Downloadable - About University

Preparing for probability assessment 281
The wheel also has the advantage that it enables the decision maker
to visualize the chance of an event occurring. However, because it is
difficult to differentiate between the sizes of small sectors, the probability
wheel is not recommended for the assessment of events which
have either a very low or very high probability of occurrence (we
will deal with this issue later). The analyst should also ensure that
the rewards of the two bets are regarded as being equivalent by the
decision maker. For example, if in Bet One above, $100 000 will be
paid if the rival launches within the next year then this would imply
that the decision maker would have to wait a year before any winnings
could be paid. She would probably regard this as being less
attractive than a bet on the probability wheel where any winnings
would be paid instantly. It is also a good idea to use a large monetary
prize in the bets so that the preference between them is not influenced
by other attributes which may be imposed by the assessor. The
large payoff gives the monetary attribute a big weight compared to
the others.
A number of devices similar to the probability wheel have also been
used in probability assessment. For example, the decision maker may
be asked to imagine an urn filled with 1000 colored balls (400 red and
600 blue). He or she would then be asked to choose between betting on
the event in question occurring or betting on a red ball being drawn
from the urn (both bets would offer the same rewards). The relative
proportion of red and blue balls would then be varied until the decision
maker was indifferent between the two bets, at which point the required
probability could be inferred.
Assessment methods for probability distributions
The probability method
There is evidence 3 that, when assessing probability distributions, individuals
tend to be overconfident, so that they quote too narrow a range
within which they think the uncertain quantity will lie. Some assessment
methods fail to counteract this tendency. For example, if a decision
maker is asked initially for the median value of the distribution (this
is the value which has a 50% chance of being exceeded) then this can
act as an anchor. As we saw in Chapter 9, it is likely that he will make
insufficient adjustments from this anchor when assessing other values
in the distribution. For example, the value which has only a 10% chance
of being exceeded might be estimated to be closer to the median than