Assumption Budget¶
Checklist — instantiates Complexity Budgeting
Caps and scores the load-bearing assumptions a model, plan, or forecast is allowed to rely on, so fragile premises must be retired or evidenced before more are added.
Most complexity budgets police the parts you can see — features, rules, dependencies. Assumption Budget polices the parts you can't: the unproven premises a model quietly rests on. It is a checklist that enumerates the load-bearing assumptions behind an analysis, scores each for fragility and leverage, and sets a hard ceiling on how many the work may carry. The one idea that makes it this mechanism and not a feature or scope budget is that it charges complexity against hidden premise load rather than visible surface: a model with three inputs can be far more fragile than one with thirty, if those three inputs are each a hope. When the budget is full, no new assumption may be added until an existing one is either retired, evidenced, or downgraded to a labelled scenario.
Example¶
Two founders are building a discounted-cash-flow model to raise a seed round. The spreadsheet looks disciplined, but it stands on a stack of premises: 5% month-over-month growth sustained for two years, churn holding at 2%, acquisition cost staying flat as they scale, and pricing power they have never tested. Each is defensible in isolation; together they are a tower. They apply an Assumption Budget capped at seven load-bearing premises. Each surviving assumption is tagged with a fragility score (how likely it is to break) and the evidence tier required to keep it (a cohort chart, a comparable, a signed pilot). When an investor asks them to also model "enterprise upsell at month eighteen," they are already at seven — so they must retire the weakest premise, or bump upsell into a clearly-labelled optimistic scenario rather than the base case. The output is not a fancier model but a smaller, honest one: a short list of testable bets a reader can actually interrogate, instead of a spreadsheet whose confidence is manufactured by its own moving parts.
How it works¶
- Enumerate the premises. Force every implicit "we assume…" into an explicit line item; the dangerous ones are usually unstated.
- Score each on fragility × leverage. How likely is it to be wrong, and how much of the conclusion collapses if it is? High-fragility, high-leverage premises dominate the budget.
- Set the cap. A ceiling on the count, or on total scored fragility, appropriate to the stage — a napkin projection may carry more than a board-approved plan.
- Make additions displace. A new premise must earn its slot by out-scoring one already in the budget, or by arriving with the evidence tier that moves it below the fragility line.
Tuning parameters¶
- Budget size — how many load-bearing premises are allowed. Tighter forces sharper models but can push real uncertainty into hidden corners; looser tolerates nuance but invites premise sprawl.
- Fragility scale — coarse (high/medium/low) versus a graded score. Finer scoring ranks premises better but invites false precision about things that are, by definition, unverified.
- Evidence tier — how strong the proof must be to keep a fragile premise on the books. Raising it retires weak assumptions faster but slows the model down.
- Assumption-vs-scenario boundary — what counts as a premise the model relies on versus a scenario it merely explores. Moving the line changes what the budget has to charge for.
When it helps, and when it misleads¶
Its strength is that it drags the invisible into the open: the premise stack that makes a plan fragile is exactly the part that never appears on the org chart of the model. By capping it, the budget turns "this looks sophisticated" into "this rests on seven bets, here they are." Its central failure mode is that it can only charge for premises someone names, and the most dangerous assumptions are the unstated ones — the background conditions nobody thought to question. A plan can pass the budget and still be brittle, because brittleness lives in the premises no one scored.[n1] The classic misuse is relabelling: quietly promoting a shaky assumption to a "fact" so it drops off the count, which reduces the tally while leaving the risk intact. The guarding discipline is to re-run the enumeration with an outside reader whose job is to surface implicit premises, and to re-score the budget whenever the plan is revisited rather than freezing the first list.
How it implements the components¶
assumption_budget— its defining act: an explicit ceiling on the number and total fragility of premises the work may carry.complexity_metric— the fragility × leverage score is the cost measure it reads; complexity here is priced in premise risk, not part count.value_justification— each retained assumption must state what conclusion it buys, so a premise that changes nothing is retired rather than defended.
It scores premises but does not cap a whole planning envelope or force items to displace one another (complexity_budget, pruning_rule) — that is Scope Budget — nor does it track whether the result can still be tested and monitored (validation_load_budget), which belongs to Maintainability Threshold.
Related¶
- Instantiates: Complexity Budgeting — Assumption Budget applies the pattern to hidden premise load rather than visible structure.
- Sibling mechanisms: Scope Budget · Feature Budget · Minimum Description Length Penalty · Design Complexity Review · Complexity Ledger · Change Control Gate · Maintainability Threshold · Model Complexity Penalty
Editorial Notes¶
Form Classification¶
Form family: Rule, Policy & Commitment
Rationale: The mechanism imposes a standing ceiling on the count or scored fragility of load-bearing assumptions and requires fragile premises to be evidenced or retired before more are added, so its operative form is a constraint.
Nearest alternative: Assessment, Review & Assurance — Scoring evaluates individual assumptions, but the budget's defining effect is the cap that governs whether a model, plan, or forecast may proceed.
Review outcome: Adjudicated after independent review; high confidence.
Origin Attribution¶
Primary origin: Organizational & Management Science
Origin pattern: Cross-disciplinary synthesis
Present-day reach: Universal
Rationale: Management complexity and risk budgeting provide the direct institutional form of capping fragile, load-bearing commitments and requiring evidence before adding more.
Related originating lineages:
- Economics & Finance — Forecast and valuation models expose the cumulative fragility of stacked premises and the need to separate base cases from scenarios.
- Futurism & Strategic Foresight — Assumption-based planning contributes explicit premise inventories, vulnerability review, and scenario treatment.
- Philosophy — Epistemology supplies the warrant distinction between supported premises, assumptions, and unknowns.
- Statistics & Experimental Design — Model-assumption audits and sensitivity analysis provide a disciplined way to score consequential premise risk.
Review resolution: Organizational complexity budgeting is the agreed primary. Financial modeling, foresight, epistemology, and statistical assumption review each contribute a distinct part of the cap-and-score mechanism; the four alternates are retained because they are formative, not because the mechanism is merely applicable there.
Attribution caveat: A hard count budget for assumptions is an encyclopedia synthesis rather than a standard named practice.
Encyclopedia synthesis: The exact catalogued form synthesizes established practice rather than reproducing a single standard historical label.
Review outcome: Reconciled after independent review; medium confidence.
Notes¶
[n1] The idea that a system resting on many unverified conditions is fragile — small errors in the premises produce large errors in the conclusion — is central to Nassim Taleb's treatment of fragility. An assumption budget is a crude fragility control: it limits how much of a conclusion is allowed to hang on premises that could break. ↩