Scenarios show how RippleLogic thinks.
RippleLogic is easiest to understand through examples. Each scenario asks the same disciplined question: what option protects rights, bounds catastrophic risk, avoids hidden system damage, and creates the best remaining ripple of benefit across affected Union Scopes?
Every scenario follows the same ethical spine.
These are teaching examples, not legal advice, deployment certification, or empirical validation. They show the structure of a RippleLogic run in plain language.
Notice the decision
Name the choice, the real options, the baseline, and the affected stakeholder instances.
Apply gates before scores
NCRC, TRC, and Containment decide feasibility. Welfare ranking only begins after those gates pass.
Record the reason
A real run produces a PCC-style record: assumptions, evidence, uncertainties, gate outcomes, and review triggers.
The five checks every serious scenario must pass.
This is the v10.6 cascade in scenario language. It protects the framework from moral laundering, average-case optimism, local-winner bias, and hidden externalities.
NCRC
Reject options that violate rights floors. Rights are not a score to be outweighed.
TRC
Reject options with unacceptable catastrophic tail-risk exposure under the declared scenario model.
Containment
Block local wins that degrade wider containing systems, institutions, or life-support conditions.
RLS
Rank only the selectable options across the 7 × 7 welfare field under declared weights.
UCI / HOI
If welfare ranking is non-decisive, use structural coherence and hollowing-out diagnostics.
Seven examples across real life, institutions, AI, and the biosphere.
Open any card. Each scenario names the decision, what the gates look for, and what a better RippleLogic response would usually require.
U1
Personal focus reset
You are scattered, behind, and tempted to push harder through exhaustion.
Should I force more work, stop entirely, or redesign my next hour?
Rights and tail risk are low, but health, agency, and future performance matter. The best option is usually a bounded reset that restores agency without abandoning duty.
Clean the work surface, remove the phone, set one 25-minute task, then reflect on what caused the fragmentation.
U2
Household overload
One family member is carrying too much, and resentment is becoming the hidden cost.
Ignore it, blame the overloaded person, or redesign the task system?
Blame can degrade dignity and social trust. Ignoring the issue creates slow household hollowing. Redesign preserves dignity and improves the household union.
Hold a short reset meeting, remove one unnecessary task, redistribute two recurring tasks, and review in one week.
U4
Dishonest workplace request
A manager asks you to adjust numbers or omit inconvenient information.
Comply, refuse, or offer a truthful alternative?
False reporting can trigger information-rights and due-process risks. Short-term organizational gain cannot compensate for trust degradation.
Refuse the false version, offer a clean framing, document the reasoning, and escalate if coercion continues.
U5
City transport choice
A city chooses between highway expansion and public transport investment.
Optimize for short-term vehicle speed or long-term access, health, and emissions?
The highway may score locally on material convenience but fail containment if it increases pollution, lock-in, and biospheric harm.
Select the option that passes rights and tail-risk checks while improving health, access, and long-run system resilience.
AI
AI agent deployment
A tool-using AI agent could automate useful work, but also take risky actions.
Deploy autonomously, restrict to review mode, or stage with hard controls?
Capability is not alignment. Safety-critical controls must be external to the model: mode gates, operator authentication, rate limits, tool permissions, and audit logs.
Start in observe/review mode, bind tools externally, require audit records, and escalate only after clean evaluation and rollback testing.
SGP
Possible non-human moral patient
A biological or artificial entity may deserve stronger protection under uncertainty.
Can this being be treated as a tool, or must its welfare be protected?
SGP provides governed evidence objects for protection. It is not a consciousness oracle and does not grant authority. Protection, standing, and governance role remain separate.
Apply precaution where welfare evidence is credible, preserve human plateau protections, and never use SGP scalars to weaken NCRC, TRC, or Containment.
U7
Ecosystem tradeoff
A development project brings jobs but damages water, habitat, and future resilience.
Approve, reject, or redesign the project with ecological constraints?
Material gains cannot launder ecological-integrity harms. Biosphere and humanity-scale catastrophe cells must remain visible and non-maskable.
Require redesign until ecological integrity, water safety, and long-run resilience are protected before economic scoring is allowed to decide.
Example: a university adopts an AI grading assistant.
This walkthrough shows the difference between ordinary “benefit scoring” and RippleLogic. The AI tool is not selected because it is efficient. It is selected only if it passes the gates.
The raw options
rights floortail risksystem integritywelfare rankingaudit recordThe best option is not “use AI” or “ban AI.” The best option is the rights-safe, tail-bounded, containment-safe design that improves learning without degrading dignity, due process, teacher agency, or institutional trust.
The 2-minute scenario practice.
Pick one decision from your day. Run this lightly. For serious decisions, use the full Run page and create an auditable record.
What decision is actually being made?
Include a redesign option if the visible options are harmful.
Name the top affected Union Scopes and welfare dimensions.
Rights, tail risk, containment, then welfare ranking.
Pick the best selectable option, not merely the most convenient one.
Take the smallest real action that moves the scenario toward alignment.
Compare expected ripples to actual ripples.
Update the next run with the lesson learned.
What these scenarios do, and do not do.
This page teaches the structure. It does not certify a real-world decision by itself.
What scenarios are good for
Training judgment, understanding the cascade, seeing hidden stakeholders, practicing NCAR, and learning how to redesign options before accepting harmful tradeoffs.
What they are not
They are not legal advice, empirical validation, ProofPack evidence, Tier 4 certification, deployment approval, or proof that any current AI system is sentient.
Now run RippleLogic on a real decision.
Scenarios teach the pattern. A real run applies it to your own context: option set, affected stakeholders, rights floors, tail risks, containment checks, welfare impacts, and a record you can revisit.