This presentation is based on a work co-authored by Laura Bonzanigo & Nidhi Kalra (2014).

Countries invest billions of dollars annually in long-term projects. Yet deep uncertainties pose formidable challenges to making near-term decisions that make long-term sense. Methods that identify robust decisions have been recommended for investment lending but are not widely used. We seek to help bridge this gap and, with a demonstration, motivate and equip analysts to better manage uncertainty in investment decisions. We first review the economic analysis of ten World Bank projects. We find that analysts seek to manage uncertainty but use traditional approaches that do not evaluate options over the full range of possible futures. Second, we apply a different approach, Robust Decision Making, to the economic analysis of a 2006 World Bank project, the Electricity Generation Rehabilitation and Restructuring Project, which sought to improve Turkey’s energy security. Our analysis shows that RDM can help decision makers answer specific and useful questions:

How do options perform across a wide range of potential future conditions? Under what specific conditions does the leading option fail to meet decision makers’ goals? Are those conditions sufficiently likely that decision makers should choose a different option? Such knowledge informs rather than replaces decision makers’ deliberations. It can help them systematically, rigorously, and transparently compare their options and select one that is robust. Moreover, we demonstrate that analysts can apply RDM using with the same data and models typically used in economic analyses. Finally, we discuss challenges to applying such methods and how they can be overcome.

This seminar has been jointly organized by CMCC and FEEM.