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Chapter 1
Multicriteria Decision Analysis in Group
Decision Processes
Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
Abstract Important decisions are often taken by groups of decision makers who
need to make choices among several alternatives, based on an appraisal of how
the alternatives are likely to perform with regard to multiple objectives. Such deci-
sion processes can be supported by the methods of multi-criteria decision analysis
(MCDA) which help generate decision recommendations and offer process bene-
fits in terms of enhanced decision quality, improved communication, and enhanced
commitment to decision implementation. In this Chapter, we outline widely used
MCDA methods and consider their uses in group decision making. We also review
selected case studies and offer guidelines for the design of MCDA-assisted group
decision processes. We conclude with thoughts on promising application domains
and future research topics.
1.1 Introduction
Group decision making is involved in the vast majority of consequential decisions
where there is a need to choose one which one out of many of alternative courses of
action should be pursued, in view of the multiple objectives that are seen as impor-
tant by the group members (see, e.g., [8, 23, 25, 27, 47]). And even if the decision is
ultimately taken by a single individual, the decision may affect several stakeholders
whose interests need to be recognized. In these situations, too, it may be instructive
to organize consultation processes where the stakeholders’ preferences are system-
atically charted, with the aim of informing the decision maker how the alternatives
are perceived by the stakeholders [28, 36].
Helsinki University of Technology, Systems Analysis Laboratory, P.O. Box 1100, FIN-02015
TKK, Finland e-mail: ahti.salo@tkk.fi, raimo@tkk.fi
1
2 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
The literature on multicriteria decision analysis (MCDA) offers numerous methods
which help decision makers address problems characterized by multiple objectives
(for textbooks and surveys, see, e.g., [8, 23, 25, 88]). Fundamentally, these objec-
tives represent the subjective values that are important in the decision making situ-
ation. The articulation of these values in terms of corresponding objectives can be
useful for many reasons: for instance, it fosters the identification, elaboration and
prioritization of alternatives that contribute to the realization of values [49]. For ex-
ample, the value of safety may suggest objectives such as reducing the number of
accidents,reducing the severity of injuries in accidents, or providing faster access
to first-aid services, which can be examined further to derive suggestions for al-
ternative courses of actions for the improvement of safety. Indeed, the systematic
specification concretization of objectives in terms of corresponding evaluation cri-
teria and attendant measurement scales offers an operational approach for assessing
how the alternatives contribute to the decision objectives and thus the realization of
values. MCDA methods thus offer systematic frameworks that help synthesize both
subjective and objective information, in order to generate well-founded guidance for
decision making.
From a theoretical perspective, many MCDA methods build on normative theories
of decision making that characterize what choices a decision maker would make
among alternatives, if his or her preferences comply with stated rationality axioms
(see, e.g., [47, 89]). Extensions of these theories into group settings have contributed
to the development of MCDA methods which are capable of admitting and synthe-
sizing information about the group members’ preferences and which can therefore
offer valuable insights into what alternatives are preferred to others by the partici-
pating individuals or the group as a whole. Such insights enable learning processes
which can be an important–if not the most important–motivation for MCDA-based
decision modeling (see, e.g., [29]).
In this Chapter, we consider decision settings where a group seeks to collaborate,
with the aim of identifying the most preferred one out of many alternatives, based on
an explicit articulation of decision objectives, corresponding evaluation criteria and
the appraisal of alternatives with regard to these criteria. The members of this group
can be either decision makers or representatives of stakeholders who are impacted
by the decision and have consequent interests in the decision outcome.
We assume that the number of decision alternatives is not too large so that all alter-
natives can be evaluated with regard to all the decision criteria. If this is not the case,
suitable screening approaches can be applied to reduce the set of initial alternatives.
The number of groups members involved in the decision support process can vary
considerably. For example, if web-based approaches are employed, even hundreds
of group members may be consulted (see, e.g., [33]). We also assume that there are
multiple criteria and that these are explicitly addressed. The many variants of vot-
ing procedures discussed in the literature on social choice are therefore beyond the
scope of this paper (see [1] for a seminal reference and the related Chapters in this
1 Multicriteria Decision Analysis in Group Decision Processes 3
volume). Nor do we cover multicriteria agency models [87] or bargaining models
where the group members (or agents) pursue different objectives [30, 60].
1.2 MCDA Methods
Although MCDA methods differ in their details (e.g., [8, 88, 27]), they are often
deployed by adopting rather similar decision support processes. At a high level of
aggregation, these processes often consist of partly overlapping and iterative phases:
1. Clarification of the decision context and the identification of group members: An
important initial phase is the scoping of the decision support process. Here, it is
necessary to clarify what the decision is really about, how the group members
are identified and engaged, and in what role they will participate in the process.
They can take part, for instance, as decision makers, sources of expertise, or
representatives of their respective stakeholder groups. Also, even if in high-level
decision making the actual decision makers may not be able to devote much time
to the process, it is often advantageous to include some decision makers in the
group, because this engages them into an intensive learning process, which is
likely to expedite the uptake and implementation of decision recommendations.
