The basics of quantifying
qualitative scenarios
By Gerald Harris
Author, The Art of Quantum
Planning
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
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.
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.)
From qualitative drivers to quantitative
measures (examples)
Driver
Possible proxy variables
Economic
Change
Interest rates, rates of inflation, GDP growth, growth in
sales, wage rates, etc.
Social Change
Demographic 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.
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
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