In chapter 5 all aspects of decision analysis will be

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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 outcome. 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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