Scenario Sensitivity Grid¶
Test or assessment — instantiates Temporal Discounting and Present-Value Framework Selection
Shows how conclusions vary across plausible rates, horizons, timing assumptions, or value bases.
A Scenario Sensitivity Grid takes a present-value conclusion and asks how fragile it is by recomputing it across a grid of plausible temporal assumptions — a matrix of discount rates against horizons (and timing or value-basis variants) — and reporting where the verdict flips. Its defining move is the sweep: it does not read a single scenario, it maps a whole region of assumptions and finds the boundary at which "yes" becomes "no." The output is not a number but a robustness picture — either "positive across every plausible cell" or "positive only if the rate is below 4% and the horizon runs past year 20." It exists to answer the archetype's warning sign, conclusion reversal: is this decision robust, or is it an artefact of one convenient rate and one convenient horizon?
Example¶
A regional transport agency is appraising a highway bypass. The base case, at a 3.5% rate over a 30-year horizon, shows a benefit-cost ratio just above 1 — a marginal "build." Rather than trust that single cell, the analysts build a Scenario Sensitivity Grid. Rows are discount rates (2%, 3.5%, 5%, 7%); columns are horizons (15, 30, 45, 60 years); each cell holds the recomputed benefit-cost ratio, and cells below 1 are shaded. A second panel varies the timing of the traffic-growth benefits, which many appraisals assume arrive suspiciously early.
The grid tells a story the single figure hid. The project is above 1 only in the lower-rate, longer-horizon cells; at 5% over 15 years it falls well below 1. The switching value — the rate at which the ratio crosses exactly 1 — sits around 4.2% at the 30-year horizon. Suddenly the whole decision rests on two contestable choices: whether a rate below 4.2% is defensible and whether a 30-plus-year horizon is honest. The agency now argues about those two things, which is exactly the right argument, instead of nodding at a fragile base case.
How it works¶
- Fix the axes to temporal assumptions. Rate and horizon are the primary grid dimensions, with timing and value-basis variants as extra panels — the assumptions the archetype says most often reverse a call.
- Recompute, do not re-argue. Each cell is the same underlying model re-run under that cell's assumptions; the grid perturbs inputs, it does not build new cases.
- Shade the flip. Mark the region where the verdict changes sign and locate the switching value — the exact rate or horizon at which the conclusion crosses.[n1]
- Report the robust claim. State the conclusion as a region ("holds below 4.2% and beyond 30 years"), so the decision attaches to the boundary rather than to one cell.
Tuning parameters¶
- Axis choice — which assumptions get their own dimension (rate, horizon, benefit timing, value basis). More axes map more fragility but multiply cells combinatorially.
- Range and step — how wide and how fine each axis runs. Wider ranges catch distant flips; finer steps locate the switching value precisely but crowd the grid.
- Plausibility bounds — how far out a cell still counts as "plausible." Generous bounds test stress harder but risk treating fantasy scenarios as real threats.
- Flip criterion — what counts as a reversal (sign change, a ratio crossing 1, a rank change against an alternative). The criterion decides which cells get shaded.
When it helps, and when it misleads¶
Its strength is that it converts a point estimate into an honest map of where the decision does and does not hold, exposes rate-and-horizon dependence directly, and gives negotiators a concrete boundary — the switching value — to argue over instead of a fragile headline.
Its failure mode is scope theatre: a grid can look exhaustive while omitting the one axis that actually matters, or its "plausible" bounds can be quietly drawn to keep every cell green. Sweeping many axes at once also invites the analyst to cherry-pick the panel that flatters the project. The classic misuse is running the grid after the decision to decorate it with the appearance of rigour. The discipline is to choose the axes and their plausibility bounds before seeing the results, to include the assumptions most likely to hurt the favoured option, and to report the flip region plainly even when it is uncomfortably close to the base case.
How it implements the components¶
sensitivity_and_threshold_test— its core: it sweeps plausible rates, horizons, and timings and locates the threshold at which the conclusion reverses.decision_horizon_definition— by making horizon a swept axis, it forces the horizon choice into the open and tests whether truncation alone is deciding the case.
