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Unformatted text preview: not really a problem for mathematical statistics; they felt that there just
22 is no scientific way to make this final choice (Raiffa, 1968 p277). However, they
were in the minority.
In the early 1950s, there were many proposals suggesting how a decision-maker
should objectively choose a best strategy from the admissible class. No sooner did
someone suggest a guiding principle of choice, however, than someone else offered a
simple concrete example showing that this principle was counterintuitive in some
circumstances and therefore the proposed principle could not serve as the long sought
key (Raiffa, 1968). In 1954, Savage laid the foundations of modern Bayesian decision theory. In particular he showed that utilities and subjective probabilities
could model the preferences and beliefs of an idealised rational decision-maker facing
a choice between uncertain prospects. At least, they should do, if you accept Savage’s
axiomatic definition of rationality (French, 1984). Building on Savage’s work, decision analysis was developed in the 1960s by Howard Raiffa (Raiffa, 1968; Raiffa
and Schlaifer, 1961) and Ronald Howard (1968), and represents an evolution of
decision theory from an abstract mathematical discipline to a potentially useful
technology (foreword by Phillips in Goodwin and Wright, 1991).
Simplistically, decision analysis seeks to introduce intuitive judgements and feelings
directly into the formal analysis of a decision problem (Raiffa, 1968). Its purpose is
to help the decision-maker understand where the balance of their beliefs and
preferences lies and so guide them towards a better informed decision (French, 1989
p18). The decision analysis approach is distinctive because, for each decision, it
requires inputs such as executive judgement, experience and attitudes, along with the
“hard data”. The decision problem is then decomposed into a set of smaller problems.
After each smaller problem has been dealt with separately, decision analysis provides
a formal mechanism for integrating the results so that a course of action can be
provisionally selected (Goodwin and Wright, 1991 p3). This has been referred to as
the “divide and conquer” orientation of decision analysis (Raiffa, 1968).
Decompositional approaches to decision-making have been shown to be superior to
holistic methods in most of the available research (for example, Kleinmuntz et al.,
1996; Hora et al., 1993; MacGregor and Lichenstein, 1991; MacGregor et al., 1988;
Armstrong et al., 1975). Fischer (1977) argues that decompositional approaches
23 assist in the definition of the decision problem, allow the decision-maker to consider a
larger number of attributes than is possible holistically and encourage the use of
sensitivity analysis. Holistic evaluations, he believes, are made on a limited number
of attributes, contain considerable random error and, moreover, are extremely difficult
when there are fifty or more possible outcomes. Kleinmuntz (1990) shares this
perspective. He suggests that the consistency of holisti...
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This document was uploaded on 03/30/2014.
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