1. The basics of quantifying qualitative scenarios By Gerald Harris Author, The Art of Quantum Planning

2. The essentials The whole scenario cannot be “quantified,” only some aspects of it The key qualitative drivers can have several quantitative proxies The mix of the proxies is where the real thinking is, as well as where important factors may be missed Comparisons across the scenarios is where a lot of learning takes place Models using historical data are sometimes a good place to start, however changing conditions in the scenarios often break the old models

3. Whole scenario cannot be “quantified” A good scenario combines multiple drivers, events and logical twists. Trying to capture it all will lead to paralysis through analysis and a low return on investment. Focus on the big drivers of movement or change in a scenario which pinpoint risks and opportunities. They are generally the most interesting aspects to try to lend some quantitative assessment. Those factors which might have an impact on the central question, uncertainties and decisions which initially called for scenario analysis are areas to focus on.

4. From qualitative drivers to quantitative measures Qualitative drivers (changes in economic, social, technological, environmental, cultural issues) can mirrored in quantitative measures (or variables) that give a sense of the direction. It is smart to initially brainstorm as many proxies for those qualitative drivers as a starting point, and not depend on existing models because they are there. (The assumptions behind old models is a scenario). Accessibility to historical data and the ability to capture real time data can influence the selection of variables and be a limitation. (Not forgetting the limitations is key to preventing strategic errors.)

5. From qualitative drivers to quantitative measures (examples) DriverPossible proxy variables Economic Change Interest rates, rates of inflation, GDP growth, growth in sales, wage rates, etc. Social ChangeDemographic shifts, voting patterns, levels of integration, rates of intermarriage, surveys on issues, TV watching patterns, etc. Technology Change Rates of obsolescence in products, introduction of new features, rates of productivity increase or costs declines, etc. Environmental Change Decline in species, global temperature change, frequency of extreme weather conditions, rates of illness from toxics, etc.

6. Comparisons across the scenarios The first step is to capture all the differences at points where it matters– assess the magnitude of the differences and why they exists Try to explain why individual differences are there and their potential effects Look at the differences in term of how they might impact different interests or stakeholders Look at the key differences in terms of how they would impact the focus question and key decisions driving the scenario analysis Compare affects on strategic options and their implementation

7. Some key points on modeling Creating simple models for learning and Illustrative purposes can be very useful Cause and effect are very hard to determine in a complex system (correlation is not causation) History does not predict the future so models built on historical data (and even deviations from them) present significant risks (of being wrong) Forecasting models are not predictors of actual events or conditions; even if they “get it right,” it is a lucky guess likely to be wrong in the future