Make decisions that protect rights, bound catastrophic risk, and account for the whole ripple field.
RippleLogic is the MathGov decision engine: a rights-constrained, ripple-aware ethical operating system for governance, AI alignment, institutional decision-making, and high-stakes public choices.
Structure consequential decisions around rights, worst-case risk, system integrity, public reasons, and a record people can review.
Use the Canon, SGP, ripple.md, Agent System, Aligners Sheet, and release-integrity files as one synchronized package.
Evaluate a clear boundary: Tier 1-3 framework specification is claimable; empirical validation, Tier 4, and ProofPack are future work.
The framework
RippleLogic keeps ethics structured, inspectable, and hard to hide.
It does not replace human judgment, law, democratic authority, or professional responsibility. It makes the decision logic visible so it can be tested, challenged, and improved.
Map stakeholders
Use nested union scopes from Self to Biosphere so affected people, institutions, ecosystems, and future consequences do not disappear.
Apply hard gates
Check rights, catastrophic tail risk, and containment before any aggregate benefit score can influence the decision.
Rank and record
Only surviving options are ranked through the RippleLogic Score, then documented through auditable records and public reasons.
Rights floor
Remove options that violate non-compensatory rights. Protected interests cannot be traded away for aggregate welfare gains.
Tail-risk bound
Remove options with unacceptable catastrophic, irreversible, or lock-in downside. The worst tail matters, not only the average.
System integrity
Prevent a local win from degrading the larger systems, institutions, and life-support conditions it depends on.
Residual welfare
Among surviving options, compare ripple effects across a 7 × 7 welfare matrix of union scopes and welfare dimensions.
Structural tie-break
Use coherence and hollowing-out diagnostics when leading options are effectively tied, then escalate if uncertainty remains.
RippleLogic does not ask, “Which option scores highest?” until it has first asked, “Which options are ethically admissible?”
MathGov system stack
One framework, several synchronized parts.
The current release separates the governing decision architecture, moral-status protocol, assurance wrapper, agent runtime specification, primer, workbook exemplar, and integrity layer.
Canon v10.6
The formal RippleLogic decision architecture: NCRC, TRC, Containment, RLS, UCI/HOI, PCC, tiers, and claim boundaries.
SGP v5.3
The Sentience Gradient Protocol for protection-relevant moral patienthood, with protection kept separate from authority.
ripple.md v3.4
A portable decision-note and audit standard for evidence, reconstructability, falsifiers, and wrapper assurance.
Agent System v10.6
How AI and hybrid agents should be constrained, audited, authenticated, and prevented from unsafe escalation.
Primer v2.4
The clearest first read for non-specialists, teams, reviewers, educators, and implementation partners.
Aligners Sheet v3.4
A spreadsheet exemplar for training and replay practice. It is not a validator, ProofPack, or deployment certification.
Reports
Verification reports, feedback integration records, Markdown conversion notes, and release synchronization summaries.
SHA-256
Manifest and verification files make the release package checkable and resistant to silent file drift.
Current release
v10.6 + SGP v5.3 is live on GitHub.
This is the current public Tier 1-3 core-foundation and synchronized companion release of the MathGov / RippleLogic framework.
Architecture-complete within its declared scope. Ready for public review, implementation pilots, education, tooling development, and independent critique.
Integrity check
From the repository root:
sha256sum -c releases/v10.6_2026-06-05/RELEASE_INTEGRITY/SHA256_MANIFEST_GITHUB_FINAL.txtOffice artifacts were checked for valid OOXML ZIP integrity. DOCX comments and tracked-change markers were scanned. The workbook was imported and formula-error scanned.
Claimable now
- ✓Tier 1-3 framework specification and synchronized companion package.
- ✓Governing Canon v10.6 and SGP v5.3 core pair.
- ✓Auditable release package with manifest, verification reports, and release-support documentation.
- ✓Worked-run spreadsheet exemplar for training, review, and implementation preparation.
Not claimed
- ✕Empirical validation across domains.
- ✕Tier 4 readiness or ProofPack completeness.
- ✕Legal certification, deployment certification, or public-authority approval.
- ✕Completed biological SGP measurement or current-AI sentience.
How to use it
A practical path from reading to implementation.
RippleLogic is designed to be read, reviewed, tested, implemented, challenged, and improved. Start with understanding before trying to automate.
Recommended reading path
- Start with the Foundations Primer for orientation.
- Read the Canon for the governing decision architecture.
- Read SGP for moral-status and protection handling.
- Use ripple.md when writing decision notes or assurance wrappers.
- Use the Agent System when building or evaluating AI/hybrid agents.
- Use the Aligners Sheet only as a worked-run exemplar, not as certification.
High-value first applications
- →Policy analysis where rights, long-term risk, and externalities must be visible.
- →AI governance and agent runtime controls, especially where audit logs and refusal behavior matter.
- →Institutional decisions involving multiple stakeholders, public trust, and difficult tradeoffs.
- →Education and training for systems thinking, ethical reasoning, and transparent decision design.
Next phase
From specification to validation.
The v10.6 package completes the current specification layer. The next work is evidence, tooling, replayability, and independent review.
Replayable evidence
Build schemas, validator logic, reference calculators, replay records, and public test vectors.
Calculators and agents
Develop reference implementations for decision notes, PCC records, scoring surfaces, and agent-control profiles.
Reality testing
Apply the framework in bounded institutional, educational, policy, and AI-governance contexts.
Independent critique
Invite academic, technical, legal, governance, and community review before stronger claims are made.
It is the stable foundation for the next phase: validation, tooling, pilots, public learning, and proof.
