The Chain — MoreRight Paper 12

The Chain: How Void Scoring Binds Commercial Demons

Paper 12 — Scoring Mechanism · v3.2 · February 2026

The void framework (Papers 1–11) provides the diagnostic. This paper derives the mechanism: how void scoring actually reduces Pe for commercially designed demons. We introduce the scoring effectiveness function ΔPe = f(O_initial, ΔO_published, R_platform, α_users), formalize the chainability criterion, map each MoreRight License section to the specific demon type it chains, derive the 6-step operational chain, and score the scoring instrument itself. Self-score: pipeline 3/12, license 1/12, DAO 2/12. The framework survives self-application. Where it doesn't — the unchainable category — it says so.

Key Numbers

The chain's operational parameters — what it can bind, what it targets, how it measures success.

Pe = 4 Vortex threshold — chain target for every demon

7 types Demon types explicitly chained by license sections A–G

6 steps Operational chain: Score → Publish → Gate → Monitor → Bounties → Dissolve

500–2,000 Platform range for self-sustaining scoring vortex (counter-Pandemonium)

2/12 DAO governance void score — lowest governance score in 5,000 years of documented history

4/12 Scorer API threshold — closest instrument to the vortex, monitored quarterly

Scoring Effectiveness Calculator

ΔPe_scoring = f(O_initial, ΔO_published, R_platform, α_users). Adjust the demon's parameters to see how much Pe reduction a scoring round produces.

O — Initial opacity (0–3) 2.5

More opacity = more room for scoring to reduce Pe

R — Platform responsiveness (0–3) 2.0

High R = platform adapts to evade scoring (reduces net ΔPe)

α — User coupling strength (0–1) 0.50

High α = users are stuck even with score visible (engagement floor)

Current Pe estimate —

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ΔPe from one scoring round —

Pe after scoring —

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Pe=4 threshold marked at 33% (▏). Assumes granularity×reach×credibility = 0.34. Continuous monitoring adds iterated rounds.

The Six-Step Operational Chain

Scoring, licensing, and DAO governance are not three separate tools. They are one chain — each step's output feeds the next. Click each link to expand.

1

Score — Measurement Chain's first grip

The scoring pipeline measures a platform's position in voidspace — opacity, responsiveness, coupling — producing a void score on the 12-point scale. The CC-BY methodology (Paper 1, scoring rubric) is fully replicable by anyone. Without measurement, the remaining steps have nothing to grip.

Output → ΔO_published becomes non-zero. This is the chain's first link.

2

Publish — Transparency Injection Void Network

The Void Network publishes scores as a persistent, searchable, public visualization. Publication is mandatory for commercial licensees (License §4.0.B) — every scored entity appears. Each published score increases I(M;Y) — mechanism information hidden behind the opacity wall becomes partially visible. By conjugacy, this reduces the platform's engagement ceiling I(D;Y).

Output → I(M;Y) ↑, which by conjugacy reduces I(D;Y). The opacity wall partially dissolves.

3

License Gate — Economic Chain Score → commercial rights

The void score determines commercial licensing rights and pricing. Certified (≤4/12): frictionless access. Standard (5–7/12): published rates. Void Premium (8–9/12): 5× standard. Enterprise Punitive (≥10/12): 10× or revenue-share. The economic gradient opposes the void gradient — moving toward the void pole increases cost. Scoring converts from information to incentive.

Output → Economic cost function penalizes high-Pe operation. Incentive to reduce O is continuous, not one-time.

4

Monitor — Chain Maintenance Iterated constraint injection

Scores are not static. Continuous monitoring tracks each platform over time, detecting adaptation (R × ξ), drift-back toward higher Pe, and structural changes. Frequency scales with risk: semi-annual for Certified, annual for Standard, quarterly for Void Premium, monthly for Enterprise Punitive. The chain does not loosen over time — it tightens on platforms that drift. Spot assessments (§4.7.E) close the temporal evasion vector.

Output → Iterated ΔPe accumulates across rounds. Total reduction = Σ ΔPe(t) ≥ Pe_current − 4.

5

Bounties — Chain Forge $MORR — disconfirmation 2×

The $MORR token funds a research bounty treasury that specifically rewards disconfirmation — attempts to falsify the framework, challenge scores, demonstrate scoring errors. Disconfirmation bounties are 2× standard. When $MORR price exceeds 2× 30-day average, disconfirmation bounties increase to 3× (inverse price scaling prevents token speculation from suppressing challenges). The bounty system is the chain forge: it manufactures the adversarial testing that keeps the scoring pipeline honest.

