STEPS Pathways Methods
PART 8
Multicriteria Mapping (MCM) - an illustrative example
Professor Andy Stirling
Co-director, STEPS Centre
www.steps-centre.org
www.sussex.ac.uk/spru
www.multicriteria-mapping.org
An Example: Multicriteria Mapping (MCM)
choose
options
MCM: what goes in
choose
options
MCM: what goes in
MCM: choosing options
choose
options
MCM: what goes in
define
criteria
MCM: defining criteria
choose
options
define
criteria
assess
scores
MCM: what goes in
choose
options
define
criteria
assess
scores
option 1
option 2
option 3
option 4
performance
CRITERION A
MCM: what goes in
choose
options
define
criteria
assess
scores
explore
uncertainty
MCM: what goes in
option 1
option 2
option 3
option 4
CRITERION A
performance
choose
options
define
criteria
assess
scores
explore
uncertainty
A
B
C
MCM: what goes in
MCM: assessing scores
choose
options
define
criteria
assess
scores
assign
weights
explore
uncertainty
performance
option 1
option 2
option 3
option 4
OVERALL RANKINGS
MCM: what goes in
choose
options
define
criteria
assess
scores
assign
weights
explore
uncertainty
performance
option 1
option 2
option 3
option 4
OVERALL RANKINGS
MCM: what goes in
choose
options
define
criteria
assess
scores
assign
weights
explore
uncertainty
performance
option 1
option 2
option 3
option 4
consider
ranks OVERALL RANKINGS
MCM: what goes in
MCM: assigning weights http://mcm.dabdev.net/projects/ec4cc0623c5f4bf09acd1febec66e5f6/engage/engagements/eadd5a2094cf419d86cd4dd99223f03e/#weights
choose
options
define
criteria
assess
scores
assign
weights
explore
uncertainty
performance
option 1
option 2
option 3
option 4
consider
ranks OVERALL RANKINGS
MCM: what goes in
O
C
S
U
W
R
From MCM sessions to opening up deliberation
From MCM sessions to opening up deliberation
From MCM sessions to opening up deliberation
group-based
elicitation or
deliberation
From MCM sessions to opening up deliberation
wider
stakeholder
deliberation
From MCM sessions to opening up deliberation
general diversity heuristic
ij (dij)α.(pi.pj)
β
Mapping Diversities SEG
Dancing with the quantification devil… what isn’t counted, doesn’t count!
Yoshizawa, Suzuki, et al
Rafols, Porter and Leydesdorff (2010)
Diversity in Scientometrics
Scientometrics of disciplinarity & directionality in research & innovation
narrow
broad
closing down opening up
expert /
analytic
participatory /
deliberative
citizen’s juries
decision
analysis
participatory
rural appraisal
stakeholder
negotiation
q-method
sensitivity
analysis
deliberative
mapping do-it-yourself
panels
open
space
cost-benefit
analysis
risk
assessment
interactive
modelling
structured
interviews
interpretive
participant
observation
multi-site
ethnographic-
methods
citizen’s juries
consensus
conference
open
hearings
dissenting
opinions
multi-criteria
mapping
scenario
workshops
Building Repertoires (from Dynamic Sustainabilities)
For “opening up new political spaces”
contending
histories
spot-the-
narrative
industry
NGOs
qualitative picture of framings, focusing
structured as ‘optimistic’ or ‘pessimistic’ expectations
(as well as: option/criteria definitions; transcript ‘nuggets’)
MCM: what comes out
optimistic assumptions about
technical operation
pessimistic view of how option
is likely to perform in practice
All annotations and discussion transcripts
entered and processed in database
Also detailed text ‘reports’ for selected parameters
rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out
uncertainties by option
rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out
A
B
C
D
uncertainties by option
rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out
A
B
C
D
uncertainties by perspective
academics
industry
government
NGOs
uncertainties by option
rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out
A
B
C
D
uncertainties by perspective
academics
industry
government
NGOs
scores for particular issues
A
B
C
D
uncertainties by option
rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out
A
B
C
D
uncertainties by perspective
academics
industry
government
NGOs
weights by issue for perspectives scores for particular issues
A
B
C
D
economics
health
environment
equity
academics
industry
government
NGOs
uncertainties by option
rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out
A
B
C
D
uncertainties by perspective
academics
industry
government
NGOs
weights by issue for perspectives scores for particular issues
A
B
C
D
economics
health
environment
equity
academics
industry
government
NGOs
improved services
altruistic donation
presumed consent
xenotransplantation
embryonic stem cells
healthier living
low performance high
MCM Results: an example from health policy
women’s panel (BC1)
improved services
altruistic donation
presumed consent
xenotransplantation
embryonic stem cells
healthier living
low performance high
MCM Results: an example from health policy
women’s panel (BC1)
improved services
altruistic donation
presumed consent
xenotransplantation
embryonic stem cells
healthier living
men’s panel (BC1)
low performance high
MCM Results: an example from health policy
women’s panel (BC1)
improved services
altruistic donation
presumed consent
xenotransplantation
embryonic stem cells
healthier living
improved services
altruistic donation
presumed consent
xenotransplantation
embryonic stem cells
healthier living
women’s panel (C2D) men’s panel (C2D)
men’s panel (BC1)
low performance high
MCM Results: an example from health policy
Risks and benefits of different agricultural strategies
under assumptions of selection of UK expert policy advisers (1999)
organic
environmental
intensive
GM + labelling
GM + monitoring
GM + voluntary controls
low performance high
MCM Results: an example from GM food
Risks and benefits of different agricultural strategies
under assumptions of selection of UK expert policy advisers (1999)
organic
environmental
intensive
GM + labelling
GM + monitoring
GM + voluntary controls
GOVERNMENT
organic
environmental
intensive
GM + labelling
GM + monitoring
GM + voluntary controls
low performance high
MCM Results: an example from GM food
Risks and benefits of different agricultural strategies
under assumptions of selection of UK expert policy advisers (1999)
organic
environmental
intensive
GM + labelling
GM + monitoring
GM + voluntary controls
GOVERNMENT INDUSTRY
organic
environmental
intensive
GM + labelling
GM + monitoring
GM + voluntary controls
low performance high
MCM Results: an example from GM food
Risks and benefits of different agricultural strategies
under assumptions of selection of UK expert policy advisers (1999)
organic
environmental
intensive
GM + labelling
GM + monitoring
GM + voluntary controls
GOVERNMENT INDUSTRY
organic
environmental
intensive
GM + labelling
GM + monitoring
GM + voluntary controls
PUBLIC INTEREST
low performance high
MCM Results: an example from GM food
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