Assumptions

Any system that compares years, models future pressure, or explains tradeoffs has to assume something about the surrounding conditions. The problem is not that assumptions exist. The problem is when they are left invisible and mistaken for certainty.

A transparent model therefore treats assumptions as first-class inputs to interpretation rather than background noise.

Structural assumptions include things like filing status, account type, age-based rules, and whether current law is treated as continuing. These assumptions define the framework in which the explanation operates.

If one of those assumptions changes, the explanation may change in kind, not only in degree.

Illustrative assumptions are often used for scenario comparisons: estimated future growth, timing of withdrawals, or the order in which events are modeled. These assumptions are not claims about what will definitely happen. They are tools for making tradeoffs visible.

That distinction matters because people often over-read projections. The explanation becomes more useful when the user sees which parts are rule-driven and which parts are scenario-dependent.

When assumptions are visible, disagreement becomes productive. You can ask whether the structure is wrong or whether a specific assumption should change. Without that visibility, all disagreement collapses into whether the output feels believable.

Good explanatory systems are designed so the user can interrogate the assumptions rather than merely absorb the conclusion.