Methodology
Wicked problems (aka social messes)
have five criteria
1. Not easily quantifiable – no data,
uncertain data, incomplete data
2. Problem is continually developing and
mutating
3. Full of ambiguities, contradictions and
vicious circles
4. Stakeholder oriented with strong
political, moral and professional issues
5. Reactive: the problem fights back
Wicked problems are multifaceted, multi-dimensional
‘Wicked problems’
H. Rittel & M. Webber (1973)
Dilemmas in a General Theory of Planning
Strategy foresight engages clients and stakeholders
with their complex challenges – aka ‘Wicked Problems’
Wicked problem (Rittel et al 1973)
Compare Tame Problem
1. Not easily quantifiable as uncertain
or incomplete information
1. Has a relatively well-defined and
stable problem statement
2. Continually developing and mutating
2. Has a definite stopping point i.e. we
know when the solution is reached
3. Full of ambiguities, contradictions
and vicious circles
4. Stakeholder oriented with strong
political, moral & professional issues
5. Reactive: the problem fights back
3. Binary solutions: objectively
evaluated as being right or wrong
4. Has solutions which can be tried and
abandoned
Wicked problems transcends sectors
 What should be our negotiating response based on the various possible
positions adopted by the other party?
 How should we deal with tax-related private banking challenges?
 How do we pre-empt the risk to corporate reputation in a complex,
mutating environment?
 What are the core issues that a corporate governance strategy should try
to incorporate?
 How much should the emerging sciences – synthetic biology, stem cells,
nanotechnology, pediatric trials – be regulated?
Morphological analysis is a problem-structuring method
ideal for tackling wicked problems in multiple
dimensions using extended typology analysis

Fritz Zwicky (1898-1974)
•
•

Professor of Astronomy, Caltech (1942-68)
Co-founder Aerojet Engineering Corporation
Developed Morphological Analysis as a
problem-structuring method to address
genuine uncertainty and stress test
boundary conditions, resulting in discovery :
•
•
•
Dark matter (1934)
Triple hypothesis - supernova, neutron stars,
and cosmic rays (1934)
Gravitational lensing (1937)
Depicting a 3-D Morphological Field
Point of origin
Where do you place the 4th, 5th…..nth dimension?
How to build a
morphological model
Example:
What to do about the Swedish
Nuclear Bomb Shelter Program
following collapse of Soviet Union
Dimensions
1. What’s the problem (focus question)?
2. Convene a subject – matter specialist team
3. Stakeholders facilitated to agree
on most important Dimensions of the problem complex
Quantitative
scale
2 x 2 matrix
Normative,
non-quant scale
Agree and define a range of ‘values’ or ‘conditions’ for
each dimension
A morphological model of 2034 configurations –
how to reduce to a workable number?
Consider every pair - facilitate team to knock out
illogical, or empirical, contradictory pairs
- or 0 possible
Cross
Consistency Matrix
x = not possible
S or F = not optimal
Note reduction
> 90%
Solution space: list of surviving, internally consistent
combinations – all blue cells are compatible
Strategy Foresight leaves clients with unique software
to construct their own scenarios & strategy alternatives
1. Input and outputs interchangeable
- manipulate both cause and effect
2. Ability to freeze and compare
scenarios and strategy alternatives
3. Reducing alternatives does not
require re-developing scenarios
4. Easily updatable - visual, real time
systematic group exploration
5. Speed, efficiency and cost of
facilitation and model development
fraction of traditional consultancies
– enhances entire value chain
What’s the outcome?
• Managing genuine uncertainty in real time by placing comparative
judgements on a sound methodological basis
• Accommodating multiple, alternative perspectives to anticipate
unintended consequences vs. prescribing single solution
• Anticipating consequences of decisions made under conditions of
high uncertainty, incomplete data and high decision stakes
SFP uses multi-methodology to give clarity
to messy situations and decision-support in
ranking solutions
Bayesian Belief Networks
Morphological Analysis
Structure (dimensionalise) the
problem complex
Decision-making process for prioritising
alternatives when multiple criteria must be
considered
Assigning a
probability to an
event to give
indication how
strongly client
believes an event
will occur
Analytic Hierarchy Process
Analytic Hierarchy Process
 Give a brief description of the methodology
• Will avoid the mathematics (Eigenvector)!
 Provide examples of where AHP has been used
 Illustrate principle by way of simple example – choosing a car
Analytic Hierarchy Process is a decision making
method for prioritising alternatives when multiple
criteria must be considered

