Unformatted text preview: erview of the approach, the
16 standard decision analysis can be summarised as a series of steps (Simpson et al.,
1999; Lamb et al., 1999; Newendorp, 1996; Goodwin and Wright, 1991; Morgan and
Henrion, 1990; French, 1989; Thomas and Samson, 1986):
1. Define possible outcomes that could occur for each of the available decision
choices, or alternatives.
2. Evaluate the profit or loss (or any other measure of value or worth) for each
3. Determine or estimate the probability of occurrence of each possible outcome.
4. Compute a weighted average profit (or measure of value) for each decision choice,
where weighting factors are the respective probabilities of occurrence of each
outcome. This weighted-average profit is called the expected value of the decision alternative, and is often the comparative criterion used to accept or reject
the alternative. Another measure that can be used to compare decision alternatives
is the expected preference/utility value of the decision alternative. This is a
decision criterion that attempts to take into account the decision-maker’s attitudes
and feelings about money using preference or utility functions. In either case, the
decision rule is to choose the decision alternative with highest expected
preference/utility value. This is the third and final stage of the R.Q.P. heuristic.
The new parts of this standard decision analysis approach are steps 3 and 4
(Newendorp, 1996). The analyst is required to associate specific probabilities to the
possible outcomes. Since this basic approach was proposed, the experience gained by
academics and consultants has stimulated changes designed to make the decision
analysis approach more flexible to the needs of managers (for example, Hammond et
al., 1999; Thomas and Samson, 1986; Keeney, 1979; Kaufman and Thomas, 1977).
Recently, as computing power has increased, the dimension of simulation has been
added to the standard decision analysis approach (Newendorp, 1996). Risk analysis
based on Monte Carlo simulation is a method by which the risk and uncertainty
encompassing the main projected variables in a decision problem are described using
probability distributions. Randomly sampling within the distributions many, perhaps
thousands, of times, it is possible to build up successive scenarios. The output of a
risk analysis is not a single value, but a probability distribution of all expected returns.
17 The prospective investor is then provided with a complete risk-return profile of the
project showing the possible outcomes that could result from the decision to stake
money on this investment (Newendorp, 1996).
More recently, preference, portfolio and option theories have been attracting some
attention in the decision theory literatures (for example, Bailey et al., in press;
Simpson et al., 2000; Simpson et al.¸ 1999; Galli et al., 1999; Hammond et al., 1999;
Smith and McCardle, 1997; Ross, 1997). Each of these techniques will be discussed
in Chapter 5. The plethora of techniques that a...
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