# **1 Introduction**

Each year governments invest billions of dollars towards development. Many of these investments – in energy, land use, transportation, and other sectors – are long-term and will shape the course of development in their countries. Yet we live in an unpredictable world governed by competing beliefs and preferences. Our decisions are engulfed in _deep uncertainty_ about the long-term cost of energy inputs, the impact of climate change, and a host of other factors that shape our decisions. Deep uncertainty occurs when the parties to a decision do not know—or do not agree on— the likelihood of future events, the best model for relating actions to outcomes, or the value of potential outcomes (Lempert et al., 2003).

Deep uncertainties pose formidable challenges to making near-term decisions that make long-term sense. Governments grapple with deep uncertainties daily. Traditionally, they have asked, _“Which investment option best meets our goals given our beliefs about the future?”_ Such approaches, sometimes called “Predict-then-Act”, hinge on our accurately predicting and then reaching consensus on what the future will bring (Hallegatte et al., 2012; Lempert and Kalra, 2011). But disagreements about the future can lead to gridlock. Worse, investments tailored to one set of assumptions about a deeply uncertain future often prove inadequate or even harmful if another future comes to pass. Governments and international development organizations increasingly recognize that current approaches to decision making struggle to meet these ubiquitous challenges (Rennkamp, 2012; Ranger, 2013; IEG, 2010, 2012; The World Bank, 2013).

For instance, throughout the World Bank, there is a growing recognition that deep uncertainties need to be better managed in decision making (Hallegatte et al., 2012; IEG, 2010; Independent Evaluation Group (IEG), 2012). In 2010, The World Bank’s Independent Evaluation Group released a report pressing World Bank staff to conduct more rigorous economic analyses of investment options (IEG, 2010) and to use these analyses to inform decisions. This was endorsed by the World Bank’s Board of Directors in a Guidance Note to project managers (The World Bank, 2013). More recently, a more explicit consideration of risk and uncertainty is also one of the key messages of the World Development Report 2014 (The World Bank, 2014).

Many methods have been developed over the last half-century to help decision makers manage deep uncertainties and make investments that are robust to the unpredictable future. These approaches ask, _“Which investment option best meets our goals given that we cannot know what the future will bring?”_ (Hallegatte et al., 2012). These methods seek to identify _robust_ decisions – those that satisfy decision makers’ objectives in many plausible futures, rather than being optimal in any single best estimate of the future (Lempert et al., 2013). They have been recommended for investment lending but are not widely used in practice, leaving many decisions vulnerable to surprise. One reason may be that we do not understand well the extent to which deep uncertainties affect lending decisions, or know the usefulness and practicality of these methods for investment lending.

We seek to help bridge this gap. In this study, first we review the economic analysis of ten World Bank projects approved between 2002 and 2011 in order to understand how they managed risk and uncertainty. Then, we test and demonstrate the practicality and valueadded of new methods for long-term infrastructural investment lending decisions. Research suggests that having practical tools to solve a problem can increase one’s awareness of the 2