1. 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

2. An Example: Multicriteria Mapping (MCM)

3. choose
options
MCM: what goes in

4. choose
options
MCM: what goes in

5. MCM: choosing options

6. choose
options
MCM: what goes in
define
criteria

7. MCM: defining criteria

8. choose
options
define
criteria
assess
scores
MCM: what goes in

9. choose
options
define
criteria
assess
scores
option 1
option 2
option 3
option 4
performance
CRITERION A
MCM: what goes in

10. choose
options
define
criteria
assess
scores
explore
uncertainty
MCM: what goes in
option 1
option 2
option 3
option 4
CRITERION A
performance

11. choose
options
define
criteria
assess
scores
explore
uncertainty
A
B
C
MCM: what goes in

12. MCM: assessing scores

13. 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

14. 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

15. choose
options
define
criteria
assess
scores
assign
weights
explore
uncertainty
performance
option 1
option 2
option 3
option 4
consider
ranksOVERALL RANKINGS
MCM: what goes in

16. MCM: assigning weights
http://mcm.dabdev.net/projects/ec4cc0623c5f4bf09acd1febec66e5f6/engage/engagements/eadd5a2094cf419d86cd4dd99223f03e/#weights

17. choose
options
define
criteria
assess
scores
assign
weights
explore
uncertainty
performance
option 1
option 2
option 3
option 4
consider
ranksOVERALL RANKINGS
MCM: what goes in

18. O
C
S
U
W
R

From MCM sessions to opening up deliberation

19. 
From MCM sessions to opening up deliberation

20. 


From MCM sessions to opening up deliberation

21. 




group-based
elicitation or
deliberation
From MCM sessions to opening up deliberation

22. 













wider
stakeholder
deliberation
From MCM sessions to opening up deliberation

23. 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

24. Rafols, Porter and Leydesdorff (2010)
Diversity in Scientometrics
Scientometrics of disciplinarity & directionality in research & innovation

25. 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

27. 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

28. rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out

29. uncertainties by option
rich body of background data
concerning ‘framings’ of options by perspectives
MCM: what comes out
A
B
C
D

30. 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

31. 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

32. 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 perspectivesscores for particular issues
A
B
C
D
economics
health
environment
equity
academics
industry
government
NGOs

33. 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 perspectivesscores for particular issues
A
B
C
D
economics
health
environment
equity
academics
industry
government
NGOs

34. improved services
altruistic donation
presumed consent
xenotransplantation
embryonic stem cells
healthier living
low performance high
MCM Results: an example from health policy

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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