framework reference

Long-form treatment of this canon entry. A compact, operational version exists as the skill companion — what the agent reads when calling this tool.

Scenario planning

A structured way to think about the future when prediction is impossible. You don't forecast a single number — you build three to four distinct, internally-coherent stories of how the world could unfold, and stress-test strategy against each. The output isn't a probability distribution; it's a set of named worlds the team can argue with.

Where it comes from

In the late 1960s, two men inside Royal Dutch Shell — Pierre Wack and Ted Newland — were given an unusual brief: study the long-term oil market. Forecasting was the standard tool of the day, and forecasting in 1968 said the world would keep buying cheap oil from a stable Middle East forever.

Wack was sceptical. He had read Herman Kahn's nuclear war scenarios and noticed something: Kahn's value wasn't in predicting which war — it was in giving generals a vocabulary to think in. Wack wondered whether the same trick would work on oil executives. Instead of a forecast he built a small set of plausible futures, each with a name, and walked the Shell board through them.

When OPEC announced the 1973 embargo, Shell was the only major that had already imagined the world it was now living in. Production planning, capital allocation and refinery configuration had been pre-decided against the shock scenario. The competitive position the company gained over that decade is the reason scenario planning is taught in business schools today.

Wack codified the practice in two 1985 Harvard Business Review articles — "Scenarios: Uncharted Waters Ahead" and "Scenarios: Shooting the Rapids" — which remain the canonical primary sources. Peter Schwartz, who succeeded Wack at Shell, took the practice into the rest of the world with The Art of the Long View (1991). Adam Kahane later adapted it for political and social conflict in Transformative Scenario Planning (2012).

What it actually is

Scenario planning is a strategy tool, not a forecasting tool. The distinction matters.

A forecast says: "Oil will be $80 in 2030." A scenario set says: "Here are four futures — Boom, Bust, Walled Garden, Open Roads. In each, oil trades in this band, the regulator does this, our customers behave like that, and our company should do the following."

The forecast invites a single decision. The scenario set invites a conversation about what to do in each world — and a commitment to recognise which one we're in as it unfolds.

Three things separate scenario planning from cousins like sensitivity analysis and Monte Carlo simulation:

  1. Stories, not numbers. A scenario is an internally consistent narrative with a beginning, middle and end. The 50-row Excel that labels itself "Scenario A" is not a scenario — it's a model.
  2. Decisions, not probabilities. Wack famously refused to assign probabilities to scenarios. Probabilities collapse the conversation back into expected value. The point is commitment in each world, not the blended forecast.
  3. Names. Each scenario gets a vivid, world-shape name — "Retreat", "Bridge", "Bloom". The name is what survives the meeting. "Most likely / unlikely" leaks the answer; "Bloom" travels.

Why it works

It works because it solves the two failure modes of business forecasting at once.

The first failure mode is being precisely wrong. A point estimate projected three years out has roughly a 0% chance of being correct, and yet boards routinely allocate capital against one. Scenarios refuse the false precision.

The second failure mode is uselessly hedged. The honest forecaster who says "anywhere from 5% to 25% growth" has produced a useless artefact — nobody can plan against a range that wide. Scenarios refuse the mush.

What scenarios do instead is commit to a small set of distinct worlds, each plausible, each described in enough detail that the team can ask "if we wake up in this one tomorrow, what do we do?". Strategy then crystallises:

  • No-regret moves — moves that win in every scenario. Do them now.
  • Big bets — moves that pay off only in some. Sequence carefully or buy options.
  • Lose-everywhere moves — moves that fail in every world. Drop them.

The third category is the one most companies skip and the one scenarios catch best. A strategy that loses in every plausible future is, by definition, betting on an implausible one.

When it's the right tool

Scenarios fit when the dominant uncertainties are structural and unquantifiable — political, technological, behavioural. They're the right tool for multi-year capital allocation, market entry/exit decisions, regulatory strategy, and contrarian theses arguing the consensus path is unstable.

They're the wrong tool for tactical 3–6 month decisions (sensitivity analysis is faster), for quantifiable risk (Monte Carlo will out-perform), and for teams that won't revisit. Static scenarios decay into theatre within a year.

The full decision logic is in the skill-side reference — the operational chuleta. The version on this page is the why; the skill version is the how.

Worked example — an activist breakup decision

Imagine the board of a mid-cap conglomerate facing an activist demanding a full breakup by year-end. The board's instinct is to reject. The CFO wants scenarios.

Focal question: "Should we accept, reject, or partially concede the breakup demand by Q4?"

