Uncertainty Assumptions are the quantitative assumptions in a strategy hypothesis, defined as a range of possible values rather than a single number. In BRI's three-part assumption taxonomy (choices, assertions, and uncertainties), they are the quantitative type: what you cannot yet pin down about factors outside your control, carried explicitly as ranges.
Uncertainty Assumptions are one of the three assumption types behind a strategy hypothesis: choices you control, assertions about how other actors will behave, and uncertainties you carry as ranges. As the quantitative type, they are where modeling a single point estimate manufactures false precision. Expressing each as a range, for example the span between the 10th and 90th percentiles of a distribution, preserves the real state of knowledge and lets the model show a spread of outcomes rather than one deceptively exact number. For how BRI carries uncertainty through its models, see the Modeling Uncertainty page at /supporting/uncertainty. Related Terminology Index entries: Strategy Choice Assumptions; Monte Carlo Simulation; Evidence Level.
The discipline of treating plan inputs as explicit, testable assumptions rather than forecasts traces to Rita McGrath and Ian MacMillan's Discovery-Driven Planning (1995). The case for carrying uncertainty as ranges rather than averages is made forcefully in Sam Savage's The Flaw of Averages (2009). BRI's refinement is to separate uncertainty from choice inside a single Strategy Hypothesis Model and to pair every uncertainty assumption with an evidence level, so teams can see which unknowns most affect the outcome and prioritize evidence gathering accordingly. Growth Forge® Software puts this to work: the modeling tools capture ranged assumptions, run Monte Carlo simulation across them, and surface the highest-impact uncertainties for testing.
