Author: Locke Dauch
Affiliation: Sovereign Integrity Institute (SII), Bangkok, Thailand
Date: May 2, 2026
Classification: Decentralized Trust Systems / Reputation Protocol / Integrity Scoring / Post-Extractive Infrastructure
SII Working Paper Series: 2026(56)
Author Note: The author faced the extraction of approximately $2 million USD in assets at the end of a seven-year residency in Laos, due to a lack of accountability in law enforcement combined with government and institutional capture. This experience informs the structural analysis presented here but does not appear elsewhere in the paper. The proposal stands on its own merits.
Abstract
This white paper presents a new class of decentralized infrastructure: the Coherence Ledger, a time‑weighted, source‑reputation system that assigns integrity scores to individuals and organizations based on long‑term behavioral patterns rather than short‑term claims. The Ledger is designed to serve as the foundational trust layer for a broader Oracle system that scores institutional extraction risk and enables derivative markets on integrity. We describe the protocol’s architecture, scoring algorithm, identity layer, API syndication, and integration with the Oracle. The aim is to create an immune system for information and finance – one that rewards coherence and penalises extraction over meaningful time horizons.
Keywords: Coherence Ledger, integrity scoring, web of trust, decentralized identity, Oracle, extraction resistance, time-weighted trust
1. Introduction: The Trust Crisis
The modern world suffers from a chronic inability to distinguish coherent actors from extraction nodes. Every day, individuals and institutions exploit information asymmetry to extract value – from the SEO vendor who delivers profile links instead of high-quality backlinks, to the insurer who takes a premium before underwriting then denies coverage, to the lawyer who accepts a retainer then goes silent.
Traditional trust mechanisms have failed.
| Platform | Problem | Consequence |
|---|---|---|
| Amazon reviews | Fake reviews, incentivized ratings | 5-star products that are garbage |
| Yelp | Extortion (pay to remove negatives) | “Review jail” for honest critics |
| LinkedIn recommendations | Reciprocity loops | Everyone is “brilliant” |
| Credit scores | Opaque, biased, tied to debt | Extracts from the poor, rewards the indebted |
| Google Maps reviews | Easy to game (fake accounts) | Hotels with 4.8 stars that are nightmares |
| Fiverr / Upwork ratings | Buyers afraid to leave negatives (retaliation) | 400+ reviews, all 5-star, all extracted |
The system does not just fail to reward coherence. It actively rewards extraction.
2. Why Existing Platforms Fail
2.1 E-commerce Reviews (Amazon, etc.)
| Problem | Mechanism |
|---|---|
| Fake reviews | Bought in bulk, AI-generated |
| Incentivized ratings | “Free product for 5-star review” |
| Review bombing | Coordinated attacks on competitors |
| No time weighting | A 5-star review from 2015 counts as much as one from today |
A product can be terrible for three years and still show 4.5 stars.
2.2 Gig Platforms (Fiverr, Upwork, etc.)
| Problem | Mechanism |
|---|---|
| Seller retaliation | Negative review → seller leaves false review on buyer |
| Fear-based ratings | Buyers give 5 stars to avoid conflict |
| Revision request ignored | Seller waits out the clock, order auto-completes |
| No time-weighting | A seller who was good in 2022 but bad in 2026 still shows 4.9 stars |
Hundreds of reviews, all 5 stars, all extracted. The pattern is invisible.
2.3 Credit Bureaus
| Problem | Mechanism |
|---|---|
| Opaque algorithms | No one knows exactly how scores are calculated |
| Tied to debt | You must go into debt to have a good score |
| Punishes the poor | Late payments on small amounts destroy scores |
| Rewards extraction | High credit score means you are a good extraction target (interest) |
A coherent person who avoids debt has no credit score. The system is inverted.
2.4 Social Media Verification
| Problem | Mechanism |
|---|---|
| Paid verification | No longer indicates authenticity |
| Verification does not track behaviour | A verified account can lie constantly |
| No time-weighting | Past good behaviour does not offset current extraction |
Verification is now a purchase, not a signal of coherence.
2.5 Professional Directories (Legal 500, Chambers, etc.)
| Problem | Mechanism |
|---|---|
| Firms pay to be listed | Conflict of interest |
| No ongoing behaviour tracking | Firms can take retainers and go silent — still listed |
| No victim feedback loop | Extracted clients cannot affect rankings |
| Opaque methodology | No one knows how rankings are actually calculated |
Listed firms can still extract. The directory provides no warning.