Two critical uncertainties chosen:

  1. Proxy advisor support (ISS / Glass Lewis) — Hostile to the activist ↔ Neutral / sympathetic.
  2. Public market multiple compressionCompression continues ↔ Multiples reverse with rate cuts.

These aren't independent in a strict statistical sense, but they're decisively independent for this decision: each can move on its own calendar, driven by different mechanisms (governance reform vs macro-rate cycle).

The four worlds:

Multiple keeps compressing Multiple reverses
Advisors hostile Stand-off — concede partial spin Capitulate — full breakup
Advisors neutral Boardroom war — proxy fight likely Quiet truce — reject demand

Early signals to watch (next 90 days):

  • ISS draft report tone — hostile / neutral / supportive.
  • Sector multiple movement vs S&P 500 — ±200 bps threshold.
  • Top-5 holder voting pattern in comparable recent activist campaigns.
  • Activist's tone in media — escalation vs settlement language.

The wind-vane: if signals 1 and 2 both trip toward Capitulate by week 8, the board moves from reject to negotiated partial spin before the campaign forces the issue. The scenario isn't a prediction — it's a pre-committed contingency.

This is how scenarios actually get used in a boardroom: not as a forecast, but as a trigger plan. Six months later, when the world has settled into one of the four quadrants (or, more often, somewhere on the boundary), the team already knows the move.

Common pitfalls

Fake symmetry. The most common failure: producing one optimistic scenario, one pessimistic, one base case, one "black swan". That's a sensitivity analysis with a costume. Real scenarios are each internally coherent and plausibly the future — not Goldilocks-spaced around a midpoint. If your four scenarios can be ranked from "good" to "bad", you've drawn one variable on two axes.

Naming the scenarios after probabilities. "Most likely" leaks the answer before the conversation begins. Use world-shape names that describe the shape of the world, not its desirability.

Skipping the signals step. A scenario without leading indicators is a museum exhibit. The whole point is to recognise which world you're in as it happens, not after.

Forgetting to revisit. Scenarios decay. Every 12–18 months you re-run the exercise, falsify the dead ones, and ask whether the critical uncertainties still are.

Quant-heavy, narrative-thin. A 50-row spreadsheet labelled "Scenario A" is a model, not a scenario. The story is what the team remembers in the boardroom six months later. If you can't tell the scenario over coffee, it doesn't exist.

Related canon

  • scenario-analysis — the slide artefact that summarises a scenario set: 4-quadrant grid, signals row, strategy callout. Scenario planning is the process; scenario analysis is the deliverable.
  • planning-fallacy — the cognitive bias scenarios are designed to counter: the systematic optimism baked into single-point forecasts.
  • three-horizons — McKinsey's complementary lens. Horizon 1 maps to no-regret moves; Horizon 3 maps to scenario-contingent big bets.
  • monte-carlo-simulation — the right tool when the uncertainty is quantifiable.

