Articles & extra chapters Strategic Decision Making
1. Montibeller & Franco (2010): Multi-Criteria Decision Analysis for Strategic Decision Making.
2. Tversky and Kahneman (1986): Rational choice and the framing of decisions
3. Holmes et al (2011): Management Theory Applications of Prospect Theory: Accomplishments,
Challenges, and Opportunities
4. Axelrod:, Chapters 1, 2, 3
5. Snidal (1985): The Game Theory of International Politics
6. Raiffa (2002): Negotiation Analysis. Chapter 23
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Montibeller & Franco (2010): Multi-Criteria Decision Analysis for Strategic Decision Making
In this chapter we discuss the use of Multi-Criteria Decision Analysis (MCDA) for supporting strategic
decision making, particularly within strategy workshops.
2.1 Introduction
A strategic decision has been defined as one that is “important, in terms of the actions taken, the
resources committed, or the precedents set”. Furthermore, the process of creating, evaluating and
implementing strategic decisions is typically characterized by:
The consideration of high levels of uncertainty
Potential synergies between different options
Long term consequences
The need of key stakeholders
We believe that discrete-alternative MCDA methods can be useful for supporting a strategy team
tasked with designing and selecting high-value strategic options.
2.1.1 Strategic Decisions and Strategic Decision Making
The popular view of strategic decisions is that they typically involve a high degree of uncertainty, high
stakes, major resource implications, and long-term consequences. One of the strengths of this
traditional view is that it conceptualizes the strategic decision making process in a way that is
consistent with the reality faced by practicing managers. This conceptualization, however, has been
criticized for assuming a rational and linear relationship between decisions and actions that has not
been empirically proven
2.1.2 Technical Complexity
The two most troublesome challenges in dealing with strategic decisions are the inescapable
presence of high levels of:
Uncertainty: sources of uncertainty are:
o Epistemic: refers to a lack of complete knowledge about an organization’s external
environment and its impact on the performances of potential strategies.
o Organizational values: this happens when there is doubt about what strategic objectives,
or policy values, should guide the decision or choice of action.
Decision complexity: a major source of decision complexity is the inter-relationship among
choices. Strategic decisions involve different levels of granularity. The challenge for managers is
thus to overcome the cognitive burden associated with evaluating a large set of interconnected
strategic decisions, and to devote a substantial amount of time working to achieve a holistic and
satisfactory strategic focus.
,2.1.3 Social Complexity
Social representations: strategic decisions are socially produced and reproduced mental
frameworks through which managers make sense of their strategic concerns and so are able to
act upon them. The strategic decision making thus provides the cognitive structure within which
strategic change takes place in organisations.
Where does the process of producing and reproducing strategic decisions take place? Strategy
workshops typically involve a group of managers representing key organisational stakeholder groups
that come together to impose a structure to a decision problem which they perceive as “strategic”.
Furthermore, the internal negotiation process among members of the managerial team does not
take place in a political vacuum and political conflict is also possible. Episodes of strategic decision
making such as strategy workshops then involve processes of psychological and political internal
negotiation, where issues of decision structuring, group dynamics and power become critical in
building up momentum for strategic action.
2.2 MCDA for Strategic Decision Making: modelling
2.2.1 Tackling Uncertainty with Future Scenarios
In traditional decision analysis, the standard way of analysing decisions under uncertainty is to
represent options and uncertainties as a decision tree and then select the option with the highest
expected value. There are three main assumptions in this type of analysis:
1. Outcomes from a chance node should be mutually exclusive (i.e., only one of them will happen)
and collectively exhaustive (i.e., they cover all possible outcomes that may happen in the future).
These two conditions make the sum of the probabilities of outcomes equal to one.
2. It is possible to obtain, in a reliable way, accurate probabilities of outcomes.
3. The use of the expected value rule as a way of selecting the best alternative: the expected value
rule only makes sense in repeated gambles, where the expected value provides a weighted-
average outcome.
