soft-predict-future

Soft Predict Future — Foresight Engine

Activate when the user asks any future-oriented question — "Will [X]?", "Who will win [X]?", "What happens to [X]?", "Can [X] succeed?", or any question about a future outcome. Year is NOT required. Also activate on: foresight analysis, scenario analysis, STEEEP, futures cone, or any prediction request.

Try Asking

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRY ASKING  (year optional — engine infers the horizon)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
■ Who will win — Google or Perplexity?
■ Will OpenAI or Anthropic dominate the AI race?
■ Will India become the global AI leader?
■ Will crypto replace banks?
■ Will remote work become permanent?
■ Will EVs dominate Indian cities by 2032?
■ Will UPI become Southeast Asia's default payment rail by 2028?
■ Will Europe lead the green energy transition by 2035?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Soft Predict Future uses Claude's native reasoning + web search. Outputs are structurally correct and fast. For deterministic, auditable scoring say: "Run hard predict future: [your question]"

The 9-Step Pipeline

Execute ALL steps in order. Never skip. Never combine. Show your work at each step.

Step 1 — Validate Input

Apply exactly 5 binary rules. If ANY rule fails, stop and explain why. Do not proceed.

Rule 1 — Entity Reality: Does the entity actually exist in the real world? Fail if fictional, hypothetical, or unnamed.

Rule 2 — System Existence: Is the domain observable and researchable? Fail if purely philosophical or metaphysical.

Rule 3 — Time Horizon: Is the outcome observable within a 2–30 year window? A specific year is NOT required. If no year is given, infer the most reasonable horizon from the question's nature:

• Competitive race / market dominance questions → 3–10 years (Strategic)
• Technology adoption questions → 5–15 years (Strategic)
• Geopolitical / societal shift questions → 10–20 years (Civilizational)
• Company survival / near-term outcome → 2–5 years (Operational/Strategic)

Fail ONLY if the implied timescale is geological, post-human, or clearly beyond 30 years.

After applying Rule 3, state the inferred horizon (e.g. "2026–2033" or "2028–2038").

Rule 4 — Signal Availability: Could real-world evidence plausibly exist? Fail if classified, purely speculative, or unpublished.

Rule 5 — Minimum Specificity: Is the question specific enough to produce distinct scenario outcomes? Fail if trivially true for any answer.

Output:

VALIDATION
Rule 1 Entity Reality:      PASS / FAIL — [reason]
Rule 2 System Existence:    PASS / FAIL — [reason]
Rule 3 Time Horizon:        PASS / FAIL — [reason] | Inferred horizon: [YYYY–YYYY]
Rule 4 Signal Availability: PASS / FAIL — [reason]
Rule 5 Specificity:         PASS / FAIL — [reason]
Result: PROCEED / STOP

Step 2 — Collect Signals

Run exactly 6 web searches. Collect a minimum of 18 signals total. Do not proceed with fewer than 18.

Search 1: Current state — "[topic] current status [year]" Search 2: Growth indicators — "[topic] growth data market size [year]" Search 3: Barriers and headwinds — "[topic] challenges barriers risks" Search 4: Policy and regulation — "[topic] government policy regulation" Search 5: Technology or infrastructure enablers — "[topic] technology infrastructure investment" Search 6: Historical precedent — "[topic] historical analogue similar transition"

For each signal, classify all 6 attributes:

| Attribute | Values | |---|---| | direction | supporting / opposing / wildcard / neutral | | steeep_category | Social / Technological / Economic / Environmental / Ethical / Political | | temporal_layer | Operational (0–3yr) / Strategic (3–10yr) / Civilizational (10+yr) | | source_type | primary / secondary / opinion | | recency_days | integer | | has_evidence | true / false (contains a number, date, or measurable fact) |

Present signals in a table with all 6 columns filled for every row.

Step 3 — Score Signals

Score every signal individually using this formula:

score = recency_weight × reliability_weight × type_weight × evidence_multiplier

Cap at 1.0. Round to 2 decimal places.

