Steps methods #8 mcm
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This document provides an overview of the steps involved in conducting a Multicriteria Mapping (MCM) analysis. It describes choosing options and defining criteria, assessing scores, exploring uncertainties, assigning weights, and considering overall rankings to compare options. The outcomes of MCM include qualitative data on stakeholder perspectives and framings of options, as well as quantitative data like scores, weights, and rankings. Examples of MCM results are shown for health policy options and agricultural strategies regarding genetically modified foods.
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Steps methods #8 mcm
- 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)
- 6. choose options MCM: what goes in define criteria
- 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
- 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
- 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