CfPM 19th September 2012
Tools for
Knowledge
Elicitation
KnETs
Sukaina Bharwani and Michael D. Fischer
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

Access tacit knowledge,
which is quite difficult otherwise

Provide a way to formalize
qualitative knowledge for use in
more quantitative models.
Provide some robustness to
our fieldwork including
verification and validation of
knowledge
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
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
Rapid prototyping
(Stage 2)
 Interactive
questionnaire
 Identify salient
aspects of
knowledge
domain
Automatic rule creation
(Stages 2 and 3 combined)
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
Accessing tacit knowledge
(Stage 4)
“Is Soil type light?”
“What is the crop?”
Yes
Oilseed
“Is the climate warmer?”
“What question when
answered yes will
distinguish Oilseed from
Vegetables?”
Yes
“Is overseas production
poor?”
Yes
“Under these conditions, I
suggest growing
Vegetables”
“Am I correct? (yes or no)”
No
Is the strength of the US
Dollar strong?
“Now I can guess Oilseed”
“Try again? (yes or no)”
Learning program
 Stakeholders participate in
pruning and refining resulting
decision trees using a ‘learning’
program
Pilot examples - Limpopo, South Africa
Irrigation and farming in the
UK
Floating fisherman in the Mekong
New Approaches to Adaptive Water Management under Uncertainty
Ukrainian Tisza river basin
Framing conditions
New Approaches to Adaptive Water Management under Uncertainty
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.
New Approaches to Adaptive Water Management under Uncertainty
The game
New Approaches to Adaptive Water Management under Uncertainty
The rules
New Approaches to Adaptive Water Management under Uncertainty
Decision-making heuristics
New Approaches to Adaptive Water Management under Uncertainty
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 decisionmaking 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.
Incorporation of WEAP with KnETs
Example of a tree
Forecast –
normal
rainfall
N
Y
Forecast –
above normal
rainfall
Priority – crops
in daily home
use
N
Y
N
Y
Forecast –
below normal
rainfall
Y
Grow Corn
N
Low/medium
market demand
N
Y
Rain comes
in
September
Grow
Sunflower
Grow Rapeseed
(i.e. high market
demand)
Y
Grow Soya
N
Grow Rapeseed
(i.e. rain comes
in January)
Can you sell
fish?
Y
Example of a vine
N
Is fish catch
good?
Y
Do fishing
N
Is there
access to
fishing
grounds?
 If you recognize a vine rather
N
Is capital low/
debt to
middleman
high?
Do farming
N
Y
than a tree resulting from your
game there are several things
you can do.
Y
Is there a
market for
fish?
Do farming
Y
N
Y
Do farming
Do you have
good fishing
equipment?
Continue
fishing
N
re-shuffling of the decision trees
rules you have so far using pen
and paper.
Are you in
good health/
can you
fish?
Y
 This requires some thinking and
Do farming
N
Stop fishing
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.).
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 agentbased model
Mapping drivers to rules
Mapping drivers to rules
Alluvial graph
Network graph
Stream graph
Thank you 
Feedback on method
and/or collaboration in
further case studies
welcome!