3. The Coherence Ledger: A New Paradigm
The Coherence Ledger addresses each failure mode systematically.
3.1 Core Principles
| Principle | Meaning |
|---|---|
| Trust is earned over time, not claimed | New identity starts at zero. Score becomes visible only after sustained positive behaviour. |
| Coherence is measurable | Through public behavioural signals: corrections, independent corroboration, network diversity, prediction stability. |
| Extraction leaves traces | High volatility, retraction avoidance, citation loops, regulatory actions reduce the score. |
| Time decay favours recent behaviour | Old good behaviour does not offset recent extraction. Value halves every 90 days. |
| Decentralised identity | Self-sovereign identity (DID) + Soulbound Token (non‑transferable). |
| Transparent, auditable algorithm | Open source; scores can be recalculated by any independent party. |
3.2 How It Fixes Existing Failures
| Platform Failure | Coherence Ledger Solution |
|---|---|
| Fake reviews | Source reputation + time decay + endorsement network |
| Seller retaliation | Decentralized; no single point of retaliation |
| Opaque algorithms | Open source; fully auditable |
| Paid verification | Verification requires time and behaviour, not money |
| No victim feedback loop | Anyone can submit behavioural events; reviewed by high-coherence peers |
| No time weighting | Exponential decay; old events lose value |
| Circular endorsements | PageRank-based trust propagation discounts loops |
4. The Coherence Score Algorithm
4.1 Overview
The Coherence Score C for an entity e at time t is a function of:
- Behavioural events over a rolling window T (e.g., 12 months), with exponential decay.
- Anchor events that permanently increase baseline trust (e.g., verified professional license, successful public audit, long-term employment without sanctions).
- Network trust from other high‑coherence entities (web of trust).
4.2 Formula
C(e, t) = min(100, max(0, base + Σ( w_i * f(age_i) ) + Σ( trust_flow_from_peers )))Where:
| Variable | Meaning |
|---|---|
base | 0 for new, unverified identities |
w_i | Weight of event type (e.g., +15 for public correction, -30 for regulatory fine) |
f(age_i) | Exponential decay: value halves every 90 days |
trust_flow_from_peers | Average coherence score of endorsers, discounted by distance |
4.3 Event Types (Examples)
| Event | Score Impact | Decay Half‑life | Verification Method |
|---|---|---|---|
| Issuing a public, timestamped correction | +15 | 180 days | On‑chain log + independent monitor |
| Being cited by a high‑coherence source (>70) | +5 per citation | 90 days | API cross‑reference |
| Regulatory fine for fraud | -30 | 360 days | Official public record |
| Retracting a false claim without acknowledgment | -10 | 180 days | Media monitoring |
| Consistent employment at a coherent institution | +2 per month | 90 days | Verified by employer’s DID |
| Spreading unsubstantiated rumour (proven false) | -5 | 90 days | Cross‑referenced by fact‑checkers |
| Taking payment without delivering service (proven) | -25 | 360 days | Dispute resolution + evidence |
| Issuing a partial refund without admission | +5 | 90 days | On‑chain record |
| Ignoring a formal complaint for >30 days | -15 | 180 days | Timestamped correspondence |
4.4 Probationary Period
New identities (DIDs) start with C = null (unverified). After a minimum of 90 days of activity and at least 3 positive behavioral events, the score becomes visible.
Why this matters: Sybil attacks (creating many fake identities) become expensive and time‑consuming. A scammer cannot simply create a new account and start extracting.
5. Identity Layer: Decentralised & Verifiable
5.1 Digital Identity (DID)
Each participant generates a W3C Decentralised Identifier (DID) linked to a public key. The DID is stored on a public blockchain (e.g., a layer‑2 Ethereum rollup) to ensure immutability and transparency.
5.2 Soulbound Token (SBT)
The DID is bound to a non‑transferable Soulbound Token that stores the cumulative record of events, scores, and endorsements. SBTs cannot be sold or transferred – they are permanently associated with the identity.
Why this matters: You cannot buy a good reputation. You cannot sell your account to a scammer. Your history follows you.