2. Explication of decision objectives: Starting from the values that are seen as im-
portant by the group members in the decision making situation, the relevant de-
cision objectives are elaborated and transformed into corresponding evaluation
criteria and associated measurement scales with the help of which the attainment
of these objectives can be assessed. This phase can be complemented through
in-depth interviews and questionnaires. It also often benefits from the guidance
that a skilled neutral facilitator can provide.
3. Generation of decision alternatives: A sufficiently representative and manage-
able set of alternatives is generated, possibly by applying suitable creativity tech-
niques [49, 85] when considering how the decision objectives could be achieved
through alternative courses of action. This phase is important, because the devel-
opment of eventual recommendations is strongly guided by the alternatives that
are included in the analysis at the outset. Thus, the process may be compromised
by ‘errors of omission’ if good alternatives are not included in the analysis.
4. Elicitation of preferences: The group members are engaged in an elicitation pro-
cess where subjective preference statements are solicited about (i) how impor-
tant the different evaluation criteria are relative to each other, and (ii) how much
value the group members associate with the alternatives’ performance levels on
criterion-specific measurement scales. Here, the different group members may
offer different responses, depending on their preferences.
4 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
5. Evaluation of decision alternatives: All alternatives are measured with regard to
every decision criterion using a related measurement scale. These evaluations can
be based, among other things, on the use of empirical data, subjective judgments
by external experts or by the group members themselves.
6. Synthesis and communication of decision recommendations: MCDA methods
are employed in order to derive decision recommendations by combining group
members’ preferences with the alternatives’ criterion-specific evaluations. A
careful examination of the resulting recommendations, in conjunction with the
learning process of MCDA analysis, may suggest a respecification of alterna-
tives or even objectives. In this case, it may be appropriate to repeat some of the
above phases.
At times, the third and fourth phases can be carried out in the reverse order so that
preference statements about the relative importance of attributes are elicited before
alternatives are generated. This notwithstanding, we believe that it is usually advis-
able to first develop an initial set of alternatives, because the process of generating
alternatives may give the group members an improved understanding of the decision
context. That is, the decision process may shape the group members’ preferences
which can be elicited more reliably after some alternatives are explicitly defined.
The fifth phase of evaluating alternatives often builds on information from many
sources. It may therefore be best carried out in a decentralized mode where the
participants are invited to evaluate alternatives with regard to those criteria they are
knowledgeable about. In large scale decision support processes that involve many
stakeholder groups, analogous phases of preference elicitation can also be supported
with the help of Internet-based decision support tools [33].
The close involvement of group members will be particularly crucial in the first and
last phases where the focus is on problem structuring, elaboration of objectives and
the development of decision recommendations. Here, an external facilitator often
has an important role in ensuring that the group members’ preferences are properly
charted and that each group member has a chance of voicing his or her concerns.
A facilitator also has a critical role in ensuring that (i) methodologies are employed
correctly, taking into account the pitfalls of human decision biases [75, 35], (ii) the
group members are aware of the assumptions of the decision model, and (iii) the
results of the decision model are fully understood in relation to the inputs. The de-
lineation of the above phases in MCDA-assisted decision support processes does
not emphasize the broader impacts of these processes, such as the collective learn-
ing that takes place as the group members’ perspectives evolve. For example, the
examination of tentative results may lead to the recognition of further objectives, or
suggest alternatives which were not initially considered. As a result, it may be perti-
nent to adopt iterative processes which provide possibilities for revisiting the earlier
phases. Especially in new decision contexts–where it may be difficult to recognize
1 Multicriteria Decision Analysis in Group Decision Processes 5
all the relevant criteria or alternatives at the outset–it may be useful to generate
tentative initial results for learning purposes before proceeding to the later rounds.
We next illustrate approaches to preference elicitation and synthesis by presenting
the main features of probably the two most widely used MCDA methodologies.
Here, we note that there exist many other MCDA approaches as well, such as those
based on goal programming [22] and outranking relations [76].
1.2.1 Multiattribute Value and Utility Theory
Multiattribute Value Theory (MAVT) is a methodological framework which of-
fers prescriptive decision recommendations for making choices among alternatives
x= (x1,...,xn)which have consequences xiwith regard to nattributes [8, 25, 48].
MAVT is based on a set of axioms that characterize rational decision making. For
example, it is postulated that a rational decision maker has complete preferences,
meaning that for any two multi-attribute alternatives xand y, the decision maker
either finds that these alternatives are equally preferred, or that one is preferred over
the other. Moreover, the preferences are assumed to be transitive, meaning that if
the decision maker prefers alternative xover yand alternative yover z, then xis
logically preferred over z.
