WLE Information Systems Strategic Research AC 5.1 Connecting information to development decisions A systematic approach to analyzing intervention decisions.

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Presentation transcript:

WLE Information Systems Strategic Research AC 5.1 Connecting information to development decisions A systematic approach to analyzing intervention decisions under uncertainty to identify high value information: What CGIAR research and information products will have highest value in supporting intervention investment decisions? What metrics should be monitored to assess whether interventions are on track and achieve intended development outcomes? AC 5.2 Measuring agro-ecosystem health New data & information systems that address high value information needs of development decisions. Forecasting and monitoring the chain of variables along the intervention impact pathway. How to measure ‘sustainability’, ‘resilience’, trade-offs for specific decisions

What research investments will have most development impact? Which interventions will reduce risk, increase security, and improve lives the most? How to measure and monitor development outcomes? How to assess trade-offs between agricultural productivity and the environment? What are the risks of intervention failure? What is high value information for improving intervention decisions?

Process 103 monitoring initiatives screened. 24 initiatives evaluated against 34 criteria Monitoring experience in other fields -Public health surveillance -Systems thinking in industry and public services -Decision sciences Feedback from expert group

New thinking Little evidence for impact of monitoring systems on real-world decision making/management  Define decisions before measurements Information has no value unless it has the potential to change a decision (Ron Howard) The measurement inversion − most measurement effort in business cases is spent on variables that have the least information value Applied Information Economics: Building intervention business cases with uncertainties quantified Value of Information Analysis Smart data – Smart decisions

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!) 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 We are spending more on measurements than we need to (even small reductions in uncertainty can have considerable value) Hubbard, 2010

Decisions before measurement Why are you measuring? What decisions do you expect to be taken differently? What behaviour would you expect to change? The choice of which metrics to track should result from modelling decisions / forecasting intervention impacts The more difficult and complicated problems, the more important it is to get your understanding down in quantitative terms.

Intervention Decision Model

On-going cases Irrigation development in SSA Rainfed productivity in SSA Resource reuse and recycling Water variability management in basins PES in Sasumua Catchment Global biodiversity information system

New invited initiatives Stakeholder consultation on data needs for agriculture in Africa. Results to be presented to International Conference on Open Data for Agriculture, April 2013 (DFID-commissioned) Value of Information Analysis for Smallholder Farm Decision Making - linked to Vital Signs (Gates Foundation) ISPC Workshop on CGIAR System-Level Outcome Impact Pathways and Linkages (White paper)