Assessing resilience in the Horn of Africa - an applied information economics approach

The ability of communities in the drylands of Africa to adapt to or manage shocks and stresses while maintaining trajectories towards sustainable development, or "resilience", fea- tures prominently in the objectives of donors and development partners. This concept is, however, poorly understood, particularly when it comes to measuring the impact of invest- ments on resilience. Most de nitions of resilience in development scenarios hinge upon the response of social, ecological and economic systems to shocks and stressors. It is, however, extremely dicult to quantify this response, as it is impossible to observe the full range of possible disturbances, hence assessments of system resilience normally fall short of providing comprehensive evaluations. In this paper, we discuss the use of system models to overcome this constraint in the chroni- cally food insecure region of the Horn of Africa. As this method allows a simulation of system responses to the full range of plausible disturbances, it then becomes possible to produce long-term system performance projections that convey insights into system resilience. Com- plex systems are, however, dicult to model, and there are always substantial uncertainties about model parameters, model structure and consequently model outputs. We therefore propose a modeling approach that is adequate for dealing with multiple uncertainties and that produces assessments of system resilience that can be used in planning for development activities aimed at enhancing resilience. Our method draws on Applied Information Economics, a well-established approach to sup- porting business decisions under multiple uncertainties. Rather than aiming to specify system dynamics with high precision - the level of precision rarely justi ed by available information - this approach emphasizes the adequate framing of uncertainties with respect to data concern- ing input parameters, system processes and potentially even resilience de nitions. Estimates of con dence intervals for input variables are obtained from historic and current datasets, as well as solicited from calibrated experts. Probability distributions of all input variables are used for constructing high-level system models that are then subjected to Monte Carlo analysis. Outputs from this procedure are analyzed with data mining techniques to deter- mine which uncertainties constrain our ability to evaluate resilience and are thus priorities for further research

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