Mutual preferential independence is a key axiom in MAVT [48]. Specifically, this
axiom holds if the decision maker’s preferences for alternatives which have different
consequences on some attributes and similar consequences on some other attributes
do not change if the alternatives’ similar consequences are changed. If this axiom
holds along with other, less restrictive axioms, there exists an additive multi-attribute
value function, defined on the alternatives’ consequences, such that alternative xis
preferred to yif and only if
xºy⇐⇒ V(x) = ∑
i
vi(xi)≥∑
i
vi(yi) = V(y)(1.1)
The existence of the value function has been proved using a topological approach
[16] and an algebraic approach [55]. The value function is unique up to positive
affine transformations. Thus, the preference relation that it induces on the alterna-
tives does not change if the values are multiplied by a positive constant
α
>0 or if
a constant
β
is added to the overall values of all alternatives. Due to this property,
the MAVT function in (1.1) can be written in the customary form
V(x) = ∑wivi(xi),(1.2)
where the scores vi(·)are typically normalized onto the [0,1] range so that the score
of the least preferred alternatives on a given attribute is zero while that of the most
6 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
preferred alternative is one. Furthermore, the widenote the attribute weights, which
reflect the decision maker’s preferences for the improvements obtained by changing
consequences from the least preferred attribute level to the most preferred attribute
level. These weights are customarily normalized so that they add up to one, i.e.,
∑iwi=1.
Keeney [48] extends the MAVT framework into group decision making settings
where the groups’ aggregate value depends on the values that are attained by the
individual group members. Specifically, he shows that if the the requisite axioms
hold, the group’s aggregate value function can be expressed as
V(x) = ∑
k
Wk∑wkivki(xi),(1.3)
where Wkdenotes the importance weight of the k-th decision maker and the latter
sum represents the value that alternative xwill give to her.
When using the MAVT framework in group decision support, the parameters of the
representation (1.1) or (1.3) are first estimated whereafter the alternatives’ overall
values are used for deriving decision recommendations. However, it is pertinent to
check that the underpinning axioms do hold and to elicit score and weight parame-
ters carefully, with the aim of mitigating the possibility of biases.
A major advantage of the MAVT framework is that it has a solid and testable ax-
iomatic foundation. In addition, the numerical representation is relatively simple so
that MAVT models are quite transparent, which makes it easier to understand how
the decision recommendations depend on the estimated parameters.
1.2.2 The Analytic Hierarchy Process
In the Analytic Hierarchy Process (AHP) [19, 77, 78], the decision problem is struc-
tured as a hierarchy where the topmost element represents the overall decision ob-
jective. This element is decomposed into sub-objectives which are placed on the next
higher level and which are decomposed further into their respective sub-objectives
until the resulting hierarchy provides a sufficiently comprehensive representation of
the relevant objectives. The decision alternatives are presented at the lowest level of
the hierarchy.
The elicitation of preferences is based on the use of a ratio scale. Specifically, for ev-
ery objective on the higher levels of the hierarchy, the DM is requested to compare
the relative importance of its sub-objectives through a series of pairwise compar-
isons. In each such comparison, the DM is asked to state how much more important
1 Multicriteria Decision Analysis in Group Decision Processes 7
one sub-objective is than another (e.g., ‘Which is the more important objective, cri-
terion, cost or quality?’) and to indicate the answer on a 1-to-9 verbal ratio scale
(1=equally important, 3=somewhat more important, 5=strongly more important, 7=
very strongly more important, 9=extremely more important). For the lowest level
objectives, the DM is asked to carry out similar comparisons about which decision
alternatives contribute most to the attainment of these objectives.
In the AHP, the derivation of the priorities is based on the following eigenvector
computations. First, the ratio statements are placed into a pairwise comparisons ma-
trix Asuch that the element Ai j denotes the strength of preference for the i-th sub-
objective over the j-th one. From this matrix, a local priority vector wis derived as a
normalized solution to the equation Aw =
λ
wmaxwwhere
λ
wmax is the largest eigen-
value of the matrix A. Second, using these local priorities, aggregate weights for the
objectives are derived by first assigning a unit weight to the topmost objective. This
weight then ‘flows’ downward in the hierarchy so that the weight of an objective is
obtained by multiplying the weight of the objective immediately above it with he
local priority vector component that corresponds to the lower level objective (taking
the sum of such products if the lower level objective is placed under several higher
level objectives). The weight of an alternative is obtained by summing all these
products over those objectives that have not been decomposed into subobjectives.
Despite its popularity, the AHP has been subjected to major criticisms. In partic-
ular, the AHP may exhibit so-called rank reversals [6] whereby the introduction
of an additional alternative may change recommendations concerning the other al-
ternatives. This possibility–which is caused by the normalization of local priority
vectors–violates the rationality axioms of MAVT and it is one of the reasons why
some scholars have contested the merits of the AHP as a sound decision support
methodology [18]. Other caveats in the AHP include the insensitivity of the 1-to-9
ratio scale and the large number of pairwise comparisons that may be needed when
the number of decision alternatives is large [81]. Yet, it can be shown that the pair-
wise comparisons are reformulated so that they pertain to value differences, then the
results of the AHP analysis can be expected to coincide with those of MAVT [81].
1.2.3 Methodological Extensions
The above descriptions summarize the ‘basic’ features of commonly employed
MCDA methods. These methods and yet other methods have been extended in nu-
merous ways:
•Recognition of partial or inconclusive evidence. Most MCDA methods assume
that complete information about the model parameters can be elicited in terms
8 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
of exact point estimates. Yet, such estimates can be excessively difficult or pro-
hibitively expensive to acquire. This recognition has spurred the development of
methods which represent incomplete information through set inclusion or, more
specifically, through sets of parameters that contain the ‘true’ parameters (see,
e.g., [51, 52, 80, 82]). This modeling approach can be particularly useful in group
decision making, because the sets can be defined so that they contain the param-
eters that correspond to the group members’ individual preferences [39, 40, 79].
