Futures Wheel

Quick facts

Mechanism: Produces a consequence map by tracing the first, second, and third order effects of a change or decision radiating outward from the center, surfacing ripples beyond the obvious and flagging the high-impact branches.

Produces: consequence-map

Evidence: P · Domain: Systems and Consequences

Use when a decision has knock-on effects over time, or when first-order thinking is missing downstream risks and opportunities.

Picture it

Put the change in the center, then radiate outward: direct first-order effects, then the effects of those effects, and so on. The surprises usually live in the second and third ring.

graph LR
  C["CHANGE:<br/>launch a free tier"] --> A1["1st: signups jump"]
  C --> A2["1st: support load rises"]
  A1 --> B1["2nd: free users rarely convert"]
  A1 --> B2["2nd: brand reaches new segment"]
  A2 --> B3["2nd: paid users wait longer"]
  B3 --> D1["3rd: paid churn rises"]
  classDef c fill:#e6e9ff,stroke:#6366f1,color:#1e1b4b,font-weight:bold
  classDef o1 fill:#e3f5e8,stroke:#16a34a,color:#14532d
  classDef o2 fill:#fff4d6,stroke:#b06000,color:#5c3a00
  class C c
  class A1,A2 o1
  class B1,B2,B3,D1 o2

The first ring is obvious; the value is following the chain to the non-obvious second- and third-order consequences.

Most analysis stops at first-order consequences: the immediate, obvious results. A futures wheel pushes past that. The change goes at the center; first-order consequences radiate around it; each of those spawns second-order consequences (“and then what?”); then third-order. The structure forces attention onto the downstream and cross-domain ripples that linear thinking skips, and flags the branches worth a response. The output is a consequence map. For a quick pass, a lightweight “second-order effects” mode runs only the first-to-second step.

When to Use

• A decision or change has knock-on effects that play out over time.
• First-order analysis is missing downstream risks or opportunities.
• Scanning the systemic side effects of a new idea before committing.

When NOT to Use

• Simple, linear situations with no meaningful higher-order effects.
• When you need to decide, not explore (hand the map to a decision skill).
• When the result would be branches to irrelevance rather than a focused map.

Instructions

When asked to build a futures wheel, follow these steps:

1. Center it. State the change or decision at the middle, in one line.
2. First order. List the immediate, direct consequences. Span domains (technical, financial, customer, team, competitive), not just the obvious one.
3. Second order. For each meaningful first-order effect, ask “and then what happens?” and add its consequences.
4. Third order. For the branches that still matter, extend one more step. Stop a branch when it goes trivial.
5. Flag the branches that matter. Mark the high-impact or non-obvious consequences, and add a one-line “watch or do about it” for each.
6. Emit the consequence map per references/TEMPLATE.md.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the nested consequence map with flagged branches, not prose.

Quality Checklist

Before finalizing, verify:

• The map reaches at least second order; it does not stop at first-order.
• First-order effects span multiple domains, not just the obvious one.
• Branches that go trivial are pruned, not padded.
• The high-impact or non-obvious branches are flagged with a response note.
• Branches are presented as possible ripples to watch, not predictions.
• The output is the consequence map artifact, not prose.

Evidence

Tier P. The futures wheel is an established foresight method (Glenn, 1971; used in foresight practice and documented in primers such as UNICEF’s 2025 guidance), valued for pushing analysis beyond first-order consequences. Its validation is qualitative; it does not predict the future, so branches are structured speculation, not probabilities. Evidence is transferred from human foresight practice, not AI-validated. Full grading: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed consequence map.

Deep dive: worked example

A full worked run (the shared Northwind scenario)

Consequence Map - Worked Example

A completed run of think-futures-wheel, on the shared Northwind scenario. This is the quality bar a generated map should meet.

Northwind is a B2B SaaS. Here the skill maps the ripple effects of actually launching the self-serve free tier.

---

Center

• Change / decision: Launch a self-serve free tier.