Developed by Thomas Saaty in the 70’s for
rational decision support for complex decision
situations with multiple criteria

Why? Observed the lack of practical,
systematic, approach for priority setting and
decision making by groups when dealing with
uncertainty

Crucial decision situations, forecasts or resource
allocations involve too many dimensions for
humans to synthesize intuitively
Examples of where AHP has been used
 Investigating the effect of website quality on e-business success
 Assessing supply chain risks for the off-shoring decision by a US
manufacturing company
 Involving patients in decisions regarding preventive health
interventions
 Decision support for selecting exportable nuclear technology
 A departmental approach to apportion co-author responsibility
People deal with complexity by decomposing the
problem into hierarchy of common clusters of
criteria, sub-clusters of criteria etc.
Selecting
a New Car
Goal
Criteria
Alternatives
Style
-
Audi A3
VW Golf
Megane
Ford Focus
Reliability
-
Audi A3
VW Golf
Megane
Ford Focus
Fuel Economy
-
Audi A3
VW Golf
Megane
Ford Focus
It is simpler to make comparative judgements
between two factors using a ratio scale
Style
Reliability
Economy
Style
1:1
1:2
1:3
Reliability
2:1
1:1
4:1
Economy
1:3
1:4
1:1
Given uncertainty or
incomplete data, relative
weights are agreed by
the working group or the
managerial team and led
by a facilitator
Ratio
Description
1
Equally preferred
3
Moderately preferred
5
Strongly preferred
7
Very strongly preferred
9
Extremely strongly preferred
As an illustration, ranking the priorities of the
criteria can be done by a simple method
Criteria
Average
PRIORITY
Style
0.30
0.29
0.06
0.22
2
Reliability
0.60
0.57
0.75
0.64
1
Economy
0.10
0.14
0.19
0.14
3
SUM
3.33
1.75
5.33
100%
1. Sum ratios in each column
2. Divide each ratio by the column sum
3. Compute the row averages
Repeat process with each decision alternatives (Audi,
Gold, Megane, Focus) with respect to each criteria:
STYLE
Audi A3
VW Golf
Megane
Ford Focus
Normalised
Audi A3
1:1
1:4
4:1
1:6
0.12
VW Golf
4:1
1:1
4:1
1:4
0.25
Megane
1:4
1:4
1:1
1:5
0.06
Ford Focus
6:1
4:1
5:1
1:1
0.58
Qualitative judgements
and Quantitative measures
can be incorporated in the
same decision matrix
FUEL
ECONOMY
Miles per
gallon
Normalised
Audi A3
45
0.26
VW Golf
50
0.28
Megane
42
0.24
Ford Focus
39
0.22
Combine the hierarchy…..
Selecting
a New Car
1.0
Style
0.22
Audi A3
VW Golf
Megane
Focus
0.12
0.25
0.06
0.58
Reliability
0.64
Audi A3
VW Golf
Megane
Focus
0.38
0.30
0.07
0.26
Fuel Economy
0.14
Audi A3
VW Golf
Megane
Focus
Which car did you choose?
0.26
0.28
0.24
0.22
The correct method requires dedicated
software and facilitation to calculate the…
 Eigen Vector
• mathematical function used in prioritising elements of
different sizes and scale in a matrix
 Consistency ratio
• a measure how consistent the judgements have been
relative to large samples of purely random judgements
• must be less than 10% (dependent upon team expertise
and quality of facilitation)
To recap….
The Facilitated Process
The Rationale
1.
 For multi-inclusive modelling
2.
3.
4.
Deconstruct problem into a
hierarchy
Make pairwise comparison and
establish priorities of elements
in the hierarchy
Synthesise the results (to obtain
the overall ranking of
alternatives w.r.t. goal)
Evaluate consistency of
judgements
• i.e. ‘and’ rather than ‘or’ (use
morphological analysis)
 Allows rational group decision
making where stakeholders use
experience, data/knowledge to
address uncertainty
 Gives decision support to complex,
mutating problems