Even if the resulting decision model may not provide conclusive recommenda-
tions for choosing the group’s preferred alternative, it may still help determine
which alternatives do not merit further attention so that the later phases of the
analysis can be focused on other alternatives. A further advantage of set inclusion
is its relative simplicity in comparison with methods that are based on evidential
reasoning [90] or fuzzy sets [41].
•Aggregation of individual preference statements. In group decision support, the
aggregation of individual preference statements into a group representation can
be supported through various approaches, for instance (i) by assigning weights
to the group members so that their weights reflect the perceived ‘importance’ of
the group members [48], (ii) by computing averages from the group members’
individual estimates [4], or (iii) by forming wide enough interval statements that
capture the preferences of all group members [40]. In some cases, the members
need not even approach the problem using the same problem representation: in
the Web-HIPRE software [33, 66], for instance, the group members may examine
the problem using their own individual value trees, whereafter recommendations
for group decision are generated by attaching importance weights to the group
members.
•Interfacing MCDA models with other decision support tools. In many decision
contexts, information about the impacts of the alternatives is generated with other
modeling tools. In such cases, MCDA models can be usefully interfaced with or
even integrated into other tools, because this may expedite the evaluation of de-
cision alternatives, contribute to enhanced communication, and facilitate the im-
plementation of decision recommendations. For example, the Web-HIPRE tool
has been incorporated into the RODOS decision support system for the predic-
tion of radiation exposures associated with nuclear emergency scenarios so that
the system provides timely guidance for the prioritization of countermeasures for
mitigating the impacts of an emergency [28, 33].
1.3 Group and Decision Characteristics
The development of MCDA-assisted decision support processes needs to be based
on a well-founded appraisal of the decision context. This involves a broad range
1 Multicriteria Decision Analysis in Group Decision Processes 9
questions about what is really at stake in the decision, who the stakeholders are
[24], and which group members will be engaged in the decision support process:
•Decision makers and their needs: Who are the decision makers? What is their
role in relation to the decision problem? Which stakeholders are affected by the
decision? What expectations are placed on the group decision support process?
Is it sufficient to provide just a decision recommendation, or is there a need to
justify and legitimize the recommendation? Is it the right time to launch a de-
cision support process, in the sense that there is a sense of urgency among the
decision makers, but no far-reaching commitments have yet been made to any of
the alternatives? In general, the process should be initiated early enough, because
this will leave more time for the possible generation and analysis of additional
alternatives, which in turn is likely to contribute to enhanced decision quality.
•Group members and group process: Have the group members collaborated on
earlier occasions? Is it likely that strongly opposing viewpoints will be pre-
sented? What is the prior level of trust that exists among the group members?
Is there a willingness to collaborate in a consensus-seeking spirit in an open
dialogue [84]? How can the facilitator best promote trust among the group mem-
bers?
•Level of knowledge: How familiar are the group members with the decision prob-
lem? What aspects of the decision problem do the group members have knowl-
edge on? How will the relevant sources of knowledge be captured during the
process?
•Possibilities for the use of support tools: How much time and effort can the group
members devote to the process? What methodological tools are best aligned with
such requirements (e.g., workshops, video conferences, internet-based surveys)?
What temporal, technical, and budgetary constraints apply to the decision making
process?
Furthermore, the characteristics of the decision problem can be clarified through
questions such as:
•Time for decision making: By what time is the decision to be made? Are there
possibilities for either hastening or postponing decision making? Is it possible to
organize iterative decision support processes where results from the early phases
inform later one?
•Reversibility and flexibility: Can the decision be modified or revoked later on? If
so, What implications do these possible flexibilities have for the definition of the
consequences of the different decision alternatives?
10 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
•Presence of uncertainties: How much is known about the different decision al-
ternatives and their consequences? Can the major uncertainties be reduced? If
so, when, how, and at what cost? Is the decision support process likely to benefit
from initial scenario studies which provide early learning experiences and offer
guidance for the collection of data?
•Reoccurrence of decisions: Has a related or similar problem been addressed be-
fore? If so, is it possible to re-use earlier decision models in support of current
decision making needs?
The above questions help determine how much time and effort should be invested
into the development of an MCDA-assisted decision support process (see also [72]).
For instance, the case for making an major investment is most compelling in deci-
sion problems where the impacts are significant, the decision is inflexible and irre-
versible, and where there is ample time for the analysis. Also, if it is expected that
the same decision problem will be encountered repeatedly, a sizeable investment
may be warranted even if it would not be justified by the significance of a single iso-
lated decision. In the presence of high uncertainties, it is pertinent to ask if it would
be advantageous to postpone the decision, in the expectation that some uncertainties
will be resolved so that more information could be used to generate a decision rec-
ommendation later on. Indeed, the key initial decision is whether or not the decision
should be taken now or later.