Consequence map

• First order: surge in signups (customer)
  • Second order: support volume rises
    • Third order: support team overwhelmed, paid-customer SLAs slip
  • Second order: mix shifts toward non-ICP users
    • Third order: sales wastes time triaging unqualified leads
• First order: some paying customers downgrade to free (financial)
  • Second order: net new MRR slows
    • Third order: board reads Q3 as a miss despite signup growth
• First order: infrastructure usage rises (technical)
  • Second order: cloud cost per user climbs
    • Third order: unit economics break if free cohort is large and non-converting
• First order: competitors see the move (competitive)
  • Second order: price/feature response, “free tier” becomes table stakes
• First order: sales comp and routing strain (team)
  • Second order: rep behavior shifts to protect commissions
    • Third order: reps suppress free signups, undercutting the whole motion

Flagged branches (high-impact or non-obvious)

Branch	Why it matters	Watch or do about it
Downgrade -> MRR slows -> board reads a miss	The growth move could register as a failure on the one metric that triggered it	Instrument free-vs-paid downgrade weekly; pre-brief the board on leading vs lagging signals
Support overwhelmed -> paid SLA slips	A growth tactic damaging existing paid customers is a net loss	Cap free usage; ship self-serve docs before launch; set a support-load tripwire
Reps suppress free signups	A non-obvious second-order effect that quietly kills the motion	Realign comp and routing before launch, not after

---

Note: the value is the second- and third-order branches. A first-order-only view (“more signups, good”) misses that the same move can slip paid SLAs, break unit economics, and be quietly sabotaged by the comp plan. This map feeds directly into a premortem.

Grounding: the full evidence dossier

What the research does and does not show, with graded sources

Evidence Dossier: Futures Wheel

Single source of truth for the futures-wheel skill. The SKILL.md, sidecar, and evals derive from this.

Skill	thinking-framework-skills.futures-wheel (installable name think-futures-wheel)
Family	systems-and-consequences
Evidence tier	P (foresight practitioner; qualitative validation)
Confidence	High that first-order-only thinking misses real effects; the wheel is a structured aid, not a predictor
Status	draft (authored 2026-05-31 from the discovery corpus)

1. The mechanism (what actually does the work)

Most analysis stops at first-order consequences: the immediate, obvious results of a change. A futures wheel pushes past that. Put the change or decision at the center; radiate the first-order consequences around it; from each of those, derive second-order consequences (“and then what?”); then third-order. The structure forces attention onto the downstream and cross-domain ripples (technical, social, financial, organizational) that linear thinking skips. The work is done by the disciplined “and then what happens?” expansion across orders, and by flagging the high-impact or non-obvious branches that deserve a response.

A lightweight mode (“second-order effects”) runs only the first-to-second order step as a quick prompt when a full wheel is overkill.

2. Lineage

• Jerome Glenn devised the Futures Wheel (1971) as a foresight technique. Widely used in foresight practice and documented in primers such as UNICEF’s 2025 foresight guidance and in transport/policy research.

No trademark. Named descriptively.

3. What the evidence shows, and what it does NOT show

Supported (practitioner): the futures wheel is an established, widely used foresight method, valued precisely for pushing analysis beyond first-order into second- and third-order consequences. Its validation is qualitative and practice-based.

NOT shown: it does not predict the future and has no controlled evidence of forecast accuracy. The branches are structured speculation, not probabilities. Grade P; present outputs as a map of possible ripples to watch and respond to, not as predictions.

4. Transferred-evidence flag

Evidence is from human foresight practice, not AI-augmented use. Transferred, not AI-validated. The AI value: a model defaults to first-order answers; forcing the multi-order expansion and a structured map is a direct counter, though the radial diagram is approximated in markdown as nested structure.

5. When it works / when it fails

Works best when: a decision or change has knock-on effects over time; the obvious first-order analysis is missing downstream risks and opportunities; scanning the systemic externalities of a new idea.

Fails or misleads when (poor-fit / anti-patterns):

• Stopping at first-order (defeats the purpose).
• Branching to irrelevance - every node spawning trivial children until the map is noise.
• Treating the speculative branches as predictions or probabilities.
• Simple, linear situations where there are no meaningful higher-order effects.
• When you need to decide, not explore (hand the map to a decision skill).

6. Output artifact

A consequence map: the central change, then nested first / second / third-order consequences (tagged by domain where useful), with the high-impact or non-obvious branches flagged and a short “what to watch or do about it” note for those.

7. Sources

1. Glenn, J. (1971). The Futures Wheel (foresight method).
2. UNICEF (2025) foresight primer; foresight and transport/policy literature describing the wheel’s use for second- and third-order consequences.

Verification status: Glenn attribution and foresight usage are well-attested. Do not attach forecast-accuracy claims; the method’s validation is qualitative.