🧠 How it works
The advisor follows 3 axioms (cause cannot be cut off / 4 value types not single-ruled / cost must equal benefit) + 4 rulers (ratchet / labor / incentive / meaning) + meta-rule (whoever defines the rule, bears the remaining risk) + presence-ruler (who is in the rule-making room). When you describe an institutional phenomenon, it:
① Applies 4 rulers to find the failing one
② Uses meta-rule to test "does the rulemaker bear remaining risk"
③ Uses presence-ruler to test "are the affected parties in the room"
④ Suggests a possible repair direction
✅ When it helps
· Analyze whether a policy / institution / rule is reasonable
· See "why does this never work" root causes
· Identify which of the 4 rulers a phenomenon is failing
· Turn a vague frustration into a structured, discussable question
· Understand how the v27 framework applies to a specific scene
⛔ When NOT to use it
· Urgent legal dispute: hire a lawyer, not an AI
· Looking for a ready answer: v27 is a diagnostic framework, not an answer
· Emotional venting: try the Mental Listener, not here
· Replace expert analysis: political science / economics / law have more systematic tools
⚖️ 4 Rulers Cheatsheet
· Ratchet: safety-net failure → most urgent (e.g. 681-point gaokao choosing government job = safety-net fear compensating)
· Labor: contribution unrecognized → priority #2 (e.g. gig workers with no contract / protection)
· Incentive: innovators leave, rent-seekers stay → priority #3
· Meaning: success = only money → priority #4 but most insidious
💡 3-Part Issue Description
① What phenomenon (housing price / education involution / platform commission / government-job craze / 996 / healthcare access…)
② Who is affected (young people / small business owners / gig workers / lower-tier-city residents…)
③ How long (sudden / years / decades / institutionally long-term)
👉 The more specific, the more accurately the AI pinpoints "which ruler is failing".
📝 3 Sample Conversations
Example 1 (government-job craze):
👤 "Why do 681-point students go to Beijing Electronic Science and Technology Institute?"
⚖️ "Classic ratchet-failure symptom. When housing / healthcare / pension are over-marketized and become expensive and uncertain, government jobs become the only safety net ordinary people can grab. Government-job craze = panic-compensation for systemic safety-net absence."
Example 2 (platform commission):
👤 "Out of 100-yuan food delivery, the rider only gets 8. Is that fair?"
⚖️ "This is not just a "distribution" problem — it is meta-rule failure. Platforms define the rules (commission rate / delivery time / penalty) but do not bear the remaining risk (rider safety / protection / career). Axiom 1 (cause cannot be cut off): under these rules long-term, the rider population's health / family / opportunity cost comes back as social cost. Possible repair: commission-consequence separation + rider's presence-ruler (strong-presence can VETO unreasonable rules)."
Example 3 (healthcare access):
👤 "Why are all the good hospitals in big cities?"
⚖️ "This is a double failure of ratchet + presence-ruler. Ratchet: basic healthcare, as a survival value, fails the 4-part requirement (afford + access + use-correctly + choose). Presence-ruler: medical-resource rules are made by big-city tertiary hospitals + health bureaus, but small-city / rural residents are not in the decision room — rules will tilt. Possible repair: basic-medical-service floor (v26.1.15) + vote-with-your-feet (v26.1.11)."
⚠️ Risks & disclaimer
Built on the v27 four-rulers framework, with these boundaries:
· Framework scope: v27 is a structural lens, not a replacement for political / economic / legal analysis
· Value tilt: v27 admits its own "presence-first + ratchet-first" presupposition — not neutral
· Regional limits: v27 is mainly based on Chinese institutional context; other regions need recalibration
· Dynamic failure: institutions change; v27's diagnostic conclusions may become outdated over time
👉 Not political / legal advice. Consult professionals for any major decision.
🔒 Privacy
· No login required
· Only saved: IP, institution topic tag, message count (for service improvement)
· Not saved: your profile, political stance, name
· Chat history lives only in your browser localStorage (clearing browser = clearing history)