Another key consideration is whether the decision is to be taken in isolation or
possibly in connection with other decisions. Specifically, if the group members are
addressing several decisions together, it may be possible to apply methods of port-
folio decision analysis to develop recommendations that may be superior to those
reached by analyzing individual decisions one-by-one (see, e.g., [21]). This is be-
cause these methods help identify portfolios of ‘win-win’ recommendations which
are deemed acceptable by most or all group members.
1.4 Design of MCDA-Assisted Decision Support Processes
The careful consideration of the decision problem, and its relations to decision mak-
ers and stakeholders, is a key initial step in the design of an MCDA-assisted decision
support process. Due to the large variety of group decision making contexts and the
large number of MCDA methods, it is not possible to provide straightforward guide-
lines for this design task. Similarly, it is not possible to make general conclusions
about which methods are ‘best’ across the full range of decision contexts, given that
the relative advantages of different MCDA methods differ from one decision context
to another.
1 Multicriteria Decision Analysis in Group Decision Processes 11
These differences notwithstanding, the development and deployment of MCDA-
assisted decision support processes often involve steps such as:
•Identification of the potential need for MCDA approaches: A starting point for the
development of a MCDA-assisted group decision making process is the recogni-
tion of a decision problem which can benefit from an explicit articulation of mul-
tiple criteria and alternatives. This early stage–which is often quite ‘nebulous–
may benefit from the deployment of various problem structuring methods and
soft systems approaches (such as CATWOE [14]) which may yield some insights
into the possible benefits that may be achieved through more formal modeling ef-
forts.
•Elaboration of decision context. This involves the explicit identification of the
decision that is to be supported, in view of questions such as: Who are the deci-
sion makers? Which organizations and stakeholders groups are impacted by the
decision and how? What commitments and timeframes are involved? Will the
same decision problem be encountered repeatedly, or does the decision pertain
to one-of-a-kind problem?
•Identification of participants. The identification of the group members who will
be engaged part in the MCDA process either as decision makers, sources of ex-
pertise, or as representatives of stakeholder groups is an important phase that is
largely guided by an early analysis of the decision context. To ensure the trust-
worthiness of the process, it is therefore helpful to address considerations such
as comprehensiveness and balance. For instance, are all relevant interests and
sources of information duly represented? Or are some stakeholders dispropor-
tionately under/overrepresented?
•Design of the decision support process. The detailed design of the process in-
volves choices about what MCDA methods will be used and how these methods
will be deployed. The process often benefits from an explicit specification of the
roles in which the group members take part in the process. For example, some
group makers may take part in the identification of the relevant decision crite-
ria, in view of their understanding of the organization’s values and objectives;
but they may also take part in the process as suppliers of factual information
about the impacts of the different alternatives. Particularly in long-lasting policy
processes, different groups may participate in different stages and in different
tasks. For instance, there could be a small initial core group for the structuring of
the MCDA model, followed by the prioritization activities of a larger group and
the synthesis of results by a steering group. In general, the design phase should
yield a clear plan of how the process will be carried out. Such a plan is likely to
enhance the legitimacy of the process. It may also serve as communication tool
which clarifies how the different groups members can expect to benefit from their
participation [40].
12 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
•Enactment of the decision support process. This involves the use of the MCDA
methodologies and tools in accordance with the process design, going through
phases such as the elaboration of the values, objectives and criteria; elicitation
of preferences; development of alternatives; assessment of decision alternatives;
synthesis of decision recommendations; and discussion of results, possibly in a
workshop setting where the relevant decision makers are present. While adher-
ence to the process design is often useful, there may also be situations where it is
pertinent to adjust it in response to feedback that accumulates in the course of the
decision support process (see, e.g., [36, 61]). Also, when using methodologies,
attention must be given to the possibility of procedural biases and ways in which
these can be best avoided [74].
•Evaluation of the decision support process. The post evaluation of the decision
support process–in view of dimensions such as relevance of decision recommen-
dations or the uptake and implementation of decision recommendations–can of-
fer reflective insights and valuable learning experiences which are needed when
building cumulative competencies in decision modeling (see, e.g., [38, 65]).
The choice of an external facilitator is another important design issue. Decision
makers are rarely experts in MCDA methodologies, and consequently a neutral fa-
cilitator can be essential in ensuring that these methodologies are deployed con-
structively and productively. The specific competencies and past expertise of the
facilitator should be explicitly recognized during the design phase. In particular,
the MCDA process should not be designed “in the abstract”, resulting in mere role
descriptions, without considering the specific competencies of the individuals who
will enact these roles.
1.5 MCDA Methods in Action
We next exemplify the use of MCDA methods in group decision support in view
of selected case studies. Our selection is necessarily limited and merely highlights
the key aspects of MCDA support, particularly in the light of more recent appli-
cations that reflect advances in methods and tools. For earlier and more extensive
reviews, we refer to Bose et al. [10], Vetschera [86], Matsatsinis and Samaras [62],
and Wallenius et al. [88].
Mustajoki et al. [71] (see also [32, 37, 70]) consider the development of models for
the assessment of alternative strategies in response to a nuclear emergency situation.
