Knowledge Elicitation Tools Knowledge Elicitation Tools (KnETs)(KnETs)

Sukaina Bharwani, (School of Geography and the Environment, University of Oxford / Stockholm

Environment Institute), Michael D. Fischer (Centre for Social Anthropology and Computing), Thomas E.

Downing (SEI), Gina Ziervogel (University of Cape Town)

Key featuresKey features

Formalisation of field data using these innovative methods has been started in 2 sites so far (East Kent, UK and Limpopo Province, South Africa).

These innovative methods for knowledge engineering allow the construction of rules and heuristics used by stakeholders to:a) provide new questions and insights in the data collected and/orb) to broach the realm of tacit knowledge, or that which people find hard to articulate, which difficult to elicit by other methodsc) include in an agent based model

d) to provide a means to integrated qualitative and quantitative models in a way which has failed in the past due to the failure to understand the fundamental differences between them.

Knowledge engineeringKnowledge engineering

•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

Fieldw ork(interview s, focus

groups, etc.)

Interactivequestionnaire -

design inform ed byStage 1

M achine learningalgorithm createsheuristics using

data from thequestionnaire

Learning DecisionTree program -

expands/prunes/refines existingdecision trees

Know ledgeRepresentation -

decision trees/rules

Choices m adeby stakeholders

are recorded

Identification of salientdom ains, drivers and

strategy choicesTesting w ith

stakeholder input

Stage 1 Stage 2 Stage 4Stage 3

Rapid prototypingRapid prototyping

•Interactive questionnaire

•Identify salient aspects of knowledge domain

•Social, environmental and economic

•Goal – crop and strategic adaptation

Rule induction programRule induction program

•Rule induction algorithm creates rules based on data from questionnaire

Learning programLearning program

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

Funding Body: Tyndall Centre for Climate Change Research

School of Geography and the Environment, Oxford University

Stockholm Environment Institute, Oxford, UK

Climate Outlooks and Agent-Climate Outlooks and Agent-Based Simulation of Adaptation in Based Simulation of Adaptation in

Africa (CLOUD)Africa (CLOUD)

OverviewOverview

Case study: Mangondi– Community garden and individual profiles– Adaptation strategies– Ability to adapt to climate variability will

improve ability to adapt to climate change• Agent-based social simulation model

• Climate scenarios (Seasonal to decadal)

• Farmer adaptation (Based on fieldwork)

• Crop models (FAO CropWat)

CLOUD...CLOUD...

Perfect vs. imperfect farmer decision-responsesAnalysis of adaptive capacityVariable skill levels of the forecastsPotential strategies not currently used (e.g.

experimentation with a market crop)

FieldworkFieldwork

Survey Interviews using KnETs methods – salient features

Forecast useIrrigation reliabilityMarket demand (More likely to trust dry forecast than wet as marketing farmers)

Options – crop and strategic adaptation Perceptions are often different from reality and anchored in the memory of past

extremes

Adaptive capacityAdaptive capacity

Strategies: Change crops (e.g. poor opting for market variety) Change planting date of existing crop Increase/decrease area

Crops: Maize - subsistence crop kept for 2 years in storage Butternut - seasonal crop for Christmas market Cabbage - market crop

It appears that wealthier farmers adapt more to the market (more cautious of trusting the forecast) and poorer farmers adapt more to climate signals (forecast helps to support their choices since irrigation is unreliable)

Access to waterAccess to water

Irrigation and distance from pump is importantVisible use of drought resistant planting regimes based on experiences of access to waterPerceptions of water allocation are not homogeneous

Possibility of planting the same crop on several plots to market more strategically.

However, planting rows of multiple crops spreads their risk and they recognise it as a ‘safer’ strategy.

Could WEAP be used to compare the water usage of these two options?

Social networksSocial networks

Different agent types and degree of heterogeneity? - Poor and better-off farmersWhich agents are influential and what is the flow of information? - Poor will be influenced by all peers, but average farmers will on be influenced by other better-off farmers

Model…Model…

ExperimentsExperiments

Experiment A: Seasonal forecasts are not used but the community is still impacted on by climate: control experiment

– No memory, no forecast. Agents assuming this year will be like last year.

Experiment B: With seasonal forecasts (how vulnerable are farmers to climate change when using seasonal forecasts, compared to A?)

– Forecast, no memory = Use of forecast with no biased perception.

Experiment C: With climate change but not seasonal forecast: climate changes and agents learn through experience of changing climate.

– Memory, no forecast. Agents assuming this year will be based on last 5 years.

Experiment D: With climate change but with seasonal forecasts.– Forecast and memory.

Computers in the field!Computers in the field!