Modeling resilience with applied information economics (AIE)
Douglas Hubbarda, Matthew Millar. (30/11/2014). Modeling resilience with applied information economics (AIE). Nairobi, Kenya: International Livestock Research Institute (ILRI).
We created a probabilistic decision analysis tool to model the issue of resilience in the Horn of Africa through a cooperative effort between the Technical Consortium for Building Resilience in the Horn of Africa (TC) and Hubbard Decision Research (HDR). The work was carried out under the guidance of Katie Downie from the International Livestock Research Institute (ILRI). The objective was to provide a modeling framework to provide guidance for what should be measured to best support future decisions related to household and community resilience in the Horn of Africa. The quantitative methods used are supported by published research showing how these methods provide a measurable improvement on expert decisions done without the aid of such models. The process we use for improving decision quality is based on a probabilistic risk return analysis called Applied Information Economics, which uses Monte Carlo simulations to produce a distribution of potential outcomes. This method allows the potential stakeholder to consider uncertainty explicitly and to calculate the risk of a negative outcome or loss. Another primary output of an Applied Information Economics model is the calculation of the economic value of information for each uncertain variable. By collecting information values for interventions related to resilience, we can identify priorities for research and data collection related to investments in promoting resilience. Preparations for the project were started in June 2013, followed by a July workshop in Nairobi. The workshop included training on the AIE method including “calibrating” all workshop attendees. From the workshop we also selected a core group to work on the pilot resilience model. The group met (remotely) ten times between September and January, 2014 – two meetings to define the decision and pick the pilot project, six meetings for modeling and estimation, and two meetings for reviewing results of the model and discussing recommendations. This report contains a summary of our effort, gives an overview of the pilot project, and presents modeling results. We conclude with specific recommendation of next steps for reducing uncertainty on the project in question, as well as suggested course of action based on our findings.