These models–which were constructed through a close dialogue with key decision
makers (see also [37])–made it possible to evaluate different remediation alterna-
tives with regard to the attributes that captured main impacts (e.g., human health,
1 Multicriteria Decision Analysis in Group Decision Processes 13
social impacts, economic losses, environmental impacts). An important benefit of
using these models repeatedly in facilitated workshops was that the learning expe-
riences allowed the decision makers to acquire a better understanding of relevant
alternatives and tradeoffs. Many of these models and decision support tools (such
as Web-HIPRE) have been subsequently incorporated into RODOS, a real-time on-
line decision support system which supports the development of countermeasure
strategies in recognition of different time horizons [28]. It is of interest to note that
the use of MCDA tools for nuclear power issues in Finland began already in the
1980’s when the Parliament of Finland discussed whether or not a fifth nuclear re-
actor should be constructed. At that time, MCDA tools served to clarify differences
of opinion among different political groups [32].
K¨
onn¨
ola et al. [54] report a case study where national research priorities for the
forestry and forest-related industries were developed in three months by engaging
more than 150 people. Due to the tight schedule, the process relied extensively on
the web-based solicitation of prospective research themes proposed by members of
the research community. The themes were then commented on and evaluated by
specifically appointed reviewers with regard to three criteria: feasibility, novelty,
and industrial relevance. Based on these valuations, shortlists of most promising
themes were generated with the Robust Portfolio Modeling (RPM) methodology
[58]. The final priorities were developed in decision workshops where the RPM re-
sults helped ensure that the attention could be focused on the themes that appeared
most promising in view of the preceding consultation and multi-criteria evalua-
tion process. Analogous RPM-based processes have supported the development of
strategic product portfolios [67] and the establishment of priorities for international
research and technology development programmes [12, 13].
Hobbs and Meier [43] describe a comparative study where several MCDA methods
where employed for planning of a resource portfolio for Seattle City Light. In this
study, planners and interest group representatives applied different preference elici-
tation techniques–such as direct weight assessment, tradeoff weight assessment, ad-
ditive value functions, and goal programming–which were then compared in terms
of their perceived ease of use and several validity measures. The participants noted
that the MCDA methods did promote insights and increased their confidence in
decision making; yet no single method emerged as the best one. The results also
suggested two or methods should be ideally applied in conjunction, because this
would generate additional insights and allow for consistency checks against biases.
Barcus and Montibeller [3] describe a MCDA model that was used to support deci-
sions concerning the allocation of team work in a major global software company,
subject to the demands that arise from technical complexities, multiple communica-
tion lines and stakaholders’ divergent interests. This model was built in close collab-
oration with software development project managers, based on MAVT and decision
conferencing. It addressed both software engineering attributes as well as soft and
strategic issues, such as team satisfaction and training opportunities. Its deployment
14 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
contributed to improve organizational learning, most notably by uncovering earlier
inconsistencies in the communication of strategic objectives and by improving the
communication of project managers concerns to other managers.
Bell et al. (2003) [5] consider uses of MCDA methods in integrated assessment
(IA) where the aim is to capture interactions of physical, biological, and human
systems so as to better understand long-term consequences of environmental and
energy policies (e.g., limits on greenhouse gas emissions, and other strategies for
the mitigation of climate change). Specifically, they organized a workshop where
climate change experts used several MCDA methods for the ranking of hypothetical
policies for abating greenhouse gas emissions, using data outputs from integrated as-
sessment models. These methods did help group members understand policy trade-
offs as well as complex interdependencies among value judgments, data outputs and
recommended decisions. Inspired by encouraging results of their case study, Bell et
al. [5] outline alternative approaches for the use of MCDA methods in integrated
assessment.
Merrick et al. [68] conducted a multiple-objective decision analysis in order to as-
sess the quality of an endangered Virginian watershed and to guide efforts towards
improving its future quality. In their case study, the group members represented
a broad range of expertise and perspectives, such as stream ecology, environmen-
tal policy, water hydrology, sociology, psychology, and decision and risk analysis,
among others. The group members’ values and goals were brought together using
a watershed management framework that explicated the multiple criteria in maxi-
mizing the quality of the watershed. Specifically, the resulting MCDA framework
helped identify significant value gaps and contributed to the shaping of programs
for improving the quality of the watershed.
Bana e Costa et al. [2] helped the Portuguese Institute for Social Welfare to adopt a
systematic and transparent decision process for the development and renewal of the
social infrastructures whose role is to provide funding and services to children, the
elderly and the disabled. This process–which was based on decision conferencing
and multicriteria modeling–engaged key decision makers in the three main phases
of problem structuring, evaluation and prioritization. The proposed socio-technical
process was seen to improve the transparency of decision making, the “rationality”
of resource allocation decision, and the cost-effectiveness of decisions.
Belton et al. [7] report experiences from the development of strategic action plans
for the Department of a large UK Hospital Trust. Their case study was based on
the combined use of (i) the strategic options and strategic analysis (SODA) in the
problem structuring phase and (ii) the MAVT-analysis during the evaluation of de-
cision alternatives. The study was carried in a 2-day facilitated workshop where the
joint use of different methodologies helped the group make progress towards the
definition of a shared strategic direction while it also promoted a shared and im-
proved understanding of key issues. Building on this case study, Belton et al. also
1 Multicriteria Decision Analysis in Group Decision Processes 15
discuss what benefits may arise from the integration of these two approaches, and
what implications such an integration has for the development of methodologies and
tools.
