In developing research for impact, science should support decisions as decision makers are always hungry for information. Research should be tailored to specifically address particular decisions. This can be achived through direct engagement with decison makers especially in decision making under uncertainity. Tailor research specifically to address particular decisions

1. Decision analysis
Eike Luedeling, Keith Shepherd

2. Research for impact
Most science never supports any
decisions, even though decision-makers
are hungry for information.
http://www.mynamesnotmommy.com
Most research does not answer
questions that are critical for decisions,
or it is not readily available when it is
needed.
Tailor research specifically to
address particular decisions

3. Decision making under uncertainty
Identify all uncertainties in the
decision of interest
Make probabilistic projections of
likely decision outcomes
http://www.relationshipeconomics.net
Engage directly with decision
makers
This is the core of WLE’s decision
analysis procedures
Identify uncertain variables with
high ‘information values’. These
are priorities for measurements

4. WLE’s decision analysis approach
Optimize the decision
Use preferences of decision makers to determine best decision
YesIs there significant value to
more information?
Yes
No
Compute the value of additional information
Determine where and how much measurement effort is needed
Model the current state of uncertainty of all relevant factors
Initially use calibrated estimates and then actual measurements
Define the decision(s) – Identify relevant variables
Set up the ‘Business Case’ for the decision.
Measure where the
information value is
high – Reduce
uncertainty using
proven empirical
methods

5. Outputs
Replenishment
Irrigation growth
Initial irrigated area
Water use per hectare
Aquifer size
Natural water use
Importance threshold
Identification of high-value
variables
Probabilistic impact
projections
Aquifer size after 70 years of
abstraction (% of original)

6. Advantages and applications
• Inclusion of uncertain variables allows truly
holistic impact assessments
• High information value variables are almost
always not those typically measured
• Probabilistic impact projections can often provide
sufficient certainty about decision outcomes
• Possible applications include:
– Quantitative and probabilistic impact pathways
– Ex-ante impact projections
– Counterfactuals for impact assessments
– Definition of priority variables for impact monitoring

7. Thanks for your attention!
Eike Luedeling
(e.luedeling@cgiar.org)