Scenario Modeling
as a structured discipline
Scenario modeling sits between data analysis and strategic planning — a method for building plausible futures from incomplete information. Tukameslov has developed this curriculum since 2019, distilling practical techniques used across regional institutions and independent practitioners.
What the program covers
Four thematic pillars, each building on the previous. Participants move at their own pace within each track.
Foundations of Scenario Logic
How scenarios differ from forecasts and predictions. Participants learn to identify driving forces, uncertainties, and the structural relationships between them before building any model.
Variable Mapping and Dependencies
Mapping the variables that shape an outcome and tracing their interdependencies. The module uses causal loop diagrams and cross-impact matrices as primary analytical tools.
Model Testing Under Constraints
Stress-testing constructed scenarios against resource limits, time horizons, and competing priorities. Participants work with real-world cases sourced from public institutional data.
Communicating Scenario Outputs
Translating model outputs into structured reports, briefings, and decision-support documents. Emphasis is on clarity for audiences with different levels of technical familiarity.
"Practitioners often skip variable mapping and move straight to outcomes. That shortcut produces models that look complete but break under the first unexpected input."
Reginald Obukhov — Lead Program Author
The curriculum was designed after reviewing common gaps in how scenario analysis is taught across regional institutions. Rather than starting with software or frameworks, participants begin with logical structure — learning to ask which variables actually change the outcome before touching any modelling tool.
Approaches and methods used →
Saoirse Valentin
Completed the advanced track after working through the core program. Found the constraint-testing module most applicable to her institutional planning work.