Hiltunen et al. [42] report experiences from a case study where Mesta, an Internet-
based decision-support tool, was employed for the development of forest manage-
ment strategies for state-owned forests. Based on an explicit recognition of the
stakeholders’ objectives and the examination of strategy alternatives with regard
to five evaluation criteria, the strategy alternatives were categorized based on the
threshold levels acceptable’ or not acceptable’ with respect to each criterion. The
user interface of Mesta allowed these thresholds to be holistically adjusted until ac-
ceptable solutions that also satisfied production possibilities were found. Once the
stakeholders had set their own thresholds in Mesta, they then negotiated until they
were able to agree on the forest management principles that were then implemented
in two regions.
In many countries, MCDA tools are widely applied in problems of water and en-
vironmental management [31, 46, 50]. For example, the Finnish Environment In-
stitute has adopted systematic processes in order to guide its decisions on water
regulation [61]. In many ways, these processes also illustrate the different phases
we have discussed in this Chapter, particularly as concerns the identification and in-
volvement of stakeholders; collaborative and iterative development of alternatives;
MCDA-assisted evaluation of alternatives in workshops; and communication of re-
sults to citizens over the Internet. These processes are noteworthy in that they have
paid explicit attention to potential biases and their mitigation.
1.6 Rationales for the Deployment of MCDA Methods
The above case studies, among many others, illustrate the benefits of MCDA meth-
ods in group decision making. Indeed, there are complementary rationales for the
deployment of MCDA methods:
•One of the key rationales for using MCDA methods is enhanced transparency.
This is achieved when the group members’ understand the structure of the
MCDA model and the interdependencies between the model outputs (alterna-
tives’ MAVT values, decision recommendations) and the model inputs (scores,
attribute weights) (see [2, 28, 44, 71]). Such an understanding will create trust in
the decision recommendations and also promote commitment to the decision im-
plementation. Transparency also offers support for learning processes where the
group members can be explore interactively how changes in the input parameters
will be reflected in the decision recommendations [28, 79].
16 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
•The legitimacy of the decision support process is often a key concern, particularly
in problems such as environmental planning where the decisions impact several
stakeholder groups [31, 50]. Indeed, even if a less formal decision support pro-
cesses might lead to the same decision outcome, a model-based approach may
still be warranted because it ensures, among other things, that alternatives will be
treated consistently within a comprehensive evaluation framework.
•The use of MCDA methods typically leaves an audit trail that records the steps
through which the decision recommendation was arrived at. The availability of
such an audit trail can be particularly valuable in situations where the decision
may have to be reached under considerable time pressure (e.g., emergency man-
agement, [9, 28]), but where there is a need to improve the quality of these pro-
cesses, which suggests that they should be subjected to scrutiny later on. At best,
audit trails may suggest instructive ‘lessons learned’ that serve to improve the
quality of decision making processes.
Table 1.1 Rationales for the deployment of MCDA methods
Rationale Brief definition Benefits in group decision support
Transparency Relationships between model inputs
and decision recommendations can
be readily understood
Supports learning by showing how
changes in model inputs are related to
the recommendations
Legitimacy Process appropriately embedded in
its institutional and organizational
context
Lends authority and credibility to
results
Facilitates the implementation of
decision recommendations
Audit trail Availability of a track record of the
consecutive steps enacted during the
support process
Permits reflective ex post evaluations of
the process
Enhances learning
There are even further benefits that can be sought after. For instance, the devel-
opment of an MCDA model for a specific decision context may result in generic
modeling frameworks that can be deployed in other contexts as well. Such a reuse
and adaptation of decision models may offer cost savings, because the develop-
ment of the MCDA model need not be started from the beginnings. It may also
contribute to the attainment of of quality objectives. Yet, some caution is called for
when introducing existing models into other contexts, because the contexts need to
be sufficiently similar (e.g., characteristics of decision objectives, evaluation crite-
ria, group members, decision alternatives). The reuse of decision models may not
necessitate any essential changes in the model structure: however, the learning as-
pects of the process may warrant particular attention, because model reuse may not
require equally thorough processes of initial deliberation.
1 Multicriteria Decision Analysis in Group Decision Processes 17
1.7 Outlook for the Future
The outlook for MCDA methods looks promising due to the potential of structured
problem solving methods in addressing complex decisions. This potential is further
amplified by recent technological and methodological developments:
1. Technological progress in ICT: The rapid diffusion of advanced information and
communication technologies (ICT) offer enhanced possibilities of interfacing
group members with MCDA models. For instance, mobile devices can be em-
ployed to solicit preference statements from the participants via text messages,
and these devices can be used for the dissemination of results as well (see, e.g.,
[33]). It has also become easier to incorporate different kinds of inputs in decision
models so that both quantitative data (e.g., scores, weights, values) and qualita-
tive data (e.g., verbal descriptions, visual images) can be handled in an integrated
manner. This kind of an integration will enable the development of decision sup-
port tools that contain ‘richer’ information in contexts such as e-democracy [26];
yet the availability of tools does not suffice without learning from good practices
[38]. Furthermore, it is plausible that repositories of model templates will be-
come popular within some communities of group members for specific decision
problems. Such templates may contain useful information about the problems
that are being addressed, and they may ensure that good modeling practices are
applied consistently in problems that are encountered repeatedly (see, e.g., [34]).
