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Scenario Planning (2x2)

Most strategy quietly rests on a single implicit forecast of the future, and then optimizes for that one future. Scenario planning refuses that bet. It constructs a small SET of alternative states of the external world the planner does not control - regulation, technology adoption, demand, geopolitics - organized by the two axes of uncertainty that most change the strategic choice, and then judges the strategy against the whole set instead of against any one prediction. The durable move is not drawing the grid. It is holding several divergent futures in parallel and asking which moves survive all of them. The output is a scenario set: 2-4 contrasting, internally consistent short narratives of alternative external futures, plus a robustness read of the strategy across them. It is explicitly not a prediction and not a single preferred path. The dominant packaging is the 2x2, because two high-impact and high-uncertainty axes cross into four contrasting worlds - enough variety to break single-future thinking without overwhelming a group.

When to Use

• The planning horizon is long, and the forces that most affect the choice are outside the planner's control and not reliably predictable (regulation, technology adoption, demand, competitive structure, macro and geopolitics).
• A strategy is quietly riding on a single implicit forecast, and it would be worth knowing which moves hold up if that forecast is wrong.
• A high-stakes, hard-to-reverse bet needs to be stress-tested against more than one plausible future before it is committed.
• The team needs a shared, legitimate way to talk about futures that contradict the house view.

When NOT to Use

• Do not use it as forecasting. The quadrants are structured speculation, not probabilities. Assigning likelihoods to the worlds, or acting on "the most likely quadrant," reintroduces exactly the single-future thinking the method exists to break. This is the most common and most damaging misuse, and even sophisticated users slip here.
• Do not use it to validate or path to one desired endpoint. That is think-backcasting (fix one desired future, derive the path back). Scenario planning refuses to pick a single future and derives no path.
• Do not use it to trace the ripples of one decision being made. That is think-futures-wheel (one consequence map radiating outward from one change), not a set of alternative external worlds.
• Do not use it to imagine one specified failure. That is think-premortem (assume one plan failed, reason back to causes). Scenario planning is multi-future and not failure-anchored.
• Do not reduce a rich force field to two axes for neatness. Forcing two orthogonal axes can discard the very interactions that matter and produce tidy quadrants with little content (Ramirez and Wilkinson, 2014). If the two best candidates are not independent, say so and reselect.
• Do not produce four mild variations of the present. Four near-identical worlds, or one obvious utopia and dystopia pair, give the comfort of "having done scenarios" with none of the cognitive benefit.
• Do not run it as ritual with no strategy tested against the worlds. Narratives that no strategy is stress-tested against are theater. The payoff is the robustness read and the signal watch-list, not the stories.

Instructions

When asked to build scenarios or stress-test a strategy against an uncertain future, follow these steps:

1. Frame the focal decision and horizon. State, in one line, the strategic choice under pressure and the time horizon over which the external future matters. The scenario set exists to serve this decision; with no decision, stop (that is the ritual anti-pattern).
2. Scan the driving forces. List the forces shaping the domain the planner does not control - regulation, technology adoption, demand, competitive structure, macro and geopolitics, social. Span categories; do not stop at the obvious one.
3. Sort by impact and by uncertainty. Rate each force on how much it would change the strategic choice (impact) and how unpredictable it is (uncertainty). The target is the high-impact AND high-uncertainty corner: the critical uncertainties. Predictable forces are noted as predetermined elements, not axes.
4. Select two critical-uncertainty axes. Pick the two forces that are both high-impact and high-uncertainty and are genuinely independent of each other. Each axis is a spectrum with two contrasting poles. Resist collapsing a rich field to two axes for neatness; if the two best candidates are not independent, say so and reselect.
5. Cross the axes into the 2x2 and name the four worlds. Cross the two axes; each of the four quadrants is the seed of one plausible external future. Give each quadrant a short, evocative name drawn from its pole combination.
6. Construct each world as a divergent, internally consistent narrative. For each quadrant, write a short narrative of that future - how the two poles and the predetermined elements play out together. The worlds must genuinely contrast (not four mild variations of the present) and each must hang together internally. (2-4 worlds; the 2x2 yields four, a smaller set may be defensible when two quadrants collapse into implausibility.)
7. Test the strategy for robustness across all worlds. Run the focal decision and candidate moves against each world. Identify the robust moves that survive every world; the moves that win in one world but lose in another (the bets); and the gaps no current move covers.
8. Name the early signals and the options to keep open. For each world, name the leading indicators that would tell the planner that world is arriving (the watch-list). Name the options worth keeping open precisely because the worlds diverge.
9. Emit the scenario set artifact per references/TEMPLATE.md: the two named axes, the named worlds with their narratives, and the robustness read (robust moves, bets, signal watch-list, options to keep open). Frame the worlds as structured speculation, never as ranked probabilities.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the filled scenario set - two named axes, the 2x2 of named worlds with short narratives, and the robustness read (robust moves, bets, signal watch-list, options to keep open) - not a prose essay. Never rank the worlds by likelihood.

Quality Checklist

Before finalizing, verify:

• [ ] The focal decision and horizon are stated in one line, and the scenario set serves that decision.
• [ ] The two axes are both high-impact and high-uncertainty, genuinely independent, and each is a spectrum with two named poles - not a rich field collapsed to two for neatness.
• [ ] The four worlds genuinely contrast (not four mild variations of the present) and each is internally consistent.
• [ ] A strategy is actually tested against every world: robust moves, one-world bets, and uncovered gaps are all named.
• [ ] Each world has a signal watch-list, and the options worth keeping open are named.
• [ ] The worlds are framed as structured speculation, never ranked by likelihood or treated as probabilities.
• [ ] The output is the scenario set artifact, not prose.
• [ ] No overclaiming: the evidence is practitioner-grade and transferred; claim a divergence-and-robustness aid, not a predictor or a measured gain in decision outcomes (see evidence/dossier.md).

Evidence

Tier P (governing; honest read M-down-to-P). Scenario planning is a genuinely established, half-century-old practitioner method (Wack 1985; Schwartz 1991; Schoemaker 1995), with a coherent rationale - counter single-future anchoring and test for robustness. There is one reasonably supportive controlled study (Meissner and Wulf, 2013) finding reduced framing bias on 252 management students, but the field's most-cited author calls the usefulness evidence "anecdotal" (Schoemaker, 2004), the strongest real-expert study finds scenarios shift judgment toward whichever scenario is shown rather than uniformly improving it (Phadnis et al., 2015), some judgmental-forecasting work finds scenarios can worsen accuracy, and the 2x2 itself is critiqued as an oversimplified off-the-shelf tool (Ramirez and Wilkinson, 2014). Per this library's conservative rule the governing grade is the lower half, P. All evidence is transferred from human subjects in workshop, lab, and field settings; none studies an AI-produced scenario set, which independently caps the grade at P. The skill ships as a divergence-and-robustness aid with a hard "this is not forecasting" wall, never as a predictor. Full grading, sources, and caveats: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed scenario set on a real decision.