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The anchoring and adjustment heuristic 261
your car fails this week’ and ‘either bad weather or supplier problems
cause delays in the project’. When asked to estimate the probability of
a disjunctive event it appears that, once again, people anchor on one of
the elementary events. With disjunctive events this leads to a tendency
to underestimate the probability. 11 Since the estimation of risk often
involves probability assessments for disjunctive events, this bias can be
a serious concern.
To illustrate this, suppose that a chemical plant will leak dangerous
fumes if at least one of 10 independent subsystems fails. Each
subsystem is designed to have only a 1/100 chance of failure in the
course of a year. An estimate of the probability of a leakage occurring
in the next 12 months is required. Most people would be likely to
anchor on the 1/100 probability of an individual subsystem failing and
produce an estimate close to it. In fact, the correct probability is just
under 1/10.
Test your judgment: answer to question 13
Q13. Did you anchor on the 5% probability? The probability of at least
one person leaving is 40%.
3. Overconfidence
Suppose that you are a maintenance manager and you are asked to
provide optimistic and pessimistic estimates of how long the overhaul
of a machine will take. You are told that your estimates should be
such that there is a 99% chance that the actual time will fall between
the optimistic and pessimistic estimates. First you consider the most
likely time and estimate this to be 30 hours. You then adjust from
this to estimate the pessimistic time, 36 hours, and the optimistic time,
27 hours. This means you are 99% certain that the overhaul will take
between 27 and 36 hours. However, when the machine overhaul takes
place you are surprised to find that it takes 44 hours – way outside your
range. Were you unlucky or was there something wrong with your
estimation method?
Unfortunately, a number of research studies 12 suggest that ranges
estimated in this way are likely to be too narrow; that is, people tend
to be overconfident about the chances that their estimated range will
include the true value. Tversky and Kahneman argue that this is because
they start with an initial value, in this case the most likely overhaul