Planning Under Uncertainty
Scope
Covers
- Turning ambiguity into an executable plan via hypotheses, experiments, and decision triggers
- Diagnosing “what’s actually happening” before acting (especially in crisis / wartime situations)
- Using data as a compass (directional checks) rather than a GPS (false precision)
- Building buffers and contingencies so the plan survives chaos
- Setting a cadence for learning, decision-making, and stakeholder communication
When to use
- “We need a plan, but the requirements are unclear and the outcome is uncertain.”
- “Create a hypothesis-driven plan (experiments + decision rules) for this initiative.”
- “We’re in a crisis (drop in retention/revenue/reliability) and need a wartime diagnosis + action plan.”
- “Help us build contingencies, buffers, and pivot triggers before we commit.”
When NOT to use
- You don’t agree on the underlying problem/opportunity (use
problem-definition). - You need to choose what to do among many options (use
prioritizing-roadmap). - You already have a clear plan and only need dates/milestones and stakeholder cadence (use
managing-timelines). - You need a decision-ready PRD/spec for build execution (use
writing-prds/writing-specs-designs).
Inputs
Minimum required
- The initiative context and desired outcome (“what are we trying to change?”)
- Time horizon and urgency (wartime vs peacetime)
- Constraints/guardrails (quality, compliance, brand, budget, “must not worsen” metrics)
- Stakeholders and decision rights (who decides pivot/stop/scale?)
- Top unknowns/assumptions (what would change the plan?)
- Current signals (what data exists; what feels true but unproven?)
Missing-info strategy
- Ask up to 5 questions from references/INTAKE.md.
- If answers aren’t available, proceed with explicit assumptions and list Open questions that could change the plan.
Outputs (deliverables)
Produce an Uncertainty Planning Pack in Markdown (in-chat; or as files if the user requests), containing:
- Decision frame (objective, “why now”, success + guardrails, time horizon, decision owner)
- Uncertainty map (assumptions/unknowns, confidence, impact, validation plan)
- Hypotheses + experiment portfolio (what we’ll learn, how, and what decision it enables)
- Plan v0 with buffers + contingencies (phases/options, triggers, fallbacks, pivot criteria)
- Cadence + comms (learning review ritual, update template, decision log)
- Risks / Open questions / Next steps (always included)
Templates: references/TEMPLATES.md Expanded guidance: references/WORKFLOW.md
Workflow (7 steps)
1) Intake + mode setting (wartime vs peacetime)
- Inputs: User request; references/INTAKE.md.
- Actions: Clarify urgency, stakes, and what decision is needed. Decide whether you’re in diagnosis-first wartime mode or exploration peacetime mode.
- Outputs: Short decision frame draft + mode declaration.
- Checks: You can state: “We’re optimizing for <fast stabilization / learning / growth>. The decision we need by is <pivot/stop/scale/commit>.”
2) Diagnose reality (humility first)
- Inputs: Current signals, anecdotes, dashboards, incident reports, qualitative inputs.
- Actions: Separate symptoms from hypotheses. Write 3–7 plausible explanations, and identify what evidence would falsify each. Avoid prematurely picking a favorite story.
- Outputs: “What we know / don’t know” + initial hypothesis set.
- Checks: At least one hypothesis contradicts the team’s initial intuition (to reduce confirmation bias).
3) Build the uncertainty map (assumptions → validation plan)
- Inputs: Hypotheses; constraints; stakeholders; time horizon.
- Actions: Create an uncertainty map of assumptions/unknowns with confidence and impact; prioritize the top items that would change the plan.
- Outputs: Uncertainty map table + prioritized “top 5 unknowns”.
- Checks: Every top unknown has a clear validation method and an owner.
4) Define hypotheses + decision rules (learning over “wins”)
- Inputs: Top unknowns; success/guardrails; risk tolerance.
- Actions: Turn unknowns into testable hypotheses. For each hypothesis, define: expected learning, success signal(s), guardrails, and the decision the result enables (stop/pivot/scale).
- Outputs: Hypothesis statements + decision rules.
- Checks: Each hypothesis ties to a decision; “winning” is defined as learning, not just positive results.
5) Design a reproducible testing process (many shots at bat)
- Inputs: Hypothesis set; available tools; team capacity.
- Actions: Create an experiment portfolio that balances speed vs confidence (smoke tests, prototypes, A/Bs, customer calls, operational drills). Set a cadence to run and review tests continuously.
- Outputs: Experiment portfolio table + review cadence.
- Checks: At least 1 fast test can run within the next 1–2 weeks (or faster in wartime).
6) Turn learning into a plan with buffers, contingencies, and triggers
- Inputs: Experiment portfolio; constraints; dependencies; timeline needs.
- Actions: Draft Plan v0 with phases/options; add buffers; define contingencies and explicit triggers for pivot/rollback/escalation. Use data as a compass: focus on directional signals and early warnings, not false certainty.
- Outputs: Plan v0 + buffer/contingency section + trigger list.
- Checks: There is a clear “if X happens, we will do Y” for the top risks/unknowns.
7) Quality gate + finalize
- Inputs: Full draft pack.
- Actions: Run references/CHECKLISTS.md and score with references/RUBRIC.md. Ensure Risks / Open questions / Next steps exist with owners and time bounds.
- Outputs: Final Uncertainty Planning Pack.
- Checks: A stakeholder can approve the plan async and the team can execute without re-litigating the ambiguity.
Quality gate (required)
- Use references/CHECKLISTS.md and references/RUBRIC.md.
- Always include: Risks, Open questions, Next steps.
Examples
Example 1 (ambiguous initiative): “We think onboarding is hurting conversion, but we’re not sure why. Create an uncertainty plan with hypotheses, experiments, and pivot triggers.” Expected: an uncertainty map + experiment portfolio (qual + quant) + a Plan v0 that commits to learning milestones, not premature delivery dates.
Example 2 (wartime): “Retention dropped 15% this week after a release. We need a wartime plan: diagnose root causes, run rapid tests, and decide whether to rollback or patch.” Expected: diagnosis-first workflow with falsifiable hypotheses, tight guardrails, and explicit rollback/escalation triggers.
Boundary example: “Write a full PRD for Feature X.” Response: clarify uncertainty first (this skill), then use writing-prds once the hypotheses, constraints, and decision gates are clear.