It does not compute the base figures it perturbs (present_value_conversion_rule — Net Present Value Model) and it does not read a single recovery point from the raw timing profile (consequence_timing_profile, real_nominal_indexing_basis); that interpretable one-point timing check is its test-cluster twin Payback and Break-Even Cross-Check. It shares the sweep with Real-Options Cross-Check but differs in what it sweeps: this grid sweeps the rate and horizon of a fixed decision to find its switching value, whereas that cross-check sweeps the governing uncertainty to price the option to wait or stage.
Related¶
- Instantiates: Temporal Discounting and Present-Value Framework Selection — it audits the temporal-valuation choices for conclusion reversal.
- Consumes: Net Present Value Model supplies the base computation the grid re-runs cell by cell.
- Sibling mechanisms: Net Present Value Model · Discounted Cash-Flow Table · Social Discount-Rate Schedule · Declining Discount-Rate Schedule · Real/Nominal Adjustment Worksheet · Payback and Break-Even Cross-Check · Real-Options Cross-Check
Editorial Notes¶
Form Classification¶
Form family: Analysis, Modeling & Optimization
Rationale: Scenario Sensitivity Grid operates as an analytical, modeling, inference, comparison, or optimization procedure that derives insight or a solution because it shows how conclusions vary across plausible rates, horizons, timing assumptions, or value bases.
Independent corroboration: The frozen evidence defines Scenario Sensitivity Grid as 'Shows how conclusions vary across plausible rates, horizons, timing assumptions, or value bases', so its operative form is Analysis, Modeling & Optimization.
Nearest alternative: Representation, Specification & Plan — Scenario Sensitivity Grid includes features of a static representation, map, specification, schema, or prospective plan that externalizes information, but its defining operation is an analytical, modeling, inference, comparison, or optimization procedure that derives insight or a solution.
Review outcome: Independent reviewer agreement; medium confidence.
Origin Attribution¶
Primary origin: Statistics & Experimental Design
Origin pattern: Convergent development
Present-day reach: Universal
Rationale: Displaying how conclusions change as rates, horizons, timing assumptions, or value bases vary is multivariate sensitivity analysis. NIST's sensitivity methodology uses designed variation of factors to identify performance effects; economics supplies domain-specific parameter meaning.
Related originating lineages:
- Data Science & Analytics — data_science contributes measurement, dashboards, mapping, and operational analytics to the mechanism's formative or independently convergent form; that contribution does not displace the primary statistics_experimental_design lineage.
- Economics & Finance — Scenario Sensitivity Grid's terminology and operating form—shows how conclusions vary across plausible rates, horizons, timing assumptions, or value bases—are rooted most directly in economics, finance, and mechanism-design practice.
- Operations Research — Decision analysis materially interprets rate and horizon variation.
- Organizational & Management Science — Organizational design, management, and operational governance supplies a parallel or contributing lineage for the mechanism's defining operation: shows how conclusions vary across plausible rates, horizons, timing assumptions, or value bases.
Review resolution: The blind reviewers disagreed on primary lineage (statistics_experimental_design versus economics_finance); authoritative or primary research supports statistics_experimental_design as the best historical origin. Displaying how conclusions change as rates, horizons, timing assumptions, or value bases vary is multivariate sensitivity analysis. NIST's sensitivity methodology uses designed variation of factors to identify performance effects; economics supplies domain-specific parameter meaning. The cited NIST, Sensitivity Analysis for Biometric Systems directly supports the defining operation used in that choice. All independently supported contributing domains are retained without an arbitrary cap, while domain_reach=universal records later applicability separately from provenance.
Encyclopedia synthesis: The exact catalogued form synthesizes established practice rather than reproducing a single standard historical label.
Review outcome: Researched adjudication after independent review; high confidence.
Sources consulted:
Notes¶
[n1] The switching value in cost-benefit analysis is the value of an input — here the discount rate or horizon — at which the appraisal's conclusion just reverses (for example, the rate at which a benefit-cost ratio equals exactly 1). Locating it turns "is this robust?" into a specific, arguable number. ↩