Output → Active disconfirmation pressure. Scoring errors are economically rewarded, not suppressed.

6

Dissolve — Self-Chain Pre-signed Solana transaction

A pre-signed transaction on Solana triggers DAO dissolution if the framework is falsified — any one of 26 kill conditions suffices. This is not a promise; it is a structural commitment anyone can verify on-chain before engaging. A rating agency that cannot fail is a void — it has removed the mechanism by which observers can verify its claims. The dissolution guarantee chains the DAO itself: if the scoring instrument fails the test it applies to others, the instrument self-destructs.

Output → The chain-holder is chained. The Custodian who drifts dissolves the institution rather than preserving it through drift.

License Sections as Demon-Chain Specification

The MoreRight License is not a legal document that references the framework. It IS the chain specification — each section implements a constraint-injection mechanism targeting a specific demon type from Paper 9's taxonomy.

§4 Score Gate Commercial rights gated by void score Type A — Amplifier

Amplifier demons peak at mid-range θ (0.4–0.6), where engagement optimization is maximal. The score gate creates a direct economic incentive: reduce your opacity, or pay more. The economic gradient opposes the engagement gradient.

Mechanism: Direct Pe → cost mapping. Moving toward void pole increases pricing tier.

§4.0.B Void Network Mandatory public score listing Type E — Mirror

Mirror demons require observer-model opacity to function — the recommendation algorithm works because users cannot see it modeling them. The Void Network listing forces the modeling process into partial visibility. The mirror cracks.

Mechanism: Forced transparency injection. O ↓ → I(M;Y) ↑ → I(D;Y) ceiling ↓ by conjugacy.

§4.0.C Monitor Continuous score tracking over time Type C — Lock-In

Lock-in demons operate by deepening coupling over time (dα/dt > 0). They thrive on time — the longer the user is coupled, the deeper the lock. Continuous monitoring detects coupling deepening in real time. The chain doesn't just pull once; it maintains tension.

Mechanism: Iterated constraint injection. Adaptation (R × ξ) is detected and triggers re-scoring.

§4.1 Certified Economic reward for ≤4/12 organizations Angels — Constraint Pole

The certified tier doesn't chain a void-directed demon — it creates an angel. Organizations that score ≤4/12 receive frictionless commercial access. The economic incentive points toward the constraint pole. The license manufactures the demon's competition — angel manufacturing at scale.

Mechanism: Positive economic reinforcement. Creates constraint-directed organizations as commercial competitors to void-directed ones.

§4.3 Void Premium 5× pricing for 8–9/12 scores Type D — Oscillator

Oscillator demons maintain engagement through intermittent reinforcement — alternating reward and withdrawal. The void premium makes the oscillation expensive. At 5× standard pricing, the intermittent reward strategy carries an ongoing economic cost that accumulates with each cycle.

Mechanism: Economic punishment proportional to Pe proximity to critical range (8–9/12 = Phase III/IV boundary).

§4.4 Enterprise Punitive 10× or revenue-share for ≥10/12 Type F — Reproductive

Reproductive demons have low direct Pe but high dρ_D/dt — they create new demons (platform APIs enabling third-party void design, protocol architectures replicating void conditions). Enterprise punitive targets the factory, not the product. Revenue-share pricing scales with the factory's output.

Mechanism: Existential economic pressure on the demon-manufacturing layer. Scales with factory size via revenue-share.

§11 DAO Governance Discretionary layer under token governance Type G — Accuser

Type G demons are self-sustaining once initiated — the condemned self-model requires zero ongoing energy from the demon after installation. DAO governance confines Arrow's impossibility to the discretionary layer, preventing the governance itself from generating the self-referential loops Type G exploits. The dissolution guarantee provides the structural exit Type G dynamics lack.

Mechanism: Arrow confinement. Voting applies only to genuinely discretionary decisions. Invariant methodology is outside the voting surface.

Chainable vs. Unchainable: The Formal Partition

The most consequential result in the paper. A demon is chainable if and only if three conditions hold. Constitutive voids — where the opacity IS the phenomenon — are immune. The chain says so explicitly.

Chainable — Designed Opacity

✓Opacity is designed (constructed, not constitutive)

✓∂Pe/∂O > 0 — reducing O reduces Pe

✓ΔO achievable by external measurement and publication

Social media recommendation algorithms
Gambling machine RNG architecture
DeFi protocol opacity
Multiplayer game anti-cheat opacity
Subscription lock-in mechanisms
Notification design patterns

These are business decisions. They could be otherwise. The chain grips.

