A system's integrated complexity at time t is the product of three independent quantities: how much irreducible information it integrates, how well it discriminates its own models from external reality, and how dynamically structured its information flow is. All three must be non-trivially present. If any one collapses, so does C.
| Component | Range | Role |
|---|---|---|
| Φ (Phi) | [0, ∞) | Integrated information. Irreducible causal structure above and beyond its parts. |
| R | [0, 1] | Reality discrimination. System's ability to distinguish internal models from external states. |
| D | [0, 1] | Dynamic complexity. Temporal structure of information flow — neither frozen nor random. |
| C | [0, ∞) | Integrated complexity. The multiplicative product — what the system is doing as a whole. |
The multiplicative structure is the claim. Integration without discrimination is a thermostat. Discrimination without dynamics is a photograph. The equation says: you need all three, simultaneously, and their contributions compound.
The Hierarchical Information-Reality Model applies this framework to the problem of subjective experience. The hypothesis: consciousness emerges when C crosses a critical threshold.
Below this value: information processing without experience. Above: subjective experience emerges. The transition is sharp, not gradual.
| Existing Theory | Component | Relationship |
|---|---|---|
| IIT (Tononi) | Φ | Φ as necessary but not sufficient |
| GNWT (Dehaene) | ignition → R | Global ignition as reality-discrimination mechanism |
| FEP (Friston) | dynamics | Free energy minimization drives D toward critical range |
| SOC (Bak) | D | Self-organized criticality as substrate for dynamic complexity |
| HOT (Lau) | R | Higher-order representations enable reality discrimination |
22 falsifiable predictions derived from the model. Full list in repo documentation.
193-paper research corpus spanning integrated information theory, global workspace theory, free energy principle, higher-order theories, and criticality. Full corpus in HIRM repo.
A platform for mapping global corruption by connecting leaked corpora, public records, and researcher findings into a single evidence-backed graph. The graph is not built by extraction. It is built by investigation. Thousands of researchers, each pulling threads from the corpora they know best, each contributing verified connections back to a shared record. The platform provides access to corpora, tools for verification, and a quality pipeline that ensures only defensible claims enter the graph.
Six prior iterations hit the same wall: visualization on top of garbage data. The visualization was never the problem. The data pipeline was. Specifically, the assumption that automated NER extraction could replace human investigative judgment. Tranche inverts that assumption. The system provides the corpora and the verification infrastructure. Humans do the investigating.
| Layer | Function | Implementation |
|---|---|---|
| Local Researcher | Private workspace. Investigation state, draft connections, local notes. Encrypted at rest. | Tauri 2, SQLCipher |
| Corpus Federation | Federated search across global leak databases and public records. No local storage of source corpora. | Oktyv adapters for ICIJ, SEC, FOIA, court records |
| Quality Pipeline | Every submitted connection passes through multi-stage verification, adversarial challenge, and epistemic calibration before entering the graph. | Consensus 13-stage, WHETSTONE, TREG |
| Autonomous Research | Overnight pattern detection. Watches new filings, detects structural anomalies, suggests investigation threads to human researchers. | NIGHTSHIFT, LANTERN |
| Public Graph | Read-only exploration. Search entities, trace connections, read evidence chains. No login required. | Supabase, D3 |
| Tier | Scale | What Emerges |
|---|---|---|
| L0 | Macro topology | Cluster shapes across the full graph. Structure before labels. |
| L1 | Sector clusters | Ownership groups, industry sectors. Leiden community detection. |
| L2 | Entity networks | Companies, funds, individuals. Hubs emerge from data via ForceAtlas2. |
| L3 | Relationship detail | Ownership percentages, trading patterns, capital flows between entities. |
| L4 | Document evidence | Source filings, dates, provenance chain. Every edge traces back to paper. |
| L5 | Claim validation | TESSRYX confidence scoring, dependency blast radius, supersession tracking. |
Six constraints locked from six failed iterations. No bulk NER (automated entity extraction produces noise, not intelligence). No co-occurrence relationships (appearing in the same document is not a relationship). No visualization before data quality (the graph cannot be better than the data underneath it). No multiple pipelines (one path from corpus to graph, or the data diverges). No architecture paralysis (build the pipeline, then the graph, then the interface). No localhost monolith (federated from day one).
