones on a road.
When working empirically, two curves should be developed, one representing the
optimistic, the other the pessimistic view of tolerance. The area between the curves
represents a range of uncertainty on tolerance defined by an organization. When
data are available theoretical curves can be developed and then discussed with key
personnel.
Why is risk tolerance so important? One example related to a classic trend: overestimating outcome severity after one mishap. Let’s consider a system that causes,
on the average, one accident every one hundred years. Most of these accidents have
relatively small consequences, say one fatality for each. Once in a while there may
be a catastrophic event generating ten fatalities. If the catastrophic event happens to
occur, the public (or regulatory agencies) may believe that all accidents have catastrophic outcomes, thus they demand more safety measures than are justified by the
actual damage expectation. Such a claim is not restricted to the particular facility that
caused the accident; improvements are required for all other facilities of this type.
The development of empirical-estimated tolerance curves requires caution and
continuous calibration as the extent of correlation between an individual’s estimate or
ranking of probabilities and the true value/ranking is usually quite weak, sometimes
even in the order of zero (Gordon 1924; Peterson and Beach 1976). However, it
has been demonstrated that pooled judgments correspond better with the truth as
the number of individuals increases. For instance, the average correlation between
individual judgments and the correct rank order may increase twofold when pooled
across seven individuals, and twenty individuals may have an excellent ranking.
No wonder juries are made out of twelve people! Jokes aside, this is one of the
reasons why risk assessments, and in particular risk tolerance curves, should always
be defined by a group, and not by an individual (Hofstätter 1986; Wilde 2001).
13.2.3 Tolerance Versus Time and Tolerance Zones
Risk tolerance is obviously a function of an organization’s wealth. In the case of
some industries—extractive industries in particular—this translates into a function
of time. In the mining industry, for example, as ore reserves are depleted, tolerance
decreases because the company has less future wealth to buffer a hit (the operational
safety margin decreases with time) (Fig. 13.5).
Corporate wealth also has to do with the attitude a corporation may have in
defining risk tolerance. Figure 13.6 shows in orange a corporate-developed tolerance
13 Tolerance and Acceptability
Risks which plot to the left and below the tolerance curve are deemed bearable.
Risks which plot to the right and above the curve are deemed unbearable and some
measures of mitigation are considered necessary to reduce their likelihood. Reducing
the likelihood of an impact may be, for example, as simple as imposing “no stop”
zones on a road.
When working empirically, two curves should be developed, one representing the
optimistic, the other the pessimistic view of tolerance. The area between the curves
represents a range of uncertainty on tolerance defined by an organization. When
data are available theoretical curves can be developed and then discussed with key
personnel.
Why is risk tolerance so important? One example related to a classic trend: overestimating outcome severity after one mishap. Let’s consider a system that causes,
on the average, one accident every one hundred years. Most of these accidents have
relatively small consequences, say one fatality for each. Once in a while there may
be a catastrophic event generating ten fatalities. If the catastrophic event happens to
occur, the public (or regulatory agencies) may believe that all accidents have catastrophic outcomes, thus they demand more safety measures than are justified by the
actual damage expectation. Such a claim is not restricted to the particular facility that
caused the accident; improvements are required for all other facilities of this type.
The development of empirical-estimated tolerance curves requires caution and
continuous calibration as the extent of correlation between an individual’s estimate or
ranking of probabilities and the true value/ranking is usually quite weak, sometimes
even in the order of zero (Gordon 1924; Peterson and Beach 1976). However, it
has been demonstrated that pooled judgments correspond better with the truth as
the number of individuals increases. For instance, the average correlation between
individual judgments and the correct rank order may increase twofold when pooled
across seven individuals, and twenty individuals may have an excellent ranking.
No wonder juries are made out of twelve people! Jokes aside, this is one of the
reasons why risk assessments, and in particular risk tolerance curves, should always
be defined by a group, and not by an individual (Hofstätter 1986; Wilde 2001).
13.2.3 Tolerance Versus Time and Tolerance Zones
Risk tolerance is obviously a function of an organization’s wealth. In the case of
some industries—extractive industries in particular—this translates into a function
of time. In the mining industry, for example, as ore reserves are depleted, tolerance
decreases because the company has less future wealth to buffer a hit (the operational
safety margin decreases with time) (Fig. 13.5).
Corporate wealth also has to do with the attitude a corporation may have in
defining risk tolerance. Figure 13.6 shows in orange a corporate-developed tolerance