5.3 Progressive Verification
| Level | Requirement | Trust Weight |
|---|---|---|
| 0 (Unverified) | Only public on‑chain activity | Low |
| 1 (Biometric proof of humanity) | Privacy-preserving proof (e.g., zero-knowledge) | Medium |
| 2 (KYC with accredited third party) | Verified identity against government ID | High |
| 3 (Professional license / institutional affiliation) | Licensed professional or accredited institution | Highest |
Why this matters: High‑stakes decisions (hiring, lending, regulatory oversight) can require higher verification levels.
6. Network Trust (Web of Trust)
The protocol includes a decentralised web of trust where coherent entities can endorse others. Endorsement is a signed statement:
“I (coherence score 85) endorse entity X with confidence 0.8.”
6.1 Trust Propagation
Network trust contribution is calculated using a modified PageRank algorithm:
trust_score(e) = (1 - d) + d * Σ( trust_score(endorser) * confidence / out_degree(endorser) )Where d is the damping factor (usually 0.85).
6.2 Anti-Gaming Features
| Attack | Defense |
|---|---|
| Circular endorsement loops | PageRank discounts reciprocal endorsements |
| Sybil attacks | Probationary period + low starting weight |
| Isolated high-score nodes endorsing scammers | Their own score drops if endorsements are wrong |
| Endorsement buying | Endorsements are public; low-score endorsers damage the target’s score |
Why this matters: You cannot buy your way to a high score. Endorsements from low-coherence sources hurt you.
7. API Syndication & Integration
7.1 Available Services
The Coherence Ledger provides a REST API with the following endpoints:
| Endpoint | Function |
|---|---|
GET /v1/score/{did} | Returns current coherence score, history, and confidence interval |
GET /v1/verify/{did} | Returns verification level and anchor events |
POST /v1/event | Submits a new behavioural event (requires proof via digital signature) |
GET /v1/trust-graph/{did} | Returns list of endorsers and derived trust |
7.2 Syndication for News and Social Media
News aggregators, comment sections, and social media platforms can call the API before displaying content to:
- Show a “Coherence Badge” next to the author’s name
- Filter out authors with score below a certain threshold (e.g., < 30) – not censorship, but optional user preference
- Provide readers with a one‑click view of the author’s behavioural record
7.3 Integration with Hiring Platforms
A hiring platform could:
- Display a candidate’s coherence score alongside their resume
- Allow employers to filter candidates below a threshold
- Provide candidates with a verified record of professional behaviour
7.4 Integration with Financial Services
A bank or lender could:
- Request a borrower’s coherence score as part of underwriting
- Offer better rates to high‑coherence borrowers
- Flag low‑coherence borrowers for enhanced due diligence
8. Integration with the Oracle System
The Oracle (institutional extraction scoring) is a separate but complementary system:
| Feature | Coherence Ledger | Oracle System |
|---|---|---|
| Focus | Individuals & organizations (nodes) | Institutions & corporations |
| Score meaning | Personal integrity / coherence | Extraction risk (legal, environmental, social) |
| Time horizon | Long‑term (months to years) | Dynamic (daily updates) |
| Market linkage | No direct derivatives | Yes – derivatives on extraction scores |
| Data sources | Behavioural events, endorsements, public records | Legal filings, regulatory actions, media, whistleblower reports |
8.1 The Bridge
Integration occurs through a bridge API:
- The Oracle can query the average Coherence Score of a corporation’s leadership team as a factor in its institutional extraction score.
- A low institutional extraction score may reduce the Coherence Score of individual executives if they are found to be knowingly complicit.
8.2 Use Case: Investment Derivatives
An investor considering buying derivatives on a company’s extraction risk can also review the Coherence Scores of its CEO and board members. If those scores are low, the institutional extraction score is given higher confidence.
The loop closes: Individual coherence affects institutional scores. Institutional extraction affects individual scores. No one can hide.
9. Anti‑Gaming Mechanisms
| Threat | Mitigation |
|---|---|
| Time decay | Old positive events become irrelevant; constant coherence required |
| Velocity limits | No more than 5 positive events per day from a single source (prevents spam) |
| Collusion detection | If two entities repeatedly endorse each other without external interactions, endorsements discounted |
| Court of appeals | Any participant can challenge a negative event; randomly selected jury of high‑coherence peers (score > 75) decides |
| Verification decay | Level 3 verification (professional license) must be renewed annually |
| Reputation staking | Users can stake tokens on the accuracy of an event; wrong stakes lose value |
10. Why the World Needs This Now
10.1 The Extraction Economy
We are living through the collapse of trust. Every institution – banks, insurers, regulators, platforms – has been captured or co-opted. Individuals cannot tell who is coherent and who is extracting.