Zero-Assumption Law. Patterns emerge from data, never from named entities. The graph is not told who matters. Density, centrality, and bridging structure surface organically. STORM generates synthetic datasets with planted dark network patterns and ground truth catalogs. The detection pipeline is validated against known structure before it touches real data.
A platform that maps corruption will be pressured to stop mapping corruption. Every architectural decision is evaluated against a single question: does this create a single point of failure? Contributor identities are never stored on the server. Private notes never leave the local device. Data exports are cryptographically signed and distributed to IPFS. The federation protocol means any mirror operator can resurrect the full graph independently. The number of people who could rebuild Tranche at any given moment is architecturally unknowable. A privacy policy is a promise. Architecture is a fact.
145+ source documents. Architected for 100K–1M entities from day one. All open-source infrastructure, $0/month operational cost, running locally and outside institutional control.
Most security systems try to keep attackers out. Some detect them when they get in. This one lets them in on purpose - into a version of reality that's cryptographically wrong in every specific detail, structurally identical in every pattern, and mathematically traceable back to the exact breach that let them through.
Instead of building a fake environment with synthetic data (which any insider will recognize as fake), run the real system against the real database, but pass every data point through a keyed distortion layer before it hits the screen. The attacker sees real patterns, real activity volumes, real statistical distributions. Every specific value - every name, every coordinate, every identifier - is wrong. Named after the moiré pattern - the interference effect when two regular patterns overlap at slightly different angles, producing a third pattern that looks structured but doesn't exist in either original.
The distortion is format-preserving encryption (NIST SP 800-38G, FF1/FF3-1). Already used in PCI-DSS payment tokenization. Nobody's applied it as a deception layer. The contribution is not any single primitive - FPE, deception technology, and intelligence tradecraft all predate this work - but their composition into a coherent operational doctrine with explicit security bounds and failure modes.
| Data Type | Method | Preserved | Changed |
|---|---|---|---|
| GPS coordinates | Rigid-body rotation + translation | Relative distances, clustering, corridor shapes | Absolute locations |
| Identifiers | FPE (FF1 mode), keyed bijection | Format, consistency across views | Every specific value |
| Timestamps | Uniform offset from key | Sequence, gaps, temporal clustering | Absolute dates and times |
| Categoricals | Keyed substitution within equivalence class | Category structure | Specific values |
| Aggregates | Pass-through | Everything | Nothing - real numbers |
The paper analyzes what happens when sophisticated adversaries know deception systems exist and actively probe for them. Seven detection techniques are identified - from self-verification (submit a test record, check if it renders correctly) to statistical fingerprinting (test coordinate clustering against road grid alignment) - with specific countermeasures for each. The self-verification attack is the most dangerous and simplest: an insider submits a record they control and checks whether it comes back correctly. This is MOIRÉ's most fundamental limitation.
| Tier | Trigger | Distortion |
|---|---|---|
| 0: Watch | Anomalous behavior | None. Real data. Behavioral telemetry only. |
| 1: Tracer insertion | Elevated suspicion | 5-10 marked records planted. All other data real. |
| 2: Partial | Confirmed compromise | High-value records distorted. Most data real. |
| 3: Full MOIRÉ | Active exfiltration | Everything distorted except insider's own submissions. |
| 4: Contain | Detected or exhausted | Access silently restricted. No deception. |
Against sufficiently sophisticated adversaries, full-environment distortion will eventually be detected. The stable equilibrium converges on selective distortion with tracers - overwhelmingly real data with a small number of strategically placed marked records. This is where intelligence tradecraft has always settled. The deception is thin but invisible.
When a legitimate insider is confirmed compromised, the standard response is to revoke access. This alerts the adversary. The alternative: silently route the compromised insider's session through the distortion layer. Every query reveals their handler's priorities. Every exfiltrated record becomes a uniquely keyed tracer. MITRE Engage acknowledges this gap. No vendor, framework, or academic paper currently formalizes this as a named doctrine. The primitives exist in patented work (Rapid7 US 11,303,675; SentinelOne US 11,038,658). The strategic composition does not.