Canonical references

scenario-planning.skill.mdskill · LLM source
---slug: scenario-planningview: skilllayer: blockagent: architectcompanion: corpus/storymakers/frameworks/block/scenario-planning.md---# Scenario planning — operational referenceCompact reference for an LLM running a scenario-planning exercise. Theshowcase version (`scenario-planning.md`) is the human-facingexplanation. This file is the chuleta: rules, recipe, anti-patterns.## Use this when- Time horizon is **≥12 months**.- There are **≥2 high-impact uncertainties** that can't be expressed as  a probability distribution.- The two top uncertainties are **roughly independent** (move on their  own calendars).- The team **commits to revisit every 12–18 months**.## Use something else when| If…                                                | Then use…                                ||----------------------------------------------------|------------------------------------------|| Horizon < 12 months                                | Sensitivity analysis or decision tree.   || You have a probability distribution                | Monte Carlo simulation.                  || One dominant uncertainty, others conditional       | Driver-tree / cascade scenarios.         || Single binary fork (yes/no on a regulation)        | Decision tree, two branches.             || Team won't revisit                                 | Don't run scenarios — write a memo.      |## Decision tree```Q1. Is there ≥1 high-impact uncertainty?    NO  → forecast / sensitivity. STOP.    YES → Q2Q2. Horizon ≥12 months?    NO  → decision tree. STOP.    YES → Q3Q3. Probability distribution available?    YES → Monte Carlo. STOP.    NO  → Q4Q4. Top two uncertainties independent?    NO  → cascade scenarios. STOP.    YES → Q5Q5. Will the team revisit in 12–18 months?    NO  → memo. STOP.    YES → run 2×2 scenario planning.```## Recipe (10 steps)1. **Focal question** — one sentence.2. **Brain-dump driving forces** — 30–50 macro factors.3. **Rank by impact × uncertainty.**   - High impact × high certainty → assumption (bake in).   - High uncertainty × low impact → noise (drop).   - High impact × high uncertainty → keep (5–8 candidates).4. **Pick the two most decisive.** Cross axes. Four quadrants.5. **Write each quadrant as a one-page story.** Beginning / middle / end.6. **Name each scenario** with a *world-shape* word — not a probability   word. ("Bloom" not "best case".)7. **List 3–5 measurable early signals per scenario** with thresholds.8. **Stress-test the strategy** against each scenario. Mark moves:   - **Wins everywhere** → no-regret, do now.   - **Wins in some** → sequence, hedge, stage-gate.   - **Loses somewhere** → drop, defer, or buy an option.9. **Document the wind-vane.** Which signals would trigger which   contingency, at what threshold.10. **Calendar the revisit.** Specific date 12–18 months out.## Headline language (verbatim bank)- *"In every scenario, [move X] is value-accretive — start it now."*- *"In the [scenario name] world, [strategy Y] becomes the dominant call.  Here's the early signal we're tracking toward it."*- *"The current plan implicitly bets on [scenario A]. Here's what we  lose if [scenario B] plays out."*- *"By [date], if [signal X > threshold], we shift to [contingency]."*## Pre-mortem checklist (run before shipping)- [ ] Each scenario has a name describing the *world*, not the *mood*.- [ ] Each scenario is internally coherent (no act-2 contradictions).- [ ] No scenario is impossible — a senior outsider couldn't say "this      could not happen".- [ ] The four scenarios are *plausibly* the future, not      Goldilocks-spaced.- [ ] Each scenario has 3–5 measurable signals with thresholds.- [ ] Strategy section names no-regret moves explicitly.- [ ] A revisit date is on the calendar — not "as needed".## Anti-patterns (reject on sight)| Pattern                                   | Why it's wrong                                                      ||-------------------------------------------|---------------------------------------------------------------------|| "Best / base / worst case"                | Three points on the same axis, not four scenarios.                  || "Black swan scenario"                     | Taleb's term means *unknowable*; modelling it is a contradiction.   || Probability-weighted average of scenarios | Defeats the point — collapses to expected value.                    || Scenarios named "Scenario A / B / C"      | Lazy. The name should describe the world.                           || One scenario per business unit            | That's a budget, not scenarios. Scenarios are about the world.      || Quant-heavy, narrative-thin               | A 50-row Excel ≠ a scenario. The story is what survives the room.   |## Related canon (call these tools when relevant)- `scenario-analysis` — the slide artefact (4-quadrant grid).- `planning-fallacy` — the bias to counter.- `three-horizons` — sequence the moves identified.- `monte-carlo-simulation` — when probability distributions are  available.
mdcorpus/storymakers/frameworks/block/scenario-planning.skill.md
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overview

What you need to know

Definition What is it?

A structured way to think about the future when prediction is impossible. Build three to four distinct, internally-coherent stories of how the world could unfold, and stress-test strategy against each. The output is a set of named worlds the team can argue with — not a probability distribution.

When to use When should you use it?
  • Time horizon ≥12 months
  • ≥2 high-impact uncertainties that cannot be expressed as probability distributions
  • top two uncertainties roughly independent
  • team commits to revisit every 12–18 months. For shorter horizons use sensitivity analysis; for quantifiable risk use Monte Carlo; for one-dominant-uncertainty use cascade scenarios.
Why it works Why does it work?

Solves the two failure modes of forecasting at once: being precisely wrong (point estimates anyone can falsify) and uselessly hedged (ranges so wide they cannot guide action). Scenarios commit to a small set of plausible worlds and crystallise strategy into no-regret moves, sequenced bets, and lose-everywhere moves to drop. Names + early signals + wind-vane triggers turn the exercise into a usable contingency plan.

Narrative purpose What's its narrative purpose?

Force narrative discipline on multi-year decisions where single-point forecasts fail. Translate structural uncertainty into a small set of decision frames the boardroom can act on by name.

Anti-pattern What are the anti-patterns?
  • Best/base/worst case (three points on one axis, not four scenarios)
  • probability-weighted average of scenarios (collapses to expected value)
  • Goldilocks-spaced scenarios that rank good-to-bad
  • scenarios named "Scenario A/B/C" instead of world-shape names
  • 50-row Excel labelled as a scenario (model, not story)
  • scenarios without measurable early signals.

Examples

Slide evidence

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