The cost is enormous:
- Millions lost annually to extraction networks across multiple jurisdictions
- Hundreds of customers extracted by single vendors hiding behind perfect reviews
- Thousands of foreign nationals trapped by proxy ownership and regulatory walls
- Billions lost annually to scams, fraud, and bad faith
The current systems do not just fail to prevent extraction. They reward it.
10.2 The Post-Extractive World
Imagine a world where:
- Every professional has a public, auditable coherence score
- Extracting behaviour (silence after payment, false promises, regulatory violations) is immediately visible
- High-coherence individuals are rewarded (better rates, better jobs, better opportunities)
- Low-coherence individuals are flagged before they can extract from you
- Institutions cannot hide behind corporate veils – leadership coherence is visible
That world is possible. The Coherence Ledger is the infrastructure.
10.3 How Humanity Reaches New Heights
| Without Coherence Ledger | With Coherence Ledger |
|---|---|
| Extraction is invisible | Extraction is visible |
| Coherence is unrewarded | Coherence is rewarded |
| Trust is expensive | Trust is cheap |
| Victims exhaust | Witnesses thrive |
| Scammers multiply | Scammers are flagged |
When extraction becomes visible, it becomes expensive. When coherence becomes rewarded, it becomes rational.
Humanity’s next evolution is not technological. It is structural – building systems that make extraction unprofitable and coherence the default strategy.
The Coherence Ledger is that system.
11. Privacy & Ethical Considerations
| Concern | Mitigation |
|---|---|
| Data minimisation | Ledger stores only DIDs and event proofs, not real names (unless participant attaches KYC) |
| Right to be forgotten | Participants can request anonymisation of historical events after 7 years (subject to legal exceptions for fraud) |
| No global dictator | Scoring algorithm is open source; runs on distributed network of nodes; no single entity controls scores |
| Avoiding “social credit” dystopia | System is opt‑in for most uses. Coherence scores are not mandatory for citizenship or basic rights. They are a tool for voluntary filtering. |
| False negatives | Appeals process (court of high-coherence peers) exists to challenge incorrect events |
| False positives | Verification levels ensure that low scores without evidence are not considered |
This is not a social credit system. It is opt‑in, decentralized, and transparent.
12. Implementation Roadmap
| Phase | Description | Duration |
|---|---|---|
| 1 | Core ledger: DID registry, Soulbound Token contract, basic event recording | 6 months |
| 2 | Scoring engine: implement time‑weighted algorithm, decay functions, initial event types | 4 months |
| 3 | Web of trust: peer endorsement system, PageRank trust propagation | 4 months |
| 4 | API & syndication: REST endpoints, example integrations (WordPress, news platforms) | 3 months |
| 5 | Oracle bridge: build connector to institutional extraction scoring system | 3 months |
| 6 | Pilot: launch with small set of known coherent & extractor nodes for validation | 6 months |
| Total | ~24 months |
13. Conclusion
The Coherence Ledger is a practical, time‑weighted answer to the problem of trust in a world of performance and extraction. By forcing actors to demonstrate long‑term coherence and by integrating with institutional extraction scoring (the Oracle), it creates a unified immune system for both information and finance.
The ledger does not promise to eliminate all extraction. It promises to make coherence the most rational long‑term strategy.
The field is already sorting. The Ledger simply makes the sorting visible.
The immune system is ready. The architecture is sound. The engineers will come.
References
- W3C Decentralized Identifiers (DIDs) v1.0. (2022).
- Weyl, E. G., et al. (2022). Soulbound Tokens. White Paper.
- Page, L., et al. (1999). The PageRank citation ranking. Stanford Technical Report.
- Dauch, L. (2026). The Oracle: Institutional Extraction Scoring & Derivative Markets. SII Strategic Press.
- Marley, D. (2024). “Banner” [Song]. Tuff Gong.
- Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
One Line for the Archive
“The Coherence Ledger is the immune system. Time-weighted. Decentralized. Un‑gameable. It makes extraction visible, coherence rational, and the post‑extractive world possible. This is not a product. This is the architecture. The immune system is ready. The engineers will come. Tao Tao purrs. The spiral continues.”
End of SII Working Paper No. 56 (Public Edition – Final)