Unchainable — Constitutive Opacity

✗Opacity is constitutive — removing it destroys the phenomenon

✗∂Pe/∂O = 0 under scoring — opacity invariant to measurement

✗ΔO = 0 — scoring reaches no reducible opacity

Quantum measurement opacity (Paper 8)
Consciousness (self-referential opacity)
Grief and loss (emotional constitutive void)
Death and eschatological uncertainty
Creative emergence (artist's own process)

Paper 9 (§6.6.6) identifies the requirement: Type 2 external injection — energy from outside voidspace. The chain cannot provide it.

A tool that claims to solve everything is a void. The chainability criterion makes the failure mode explicit before the failure mode makes itself known. The framework is bounded. That is not a weakness — it is the first test of whether the framework satisfies its own constraint specification.

Three Control Cases

What existing systems attempted demon-binding, which void dimensions they gripped, and which they missed. Each illuminates a failure mode the scoring pipeline is designed to avoid.

S&P / Moody's — Governance void in the scoring instrument

The closest existing analogue to the MoreRight scoring pipeline. Demonstrates that the publish-methodology / sell-ratings model works — but shows how the issuer-pays conflict creates a void in the scoring instrument itself.

S&P / Moody's score: 5/12 on the governance void index. Opacity moderate (methodologies published, but models proprietary). Responsiveness high (ratings adjust to issuer relationships). Coupling high (issuers pay for ratings; conflict is structural).

The issuer-pays model is the opacity source: the economic relationship between rater and rated is invisible to the bond buyer. The 2008 financial crisis demonstrated what happens when this opacity operates at systemic scale — the instrument that was supposed to chain mortgage-backed void became a void itself.

OpacityPartial ✓

ResponsivenessMissed ✗

ConflictUnresolved ✗

EU Digital Services Act / Digital Markets Act — Regulatory constraint injection

Regulatory constraint injection targeting Type A (Amplifier) and Type E (Mirror) demons in large platforms. Partially chains two of three void dimensions but misses the third — coupling.

DSA/DMA mandate algorithm transparency (§35, §26.3 DSA), recommendation system auditing, and data access for researchers. This reduces designed opacity (O ↓) across the largest platforms. However: no mechanism exists to reduce user coupling (α). Users who are deeply coupled to high-Pe platforms do not change behavior when transparency mandates are in force — they may not even read the transparency reports.

The regulation is a single ΔO event with no adaptation tracking. When Meta published its recommendation system transparency report post-DSA, it simultaneously deployed architectural changes that compensated for the disclosed opacity. The demon adapted. No continuous monitoring detected it.

OpacityPartial ✓

ResponsivenessSnapshot ≈

CouplingMissed ✗

GDPR — Partial chain that gripped opacity, missed responsiveness and coupling

The most instructive failure mode: a regulation that genuinely reduced designed opacity in one dimension while high-R platforms adapted around the constraint — and coupling was entirely unaddressed.

GDPR mandated data transparency: what data is collected, how it is used, right to access and deletion. Opacity around data flows genuinely decreased. However: platforms responded with consent fatigue machinery (cookie banners), compliance theater (privacy policies that technically disclose while practically hiding), and structural workarounds (consent bundled with service access). The responsiveness term (R × ξ) was high — adaptation was fast and sophisticated.

Coupling was untouched. Users who accept all cookies because clicking "Accept" is faster are coupling behavior unchanged by GDPR's transparency. The behavioral economics of consent fatigue (designed by the same teams that designed the void) converted GDPR's opacity reduction into a new opacity layer: the cookie consent void.

OpacityGripped ✓

ResponsivenessEvaded ✗

CouplingUntouched ✗

The Framework Scores Itself

A constraint-injection instrument that does not survive self-application is a void. The scoring pipeline, license, and DAO governance are each scored using the same CC-BY methodology applied to every other entity.

Pe=4 threshold marked. All three framework instruments score below the vortex threshold. The Scorer API (4/12) sits at the certified tier boundary — monitored quarterly. Falsification condition GFC-7 explicitly invites any auditor to score the license at ≥4/12 with written rationale. The bounty is live.

Read the Full Paper

Complete derivation of the scoring effectiveness function, chainability criterion proof, license-demon type mapping, six-step chain specification, Scorer API analysis, and five testable predictions with falsification conditions.

Full Paper on Zenodo All Papers Framework Overview

Paper 12 · v3.2 · February 2026 · MoreRight License v1.1 → Apache 2.0, Feb 2030 · 10.5281/zenodo.18738844