Structural Analysis Engine

Systems under
pressure create
patterns.

Describe a situation. The engine maps it against known structural patterns from 3,000 years of recorded system failures.

Situation Analysis
Describe a system under pressure. The engine identifies which structural patterns are active, the likely trajectory, and the signals worth watching. Layer what‑if events to stress‑test the conditions.
Engine · 8 patterns · 13 vectors
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Ready · describe a situation to begin 0 what‑if events selected
What‑if events 13 vectors

Select events to test against the situation. Each one changes the conditions — the engine shows you what changes.

0 / 13 selected
Common Sense Library
An evaluation layer. Help validate entries — if a statement is structurally obvious to you, that's a data point. No expertise required.
Validator · sandboxed session
Evaluation Layer

How it
works.

Read the statement. Grade it, answer it, or both. Your passphrase identifies your validations anonymously — nothing is stored on your device.

01
Grade · Rate structural obviousness on a four‑step scale: no, maybe, yes, obviously.
02
Answer · Say what you think in your own words — voice or text.
03
Skip · Not for you? Pass. The next statement loads.
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Common Sense Library
Session active
1 / —
CATEGORY · STRUCTURAL
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Validator Access
Sign in to begin
Use Enter Library or Train Reactions above to start a session.
Anonymous · sandboxed · no device storage.
How Situation Analysis Works

The engine uses four structural analysis methods. Each operates independently — together they cover different types of certainty.

01
Pattern Matching
The engine compares a situation against a library of structural patterns identified from historical system failures across 3,000 years. When a pattern matches, the historical outcome provides a reference trajectory. Different domain, same structure.
02
Decision Vectors
A decision vector is a structural force that shapes outcomes — role, institutional pressure, authority gradient. Once the setup is known, the prediction follows without personal data. The field determines the range of likely decisions.
03
Fragility Mapping
Inject controlled perturbations into a knowledge zone to find where stability ends. The method maps the exact threshold where a system shifts from stable to unstable — and whether it recovers or collapses.
04
Multi-Model Comparison
Run the same query through multiple AI models from different training origins. Where models agree, the answer is mainstream. Where they diverge, the territory is contested. Divergence itself is data — it maps the frontier of the known.
05
Evaluation Logic
Every input carries an interpretation distance (0 = raw event, 5 = AI consensus) and a certainty type (structural, measured, inferred, consensus, AI output). The system weights each claim by its type and domain match — not by authority or agreement.
How the Common Sense Library Works

Common sense is pre-social, structural, and universal. Your recognition of a statement is a data point — not a vote, not an opinion. The library turns that recognition into a measurable evaluation layer.

01
Structural Obviousness
A statement passes common sense validation when its structure is recognizable to an ordinary person without context, expertise, or prior exposure. Recognition — not agreement — is the signal.
02
Grade + Answer
Validators rate each statement on a four-step scale (no, maybe, yes, obviously) and can add a free-text response. Both data points are collected — the grade measures recognizability, the answer captures structural framing.
03
Sandboxed Sessions
Unknown passphrases are sandboxed until reviewed. All validation is anonymous — the passphrase identifies the session, not the person. Nothing is stored on the device.
04
Evaluation Layer
Common sense is the natural evaluation system every human has — pre-social, structural, universal. The engine uses a validated common sense library as a high-confidence evaluation layer. Crowd validation measures recognizability, not agreement.
Pattern Library

Eight structural signatures identified across 3,000 years of recorded system failures. Each pattern describes a sequence — the domain changes, the structure repeats.

Loading patterns…

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