2. Adoption of multi-modeling approaches: Many MCDA methods are good at syn-
thesizing and visualizing group members’ preferences by using numerical rep-
resentations. Yet the standard methods offer relatively static representations that
do not necessarily capture dynamical cause-and-effect relationships, or verbal
arguments that underpin stated preferences. In consequence, it may be useful
to complement MCDA methods with other approaches that serve to enrich the
decision support process. Examples of these approaches include, among others,
causal maps [69], reasoning maps [65], cognitive maps [20], reference point ap-
proaches [56, 57], system dynamics [11, 83], and argumentation analysis [63].
3. Joint consideration of multiple decisions through portfolio modeling: In many
problems, decision makers have to address multiple decision items in conjunc-
tion. This is because the group members’ preferences on one decision item may
depend on what decisions are taken on the other issues (cf. composing a meal).
The decision items may also be linked through shared constraints: this is the case,
for example, when allocating resources to different organizational units, because
the resources that are given to any one unit will have an impact on how much
resources remain available for the others (see [53]). These kinds of interdepen-
dencies can be captured through methods of portfolio decision analysis (see, e.g.,
[58, 59, 73]) which offers recommendations on all decision items jointly. Even
if there are no interdependencies among the items, portfolio modeling can still
18 Ahti Salo and Raimo P. H¨
am¨
al¨
ainen
be helpful, because it allows the group members to search for decision combi-
nations that would be acceptable to all group members. However, some caution
is needed when increasing the number of items that are covered simultaneously,
because the development of single large model that is applicable to all items may
be difficult to develop and apply.
4. Evaluation of the impacts of MCDA methods. The development and deployment
of MCDA methods can benefit significantly from the systematic evaluation of
the impacts of these methods on the decision support process. Here, statistical
analyses of controlled and well-designed experiments may, in principle, provide
information about the comparative merits of different approaches, even if such
experiments can rarely be conducted in real decision making situations. Con-
trolled experiments can also provide information about in what decision con-
texts and in what ways different biases are likely to influence the recommenda-
tion (see, e.g., [15, 45, 74]). But because controlled experiments cannot replicate
the full richness of real decisions, there is a strong need for reflective analyses
of high-impact MCDA case studies. Such analyses should not focus narrowly
on the MCDA methods and their properties. Rather, they should encompass the
broader contextual problem characteristic and report ‘lessons learned’ and ‘good
practices’ that help design and implement decision support processes in other
contexts well.
The above observations suggest possibilities of extending MCDA-assisted processes
by harnessing latest technologies, multiple methodologies, or explicit interfaces to
other systems. Yet, the development of these extensions needs to build on an ap-
praisal of whether the benefits of more encompassing models outweigh the addi-
tional efforts that are required. Even if the ultimate aim is to develop integrated
planning environments that embody multiple methodologies and offer automated
links to other modeling environments, it may best to proceed incrementally and
to add additional components iteratively, because such an iterative approach offers
useful learning experiences on the way.
There are growing pressures to improve the quality of decision making processes,
particularly when decisions are taken recurrently and when they have contentious
and far-reaching impacts. Here, quality has many dimensions, such as the abil-
ity (i) to adequately represent the group members’ preferences, (ii) to derive and
communicate well-founded decision recommendations, and (iii) to ensure the le-
gitimacy, consistency, transparency and comprehensiveness of these processes. Of
these closely intertwined quality dimensions, the first pertains mostly to method-
ology, while the second calls for support tools and the third one requires that the
decision support process is properly embedded in its organizational context. As a
potentially promising development, the quest for higher quality may create demand
for dedicated decision models which have been adapted to specific decision prob-
lems and which can be effectively re-deployed by re-using existing data sets and
by building on earlier experiences. One may even envisage that such models will
1 Multicriteria Decision Analysis in Group Decision Processes 19
be reviewed externally to ensure the adequacy of decision models in view of their
intended uses.
1.8 Conclusion
We conclude this Chapter by reasserting our belief in the major potential of MCDA
methods in complex group decision making contexts. As demonstrated by numer-
ous applications, MCDA methods offer structured frameworks for addressing multi-
faceted problems where group members’ preferences can be captured and synthe-
sized into well-founded decision recommendations. By doing so, these methods fos-
ter collective learning processes and generate a better shared understanding of how
the decision alternatives relate to the decision objectives.
MCDA methods can also be pivotal in improving the quality of decision processes
so that demands for transparency, coherence, consistency, and comprehensiveness
can be met. The attainment of such quality objectives is facilitated by recent method-
ological advances, improved availability of tool support and, quite importantly,
by the growing body of reflective reports on case studies which demonstrate how
MCDA methods can be successfully employed in different problem contexts. We
also contend that MCDA methods merit to be studied also by those who have a a
broader interest in group decision and negotiation, for because these methods are
quite central in group decision support and because current methodological and
technological developments open up exciting opportunities for the further advance-
ment of the field.
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