Tools for Knowledge Elicitation

1. Tools for Knowledge Elicitation CfPM 19th September 2012 KnETs Sukaina Bharwani and Michael D. Fischer

2. Key features • Look at the multiple stresses that make up vulnerability, providing insights in the data collected and possibly new questions and areas of enquiry • Provide some robustness to our fieldwork including verification and validation of knowledge • Access tacit knowledge, which is quite difficult otherwise • Provide a way to formalize qualitative knowledge for use in more quantitative models. Formalisation of field data using these methods is being planned in Cameroon in January on the basis of a detailed baseline vulnerability assessment carried out last year  ABM

3. Knowledge space exploration • Sub stages involved in the process • Knowledge elicitation can be a big bottleneck in the research process • KnETs are tools which can automate parts of this process

4. Rapid prototyping (Stage 2) • Interactive questionnaire • Identify salient aspects of knowledge domain

5. Automatic rule creation (Stages 2 and 3 combined)

6. Rule induction program(Stage 3 – now automated) • The output of the data mining program creates decision trees of the form: if ( ATT1 >=3.0 then if ( ATT2 >=8.0 then ON ) ELSE OFF • This is then interpreted by referring back to the mapping file: If (weather = warm) and If (economy = poor overseas production) then Grow vegetables

7. Accessing tacit knowledge(Stage 4) “Is Soil type light?” Yes “Is the climate warmer?” Yes “Is overseas production poor?” Yes “Under these conditions, I suggest growing Vegetables” “Am I correct? (yes or no)” No “What is the crop?” Oilseed “What question when answered yes will distinguish Oilseed from Vegetables?” Is the strength of the US Dollar strong? “Now I can guess Oilseed” “Try again? (yes or no)”

8. Learning program • Stakeholders participate in pruning and refining resulting decision trees using a ‘learning’ program

9. Pilot examples - Limpopo, South Africa

10. Irrigation and farming in the UK

11. Floating fisherman in the Mekong

12. Ukrainian Tisza river basin Framing conditions

13. Objectives of the Tisza study • To apply and test the KnETs methodology in the Ukrainian part of Tisza river basin. • To explore the determinants of decision making on flood protection issues in the context of climate change and the uncertainty associated with it. • To investigate the potential of introducing ‘soft’ measures in a more or less ‘technically’ dominated flood protection system.

14. The game

15. The rules

16. Decision-making heuristics

17. Conclusions • The application of the KnETs game methodology revealed the salient criteria and thresholds of decision-making by municipal representatives concerning ‘soft’ mitigation decision pathways in flood risk management. • The resulting production rules shed light on what knowledge is used for decision-making and how different criteria are prioritised in these choices. • At present, these areas are still highly dependent on individual households, social networks and the Church for support and therefore reinforcing these institutions would also provide greater stability and security for these communities in times of ‘high’ risk. This would appear to be a high priority for local government. • What is most striking is that adaptation planning is not neglected due to a lack of knowledge of adaptation strategies but rather due to a lack of institutional and financial capacity to undertake these options to their maximum benefit. • Ideally these needs could be addressed together and draw on the current strengths of the community. For example, the use of social networks, the Church and innovative information communication technologies could be drawn together to design a community-based flood early warning system.

18. Incorporation of WEAP with KnETs

19. Example of a tree

20. Example of a vine • If you recognize a vine rather than a tree resulting from your game there are several things you can do. • This requires some thinking and re-shuffling of the decision trees rules you have so far using pen and paper.

21. Turning a vine into a decision tree • Generalize some of the criteria, i.e. put similar criteria together to eliminate redundancy (Gladwin 1989). • Cluster the decision criteria logically in an order that is consistent with the decision-making of the stakeholder e.g. first criteria that ‘enable’ the decision, and then the ordering aspect that is ‘maximized’ in the decision. This means that all possible constraints are passed before the decision is taken. For example, in a tree that quickly proceeds to ‘fish’ or ‘don’t fish’, these emic criteria or constraints may be obscured (ibid.). • Identify and eliminate decision criteria that belong in a logically prior (or later) decision, and put them in another tree (ibid.). Vines often occur instead of trees because the modeler does not realize that s/he has a series of trees to model not just one (ibid.).

22. Next steps Controversy mapping Possible use of ‘Elimination by Aspects’ (Gladwin, 1976) methodology to make the process more generic and applicable in a range of socio-cultural and geographic contexts. Application in Cameroon as basis for agent-based model

23. Mapping drivers to rules

24. Mapping drivers to rules

25. Alluvial graph

26. Network graph

27. Stream graph

28. Thank you  Feedback on method and/or collaboration in further case studies welcome!