In our own experience of providing strategic decision support to organisations, scenario planning has
proved to be a powerful tool to increase awareness about future uncertainties and enhance
creativity in thinking about possible strategies.
2.2.2 Considering Multiple Objectives
The popularity and advantages of scenario planning, combined with the power of evaluation of
MCDA, provides a potent set of decision-support tools for strategic decisions. One important change
that organisations may experience, when using MCDA for strategic decisions, is the use of a value-
focused framework to guide the decision making process. In this case, strategies are seen as means
to the achievement of the organisation’s strategic objectives. This may help both in aligning the
strategic vision of the organisation with its strategic objectives, and in better scoping the strategic
choices it is considering. A key aspect in supporting strategic decisions using value-focused thinking
is, therefore, the need to help the definition and structuring of these strategic objectives.
2.2.3 Identifying Robust Options
The early focus of traditional decision analysis was in providing a single solution, the one that
maximises the expected value/utility. Instead of maximum expected utility, scenario planning
proposers have stressed the need for finding robust strategies, those that perform relatively well
across the scenarios. The multi-criteria community has also made calls for a focus on robustness
instead of optimization. If the analyst is using MCDA with multiple scenarios, two aspects should be
of concern:
1. Inter-scenario robustness: the robustness of performances of a strategy across scenarios. Thus a
strategy that performs relatively well on all scenarios exhibits higher (inter-scenario) robustness
than one that performs poorly on a given scenario.
2. Inter-scenario risk: the spread of performances across scenarios.
, One challenge of using the concept of robustness is that there are different ways of conceptualising
it. Simple way is to use the maximin rule or the min-regret; in both cases the analyst needs to
normalise the scales under each scenario, to make them comparable.
2.2.4 Designing Robust Options
Much of the focus of the MCDA literature has been on evaluating options, given a predefined set of
alternatives. Particularly at the strategic level - do not start with a well-defined set of options. In this
regard, the decision analyst can help decision makers in:
1. Identifying strategic options: the identification of options is usually supported during the
problem structuring phase. We have been using extensively cognitive maps for the generation of
options.
1. Designing better ones: one main advantage of using MCDA for supporting strategic decision
making stems from the specification and achievement measurement of the organisation’s
strategic objectives.
2.2.5 Designing and Appraising Complex Strategic Options
At a strategic level, however, many times decision-makers
are faced with far more complex strategic choices or
policies that are composed by a large set of sub-options. A
challenge in this type of problem is the cognitive burden
involved in appraising holistically the performance of each
policy and the time burden that may be required to
evaluate a large set of options. To overcome this, a tool can
be used such as the Analysis of Interconnected Decision
Areas (AIDA) technique that is part of the Strategic Choice
Approach developed by Friend and Hickling, where the links
between several “decision areas” are represented.
2.2.6 Considering Long Term Consequences
Most of the MCDA applications reported in the literature assess single-point outcomes, which try to
represent the performance of an option if it were implemented. Particularly in strategic decision-
making, however, considering long-term consequences is relevant and, many times, crucial. One
relatively simple way of considering long-term consequences in these cases is by applying time
discounting, as in net present value (NPV) analysis. A key challenge of NPV analysis is always to
define a suitable discount rate.
2.3 MCDA for Strategic Decision Making: Facilitating Process
2.3.1 Facilitated Decision Modelling
The term “facilitated decision modelling” will be used here to describe a process by which formal
decision models are jointly developed with a strategy team, in real time, and with or without the
assistance of computer support. We consider a decision model as “formal” if it:
Represents a strategic decision problem either in terms of cause and effect relationships; or
of relationships between decision choices and their (deterministic or uncertain) consequences.
A formal decision model is amenable to analysis and manipulation, but not necessarily fully
quantifiable.
When members of a strategy teams participate in a facilitated modelling process, they engage in
“conversations” to exchange their understandings and views about the strategic decision that is
being analysed. So, facilitated decision modelling is also an interactive process. Facilitated modelling
is typically organised into group work stages, which roughly correspond to:
Structuring the decision problem and agreeing a decision focus;
