what-if-oracle

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# What-If Oracle — Possibility Space Explorer

A structured system for exploring uncertain futures through rigorous multi-branch scenario analysis. Instead of one prediction, the Oracle maps the full **possibility space** — branching timelines where each path has its own logic, probability, and consequences.

Based on the What-If Statement paradigm: the idea that speculative questions ("What if X?") are not idle daydreaming but a **fundamental computing operation** — the mind's way of simulating futures before committing resources to one.

Published research: [The What-If Statement (DOI: 10.5281/zenodo.18736841)](https://doi.org/10.5281/zenodo.18736841) | [IDNA Consolidation v2 (DOI: 10.5281/zenodo.18807387)](https://doi.org/10.5281/zenodo.18807387)

## Core Principle: 0·IF·1

Every scenario analysis has three elements:

- **0** — The unexpressed state (what hasn't happened yet, the potential)
- **1** — The expressed state (what IS, the current reality)
- **IF** — The conditional bond (the decision, event, or change that transforms 0 into 1)

The quality of the analysis depends on the precision of the IF. A vague "what if things go wrong?" produces vague results. A precise "what if our primary supplier raises prices 30% in Q3?" produces actionable intelligence.

## How to Run the Oracle

### Phase 1 — Frame the Question

Take the user's What-If question and sharpen it:

**Decompose into components:**

- **The Variable:** What specific thing changes? (one variable per analysis)
- **The Magnitude:** By how much? (quantify if possible)
- **The Timeframe:** Over what period?
- **The Context:** What's the current state before the change?

**If the question is vague, sharpen it:**

- "What if AI takes over?" → "What if 40% of current knowledge-work tasks are automated by AI within 3 years in [specific industry]?"
- "What if we fail?" → "What if monthly revenue stays below $5K for 6 consecutive months starting now?"

Present the sharpened question to the user for confirmation before proceeding.

### Phase 2 — Map the Possibility Space

Generate **4-6 scenario branches** using this framework:

| Branch             | Definition                                                                   | Purpose                                            |
| ------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------- |
| **Ω Best Case**    | Everything goes right. Key assumptions all validate. Lucky breaks occur.     | Define the ceiling — what's the maximum upside?    |
| **α Likely Case**  | Most probable path given current evidence. No major surprises.               | Anchor expectations in reality                     |
| **Δ Worst Case**   | Key assumptions fail. Two things go wrong simultaneously.                    | Define the floor — what's the maximum downside?    |
| **Ψ Wild Card**    | An unexpected variable enters that nobody is tracking. Black swan territory. | Stress-test for the unimaginable                   |
| **Φ Contrarian**   | The opposite of the consensus view turns out to be true.                     | Challenge groupthink and reveal hidden assumptions |
| **∞ Second Order** | The first-order effects trigger cascading consequences nobody predicted.     | Map the ripple effects                             |

### Phase 3 — Analyze Each Branch

For each scenario branch, provide:

```
╔══════════════════════════════════════════════╗
║  BRANCH: [Ω/α/Δ/Ψ/Φ/∞] — [Branch Name]    ║
╠══════════════════════════════════════════════╣
║  Probability: [X%]                           ║
║  Timeframe: [When this could materialize]    ║
║  Confidence: [HIGH/MEDIUM/LOW]               ║
╠══════════════════════════════════════════════╣
║  NARRATIVE:                                  ║
║  [2-3 sentences describing how this          ║
║   scenario unfolds step by step]             ║
║                                              ║
║  KEY ASSUMPTIONS:                            ║
║  • [What must be true for this to happen]    ║
║  • [And this]                                ║
║                                              ║
║  TRIGGER CONDITIONS:                         ║
║  • [Early signal that this branch is         ║
║    becoming reality]                         ║
║  • [Second signal]                           ║
║                                              ║
║  CONSEQUENCES:                               ║
║  → Immediate: [What happens first]           ║
║  → 30 days: [What follows]                   ║
║  → 6 months: [Where it leads]               ║
║                                              ║
║  REQUIRED RESPONSE:                          ║
║  [What action to take if this branch         ║
║   activates — specific, actionable]          ║
║                                              ║
║  WHAT MOST PEOPLE MISS:                      ║
║  [The non-obvious insight about this         ║
║   scenario that conventional analysis        ║
║   would overlook]                            ║
╚══════════════════════════════════════════════╝
```

### Phase 4 — Synthesis

After analyzing all branches, provide:

**Probability Distribution:**

```
Ω Best Case ····· [██████░░░░] 15%
α Likely Case ··· [████████░░] 45%
Δ Worst Case ···· [██████░░░░] 20%
Ψ Wild Card ····· [███░░░░░░░]  8%
Φ Contrarian ···· [████░░░░░░]  7%
∞ Second Order ·· [███░░░░░░░]  5%
```

**Robust Actions:** What actions are beneficial across MULTIPLE branches? These are the no-regret moves — do them regardless of which future materializes.

**Hedge Actions:** What preparations protect against the worst branches without sacrificing upside?

**Decision Triggers:** What specific, observable signals should cause you to update which branch is most likely? Define the tripwires.

**The 1% Insight:** What is the one thing about this situation that almost everyone analyzing it would miss? The non-obvious pattern, the hidden assumption, the overlooked variable.

## Golden Ratio Weighting

When evidence exists, weight primary scenarios using the golden ratio:

From qinyan-ai/qinyan-academic-skills, MIT.

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