6.1 What is MCDA?
A form of MCA that has found many applications in both public and private
sector organisations is multi-criteria decision analysis, or MCDA for short (also
known as multi-attribute decision analysis, or MADA). This chapter explains
what MCDA is and then outlines what is required to carry out such an
analysis.
MCDA is both an approach and a set of techniques, with the goal of
providing an overall ordering of options, from the most preferred to the least
preferred option. The options may differ in the extent to which they achieve
several objectives, and no one option will be obviously best in achieving all
objectives. In addition, some conflict or trade-off is usually evident amongst
the objectives; options that are more beneficial are also usually more costly,
for example. Costs and benefits typically conflict, but so can short-term
benefits compared to long-term ones, and risks may be greater for the
otherwise more beneficial options.
MCDA is a way of looking at complex problems that are characterised by any
mixture of monetary and non-monetary objectives, of breaking the problem
into more manageable pieces to allow data and judgements to be brought
to bear on the pieces, and then of reassembling the pieces to present a
coherent overall picture to decision makers. The purpose is to serve as an
aid to thinking and decision making, but not to take the decision. As a set
of techniques, MCDA provides different ways of disaggregating a complex
problem, of measuring the extent to which options achieve objectives, of
weighting the objectives, and of reassembling the pieces. Fortunately, various
computer programs that are easy to use have been developed to assist the
technical aspects of MCDA, and these are set out in the Software review.
The first complete exposition of MCDA was given in 1976 by Keeney and
Raiffa,32 whose book is still useful today. They built on decision theory, which
for most people is associated with decision trees, modelling of uncertainty
and the expected utility rule. By extending decision theory to accommodate
multi-attributed consequences, Keeney and Raiffa provided a theoretically
sound integration of the uncertainty associated with future consequences
and the multiple objectives those consequences realise.
The main assumption embodied in decision theory is that decision makers
wish to be coherent in taking decisions. That is, decision makers would not
32 Keeney, R. L., & Raiffa, H. (1976) Decisions with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley, New York,
reprinted, Cambridge University Press, 1993.
Multi-criteria analysis: a manual | 47
deliberately set out to take decisions that contradict each other. No-one
would place several bets on the outcome of a single race such that no matter
which horse won they were certain to lose money. The theory expands on
this notion of coherence, or consistency of preference, and proposes some
simple principles of coherent preference, such as the principle of transitivity:
if A is preferred to B, and B to C, then A should be preferred to C, which
is a requirement if preference is to be expressed numerically. By treating
these rather obvious principles as axioms it is possible to prove non-obvious
theorems that are useful guides to decision making. A parallel can be
found in the study of geometry. Simple principles like ‘The shortest distance
between two points is a straight line’ are combined using the rules of logic
to prove theorems that are not obvious, like the Pythagorean principle, that
the square of the hypotenuse equals the sum of the squares of the other two
sides.
The first two theorems establish a logical equivalence between coherent
preference and number systems. If preferences are coherent, then two sorts
of measures follow logically: probability and utility, both associated with the
consequences of decisions. The first theorem establishes the existence of
probabilities: numbers which capture the likelihood that consequences will
occur. The second theorem shows the existence of utilities: numbers which
express the subjective value of the consequence and the decision maker’s risk
attitude.
The third theorem provides a guide to taking decisions: choose the course
of action associated with the greatest sum of probability-weighted utilities.
That is the expected utility rule, which has existed in various guises for over
200 years. To apply the expected utility rule, assess a probability and utility
for each possible consequence of a course of action, multiply those two
numbers together for each consequence, and add those products to give the
expected utility for that course of action. Repeat the process for each course
of action, and choose the action associated with the largest expected utility.
That description sounds rather dry and impractical, but decision theory gave
birth to the applied discipline of decision analysis.33 Thousands of decision
analyses have been successfully carried out since the 1960s in all aspects of
organisational life.
Keeney and Raiffa extended the set of axioms so that decisions with multiple
objectives could be analysed. In practice, MCDA is applied to help decision
makers develop coherent preferences. In other words, coherent preferences
are not assumed to start with, but the approach helps individuals and groups
to achieve reasonably coherent preferences within the frame of the problem
at hand. Once coherent preferences are established, decisions can be taken
with more confidence.
The years following the publication of Keeney and Raiffa’s book saw
increasing numbers of applications of MCDA in both private and public
sectors. Many of these are referenced in the bibliography to this manual. The
33 A term first coined by Professor Ronald Howard in 1966. The first complete exposition was given by Howard Raiffa in his
1968 book, Decision Analysis: Introductory Lectures on Uncertainty.
48 | Multi-criteria analysis: a manual
use of MCDA by various governmental agencies in the United States, at local,
state and federal level, is now widespread. The approach has also withstood
challenges of its results in courts of law and inquiries. The audit trail left by
a well-conducted MCDA suits the climate of freedom of information in the
United States, though it has not always been favoured by those who wished
to take very different decisions from those recommended by the analysis.
A notable example was the analysis of alternative sites for the disposal of
nuclear waste in the United States. Five potential sites were analysed using
MCDA, which resulted in an overall ranking of the sites. The US Department
of the Environment’s subsequent announcement of three sites for further
investigation included the sites ranked first, third and fifth by the MCDA
rather than the first three.34 Puzzled by this, Keeney35 conducted a new
analysis whose purpose was to find the best three combinations of sites
for further investigation, for it would not be cost effective to investigate
simultaneously any two very similar sites. Keeney’s analysis of this more
complex portfolio problem shows that a sequential characterisation strategy
would be more cost efficient, but still excluded the originally fifth-rated
site. Reaction to the DOE announcement was swift and drastic. Congress
and the House of Representatives initiated an investigation into the DOE’s
decision process and 46 lawsuits were filed against the DOE charging them
with violations of federal laws in their selection process. The investigation
supported the MCDA analysis, but concluded that the DOE’s decision process
was flawed.
Several lessons were drawn from this experience in a subsequent paper36
which looked at the whole sequence of events. For projects of major public
concern, ‘it is crucial to obtain inputs from a variety of professionals and
to have the implementation of the methodology monitored and routinely
reviewed by independent experts.’ The authors recommended including
objectives of key interest groups, making all value judgements explicit,
analysing as many crucial problem complexities as possible, obtaining
information from independent professionals, communicating all aspects
of the analysis to interested parties and individuals, and conducting an
independent review. While all these will not be appropriate for more modest
projects, it is notable that the recommendations focus on social issues.
MCDA is not simply a technical process. Its successful implementation
depends crucially on effective design of social processes by which the analysis
is structured and conducted.