problem and motivation to solve it (Coombes and Devine, 2010; Kolb, 1984). Our hope is that a straightforward demonstration of these methods may motivate and equip analysts to better manage uncertainty in lending decisions.

In particular, we apply Robust Decision Making (RDM, Lempert et al., 2003) to the economic analysis of a prior World Bank project, the Electricity Generation Rehabilitation and Restructuring Project, which in 2006 sought to improve Turkey’s energy security in part by increasing near-term energy supply. We use the same data and models utilized in the original analysis, but in a different way. Rather than seeking to inform electricity investments in Turkey with predictions of the future, we seek to inform them with assessments of their robustness to an unpredictable future.

The original decision was to rehabilitate an existing coal plant. Other options included building new coal-fired, gas-fired, or other power plants. Decision makers were concerned with two key metrics: a) whether the investment passed a cost minimization metric – i.e., produced electricity at lower cost than any other option, and b) whether the investment passed a cost-benefit test, in this case had a rate of return of at least 12%. We evaluated each option according to these metrics in 500 plausible future states of the world that varied under seven different sources of uncertainty.

We used the results to answer a series of specific and useful questions:

- How do decision makers’ 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 that is more robust?

Our analysis suggests that methods like RDM can provide decision makers with much more salient information about the merits and vulnerabilities of different options. This can focus decision makers’ attention on the uncertainties that matter most to a decision. It can make them aware of the important trade-offs and of the actions they could take to reduce their vulnerability. Ultimately, it puts the decision back in the hands of decision makers by helping them take measured risks and be less vulnerable to surprise.

Importantly, the purpose of this exercise is not to prove the original project decision right or wrong or to re-create the analysis under today’s conditions. At the time of this project, for instance, climate change mitigation and the possibility of a price on carbon were not widespread concerns, and this analysis reflects the original priorities of decision makers related to energy security. The same analysis today would almost certainly include these considerations. Rather, our aim is to demonstrate that an analysis of robustness can be incorporated into projects’ standard economic analyses using the same data and models, and to examine the different kinds of information that emerge from a Predict-then-Act versus a robustness analysis. Moreover, while our analysis focuses on a World Bank case study, we believe this methodology holds value for the broader international lending community.

We believe methods like RDM can be readily incorporated into cost-benefit and other economic analyses that analysts perform every day, with few additional resources. We hope 3