There is always the need to quantify uncertainty in decision research.

1. Where are the high information values?
Keith Shepherd & Doug Hubbard
Nov 2012
Vagen

2. Water, Land & Ecosystems (WLE)
Organizing research around a conceptual framework of basins
and landscapes

3. WLE Decisions
Our strategic objectives = our system level
outcomes:
(i) decrease food insecurity(ii) manage
environmental resources(iii) reduce poverty
among farmers(iv) increase nutrition, health
and wellbeing
We aim to improve stakeholder decisions on
policies, intervention programmes and
intervention designs through research
What information has high value for
improving decisions to achieve these
outcomes?
to achieve these outcomes?

4. •How to prioritize research under uncertainty
Which interventions will reduce risk, increase security, and
improve lives the most? What are the trade-offs between
competing objectives, like agricultural productivity and the
environment? What are the risks of intervention failure?How to
measure and monitor development outcomes
•Potentially huge investments in monitoring but not all metrics
will be of equal value to support intervention decisions. How
should we determine what data gathering costs are justified?
How to show the value of research
How can we show how the expense of research is justified by
better intervention decisions and improved outcomes?
• is justified by better intervention decisions and improved
outcomes?
Challenges Facing Researchers

5. •Development of systems to measure the impact of CGIAR
investments (of relevance to DFID as a significant funder) at the
level of the 4 system outcomes. Mechanisms to analyse the
impacts and trade-offs associated with sustainable
intensification at different scales (sub-national, national,
regional).Value for money metrics for measuring agriculture,
ecosystem and poverty and nutritional outcomes.
s.
Interests of donors

6. Why must quantify uncertainty
•Averages are wrong on average
•Uncertain events (floods, droughts,
erosion, market fluctuations)
•Security is a development outcome
(food/nutritional security; risk is the
complement of security)
•Value of information
Walsh

7. How much information do we need?
What defines whether information is unreasonably expensive?
What is the value of doing one more survey or experiment, or
creating another database?
Organizations often spend 10 times the value of information on
surveys and trials, etc
[Ron Howard]
We need a method to quantify information value

8. How to make preferences explicit
Objective trade-offs
•The trade-offs between productivity, ecosystem and welfare
outcomes
Valuation of outcomes (Preferences, Policy)
•Valuing one outcome relative to another (production vs
environment)
•Time (benefit now versus later)
•Uncertainty (risk aversion)
•Equity (increasing income of poor worth more than non-poor)
Making preferences explicit improves transparency and
multi-stakeholder decision processes

9. Applied Information Economics
Hubbard
Hubbard

10. © Hubbard Decision Research, 2012
Uses of Applied Information Economics
AIE was applied initially to IT business cases. But over the last 17 years it has
also been applied to other decision analysis problems in all areas of Business
Cases, Performance Metrics, Risk Analysis, and Portfolio Prioritization.
• Prioritizing IT portfoliosPrioritizing IT portfolios
• Risk of softwareRisk of software
developmentdevelopment
• Value of better informationValue of better information
• Value of better securityValue of better security
• Risk of obsolescence andRisk of obsolescence and
optimal technologyoptimal technology
upgradesupgrades
• Value of infrastructureValue of infrastructure
• Performance metrics for thePerformance metrics for the
business value ofbusiness value of
applicationsapplications
ITIT
• Risks of major engineeringRisks of major engineering
projectsprojects
• Risk of mine floodingRisk of mine flooding
EngineeringEngineering
• Movie / film project selectionMovie / film project selection
• New product developmentNew product development
• PharmaceuticalsPharmaceuticals
• Medical devicesMedical devices
• PublishingPublishing
BusinessBusiness Civilian GovernmentCivilian Government
• Environmental policyEnvironmental policy
• Procurement / auctionProcurement / auction
methodsmethods
• Grants managementGrants management
MilitaryMilitary
• Forecasting battlefield fuelForecasting battlefield fuel
consumptionconsumption
• Effectiveness of combatEffectiveness of combat
training to reduce roadsidetraining to reduce roadside
bomb / IED casualtiesbomb / IED casualties
• R&D portfoliosR&D portfolios
Payback is 20:1 to 300:1

11. The AIE Process
Identify important
metrics for monitoring
implementation
Improve the intervention
design to reduce chance
of negative outcomes
Hubbard
Forecasting intervention
impacts

12. Value of information
Game theory provided a formula for the economic value of
information over 60 years ago:
Expected Opportunity Loss = the chance of being wrong x
the cost of being wrong
Expected Value of Information is the reduction in the EOL as a
result of the additional information.
Expected Value of Information is the reduction in the EOL as a
result of the additional information.

13. AIE Empirical Evidence
• We are not as clear as we think on the decisions we are trying to influence
• Expressing uncertainty dissolves assumptions & allows all benefits, costs and
risks to be included, however intangible (especially environment!)
• We need calibrating to reliably estimate probability distributions
• There are usually only a few variables with high information value
• We are often measuring the variables that have least economic value
• And completely missing the ones that do have value (e.g. tend to measure
costs but ignore benefits, which are typically uncertain).
• Measurement is uncertainty reduction, not a gold standard
• Often need different data than we think
• Often need less data than we think
• Even small reductions in uncertainty can have considerable value

14. © Hubbard Decision Research, 2012
Safe Drinking Water Information System
• The EPA needed to compute the ROI of the Safe Drinking Water Information
System (SDWIS)
• As with any AIE project, we built a spreadsheet model that connected the
expected effects of the system to relevant impacts – in this case public health
and its economic value

15. Input sheet
Need for calibration
training

16. Cash flow page

17. Risk report page

18. Cost-effective Measurement
• Fermi decomposition
Estimate no. of piano tuners in Chicago
= No. households (population/people per household)
x % of households with tuned pianos
x tuning frequency per year / (tunings per day x
work days per year
• Secondary research - measured before? Historic data
• Observation - sampling, tracers, experiment

19. Value of Information
A Probability Management System
Decision modelling defines the metrics
Smart data - Smart decisions

20. Quantifying WLE Intervention Outcomes

21. Time value preference
Years
Environmentally rational
Poor farmer

22. Next steps
Phase 1
•Analysis of 4 - 6 WLE intervention categories/cases in 2013
•The outcomes define the agro-ecosystem metrics databases
•Decision Analyst
Phase 2
•Standardized databases and stochastic libraries
•Generic intervention screening model (triage method)
Phase 3
•Develop intervention decision modelling platform (linked
stochastic libraries, visualization tools)
•Analysis of WLE or CGIAR project portfolio

23. Smart data - Smart decisions