Steps methods #8 mcm

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Steps methods #8 mcm

  1. 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. 2. An Example: Multicriteria Mapping (MCM)
  3. 3. choose options MCM: what goes in
  4. 4. choose options MCM: what goes in
  5. 5. MCM: choosing options
  6. 6. choose options MCM: what goes in define criteria
  7. 7. MCM: defining criteria
  8. 8. choose options define criteria assess scores MCM: what goes in
  9. 9. choose options define criteria assess scores option 1 option 2 option 3 option 4 performance CRITERION A MCM: what goes in
  10. 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. 11. choose options define criteria assess scores explore uncertainty A B C MCM: what goes in
  12. 12. MCM: assessing scores
  13. 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. 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. 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. 16. MCM: assigning weights http://mcm.dabdev.net/projects/ec4cc0623c5f4bf09acd1febec66e5f6/engage/engagements/eadd5a2094cf419d86cd4dd99223f03e/#weights
  17. 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. 18. O C S U W R  From MCM sessions to opening up deliberation
  19. 19.  From MCM sessions to opening up deliberation
  20. 20.    From MCM sessions to opening up deliberation
  21. 21.      group-based elicitation or deliberation From MCM sessions to opening up deliberation
  22. 22.               wider stakeholder deliberation From MCM sessions to opening up deliberation
  23. 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. 24. Rafols, Porter and Leydesdorff (2010) Diversity in Scientometrics Scientometrics of disciplinarity & directionality in research & innovation
  25. 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
  26. 26. 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
  27. 27. rich body of background data concerning ‘framings’ of options by perspectives MCM: what comes out
  28. 28. uncertainties by option rich body of background data concerning ‘framings’ of options by perspectives MCM: what comes out A B C D
  29. 29. 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
  30. 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 scores for particular issues A B C D
  31. 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 weights by issue for perspectivesscores for particular issues A B C D economics health environment equity academics industry government NGOs
  32. 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. 33. improved services altruistic donation presumed consent xenotransplantation embryonic stem cells healthier living low performance high MCM Results: an example from health policy
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 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 GOVERNMENT organic environmental intensive GM + labelling GM + monitoring GM + voluntary controls low performance high MCM Results: an example from GM food
  39. 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 INDUSTRY organic environmental intensive GM + labelling GM + monitoring GM + voluntary controls low performance high MCM Results: an example from GM food
  40. 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 PUBLIC INTEREST low performance high MCM Results: an example from GM food
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