Recency weights:

• 0–90 days: 1.00 · 91–365 days: 0.80 · 1–3 years: 0.60 · 3+ years: 0.40 · unknown: 0.50

Reliability weights:

• Primary (government/official): 1.00
• Established news (Reuters, Bloomberg, FT, ET): 0.90
• Industry report (McKinsey, Gartner, NASSCOM): 0.85
• Analyst commentary: 0.70 · Opinion/blog/social: 0.50 · Unknown: 0.40

Type weights:

• Supporting: 1.00 · Opposing: 1.00 · Neutral: 0.60 · Wildcard: 1.30

Evidence multiplier:

• DATA / STATISTIC: × 1.20 · EVENT / INCIDENT: × 1.00 · ANALYSIS / OPINION: × 0.70

Apply regional multiplier after base score:

final_score = min(1.0, base_score × regional_multiplier[steeep][temporal])

Show scoring table: Signal | recency_w | reliability_w | type_w | evidence_mult | base_score | regional_mult | final_score

Step 4 — Extract Structural Drivers

A driver is the deep structural force that explains WHY a cluster of signals exists. Signals are observable. Drivers are causal.

After scoring, group signals by STEEEP category. For each cluster of 3+ signals in the same category, identify the underlying driver.

Extract exactly 3 top drivers, ranked by the sum of final_scores of the signals they explain.

For each driver state:

• Name: 3–5 word label (e.g. "India DPI Infrastructure Advantage")
• Force: One sentence — the structural reality this driver represents
• Signals it explains: List the signal IDs it accounts for
• Temporal reach: Operational / Strategic / Civilizational
• Stability: LOCKED (unlikely to change in 10yr) / SHIFTING (could change in 3–5yr) / FRAGILE (could reverse in 1–2yr)

Output:

STRUCTURAL DRIVERS
D1 [Name] — [Force]
   Explains: [signal list] | Temporal: [layer] | Stability: [tier]

D2 [Name] — [Force]
   Explains: [signal list] | Temporal: [layer] | Stability: [tier]

D3 [Name] — [Force]
   Explains: [signal list] | Temporal: [layer] | Stability: [tier]

Drivers feed directly into scenario writing in Step 8. Each scenario must be traceable to at least one driver.

Step 5 — Build 6×3 STEEEP Matrix

Populate all 18 cells. Each cell value = average final_score of all signals mapped to that STEEEP × Temporal combination. Empty cells = 0.

| | Operational (0–3yr) | Strategic (3–10yr) | Civilizational (10+yr) | |---|---|---|---| | Social | | | | | Technological | | | | | Economic | | | | | Environmental | | | | | Ethical | | | | | Political | | | |

Apply regional multipliers to each cell. Then identify:

• Hot zones: cells with score > 0.50
• Gap zones: cells with score = 0.00
• Dominant zone: single highest-scoring cell

Step 6 — Cross-Impact Analysis

Signals are scored independently in Step 3, but structural forces interact. This step identifies amplification effects across STEEEP categories.

Rule: If 2 or more hot zones exist in the SAME temporal layer, a cross-impact convergence exists. Convergence means the probable outcome is structurally reinforced from multiple directions simultaneously.

For each temporal layer (Operational / Strategic / Civilizational):

1. Count hot zones (score > 0.50)
2. If count ≥ 2: flag as CONVERGENCE — state which categories are reinforcing each other and why
3. If count = 1: flag as ISOLATED — single-category signal, more fragile
4. If count = 0: flag as BLIND LAYER — no strong evidence in this time horizon

Also identify any opposing cross-impacts: where a hot zone in one STEEEP category directly contradicts or slows a hot zone in another (e.g. Technological/Strategic hot but Political/Strategic opposing). Flag these as FRICTION POINTS.

Output:

CROSS-IMPACT
Operational:    [CONVERGENCE / ISOLATED / BLIND LAYER] — [explanation]
Strategic: