Uncertainty & Scenario Analysis Perspective

Since all decisions are choices about the future in the face of uncertainty (Walters, 1986), and a feasibility study is about informing decision-makers about the likely consequences of project/ development alternatives (Beanlands and Duinker, 1984), the analysis must by definition engage in thought processes that deal explicitly with the future (Duinker and Greig, 2007).

Futures analysis deals with much more than just the concept of forecasting or predicting the future. It encompasses a variety of techniques to create well- grounded menus of choices about the future by describing and studying alternative possibilities (Duinker and Greig, 2007). On the whole, the methods operate within the domain of three questions associated with the future (Rubin and Kaivo-oja, 1999):

3. Preferable futures – what would we prefer to happen?

The most common methods used for Futuring are – a) scanning, b) trend analysis, c) trend monitoring, d) trend projection, e) scenarios, f) polling, g) brainstorming, h) modelling, i) gaming, j) historical analysis, and k) visioning (Duinker and Greig, 2007).

The best known qualitative, structured futures method in use today is the Delphi method (Lang, 1998). A Delphi survey is a consensus based group process for systematically soliciting, collating, and refining a set of informed judgments on issues determined by a small number of variables. The technique usually consists of a set of sequential questionnaires. With each subsequent questionnaire, information and feedback from results of earlier questionnaires is provided, allowing a structured dialogue among experts. Delphi studies are more successful when they involve experts as opposed to the general population (Caldwell, 2003), but participant diversity is desirable to help reduce bias. Delphi works best when assessing options of relatively short-term futures (e.g., less than 5 years), and is best suited to exploring issues involving both social and scientific evidence.

Futurists frequently use scenarios to try to understand uncertainty and scope of possible alternatives. This understanding supports the creation of robust management strategies, to prepare managers to respond appropriately if their expectations of what is most likely prove false, and to provide insights into events that could indicate which path one is actually on (Duinker and Greig, 2007). A scenario analysis could therefore be used complimentary to the traditional feasibility analysis.

According to Porter (1985), a scenario is an internally consistent view of what the future might turn out to be – not a forecast, but one possible future outcome. Jake et al (1998) defined a scenario as a description of a possible set of events that might reasonably take place. Schwartz (1996) defined scenarios as a set of reasonably plausible but structurally different futures. There are several other definitions as well. The important commonality in these definitions is the idea that scenario-building does not focus on making predictions or forecasts, but

rather on describing images of the future that challenge current assumptions and broaden perspectives (Duinker and Greig, 2007). The main purpose of developing scenarios is to stimulate thinking about possible occurrences, assumptions relating these occurrences, possible opportunities and risks, and courses of action.

Scenarios usually serve one of two major functions – one is risk management, where scenarios enable strategies and decisions to be tested against possible futures, while the other is creativity and sparkling new ideas (Lang, 2001). Scenario planning attempts to compensate for two common errors in decision making – under prediction and over prediction of change; allowing for a middle ground between the two to be taken (Schoemaker, 1995). To address this issue, scenario analysis divides knowledge into three areas – things we believe we know something about (the ―Known‖), elements we consider uncertain (the ―Known unknown‖) and the unknowable (the ―Unknown unknown‖).

There are various approaches to developing scenarios (Schwartz, 1996; de Jouvenel, 2000; Godet, 2000; Masini and Vasquez, 2000; Wilson, 2000; Cornish, 2004). The most common contrasts in scenario-building include, back-casting vs. forecasting, descriptive vs. normative, quantitative vs. qualitative, and trend vs. peripheral (Greeuw et al., 2000). Both inductive and deductive methods can be used for developing the basic structure of scenarios. The inductive method is less structured and depends on the patience of a group of individuals to continue their discussions and reach a consensus. The deductive method, in contrast follows the steps described by Schwartz (1996) as well as ―intuitive logics‖ developed by Royal Dutch Shell, which are:

1. Define the topic/problem and focus of the scenario analysis 2. Identify the key factors/environmental influences on the topic 3. Identify critical uncertainties

4. Define scenario logics 5. Create scenarios

A primary objective of scenario-building is to push thinking in terms of length of time (e.g. beyond 5 to 10yrs) and breadth (e.g., across a range of possible futures). It should aid in understanding how the world could unfold, and how that understanding can be incorporated in decision making. Scenarios must therefore serve the purpose of augmenting understanding and informing good decisions (Kaivo-oja, 2001).

Instead of becoming attached to a single scenario as most likely and other hypothetical, analysts should rather seek to develop alternative scenarios that each represents plausible and possible futures. Each scenario must be rooted in the present, plausible (not impossible), and internally consistent (Ruben and Kaivo-oja, 1999).

The forecasting exercise should begin with assuming that significant contextual forces – e.g., markets, climate change and human demographics- are irreverent and hold firm in current patterns. A sensitivity analysis is then performed whereby uncertainties about parameters and relationships inside the forecasting models are systematically tested (Strafield and Bleloch, 1986). Model elements in which small changes cause large shifts in forecast need to be identified. It should be remembered that for most forecasts, the external forces will interact cumulatively with the proposed project and render the expected impacts smaller or larger, or of a different nature, depending on how the project and the contextual forces interact with each other.