Structural Dynamics Model: Reproducible Structural Analysis via Four-Layer AI Collaboration

Reproducible reasoning infrastructure for structural constraint analysis

Feed it any domain—elections, tax law, protocols, theology, algorithms—and get back formal classification and falsifiable predictions in 2-6 minutes. Every transformation is specified. Every stage is reproducible.

$ ./compile "HOA architectural covenants"

[Layer 1] Gemini executes constraint_story_generation_prompt.md (60s)
         → hoa_covenants.pl (complete Prolog model)

[Layer 2] Prolog executes validation_suite.pl (<10s)
         → Classification: tangled_rope
         → Action: reform
         → Signature: constructed_constraint
         → Gap: individual sees "none", institutional sees "rope"

[Layer 3] Claude executes UKE_W v1.0 protocol (2-5min)
         → Evidence: 70% new construction, 5-6% value premium
         → Prediction: Legislative caps will expand
         → Omegas: property_value_correlation, board_motivation
  
✓ Complete: hoa_analysis.md

Tested on 80+ domains. Same specifications analyze Colombian elections, Blackstone tax structures, medieval church hegemony, cryptocurrency protocols, and Shannon entropy limits.

---

What This Does

Traditional constraint analysis:

• Weeks of expert research
• Domain-specific methodology
• Descriptive narratives
• Few falsifiable predictions

This compiler:

• 2-6 minutes total execution
• Domain-agnostic specifications
• Formal structural models
• Explicit falsification criteria

The difference isn't just speed—it's reproducibility. Every transformation has a published specification. You can verify the process, not just the output.

---

Four-Layer Architecture

Layer 0: Human Framework Design

Designed reasoning infrastructure:

Deferential Realism (DR) - Core epistemology

• Five constraint types (Mountain/Rope/Tangled Rope/Noose/Zombie)
• Indexical relativity (WHO/WHEN/WHERE/HOW MUCH)
• Omega variables (irreducible reasoning blockers: Ω_E/Ω_C/Ω_P)
• Multi-level analysis (structural/organizational/class/individual)
• Structural signatures (natural law vs. constructed constraint)

Prolog Validation System - Logical infrastructure

• Auto-repair engine (imputes missing values)
• DR-Audit suite (computes classifications, detects gaps)
• Ontological fraud detection (tests claimed vs. actual types)
• Perspectival gap analysis (identifies indexical conflicts)
• Temporal gradient computation (predicts transformations)

Compilation Specifications - Formal protocols

• 60-page Prolog generation prompt (Layer 1 spec)
• UKE_W v1.0 essay synthesis protocol (Layer 3 spec)
• Quality gates and validation criteria

Layer 1: Structural Extraction (Gemini, ~60 seconds)

Executes: constraint_story_generation_prompt.md (60 pages) Formats: contraint_story_template.pl

Actions:

1. Provide both files to LLM and any additional information, such as file, problem description, etc.
2. LLM produces outut, which you save to ./prolog/testsets/ directory.

Input: Natural language domain description
Process: LLM converts unstructured knowledge → formal Prolog model
Output: Complete constraint story file containing:

• Narrative context (commented domain description)
• Base properties (extractiveness, suppression, enforcement scores)
• Indexed classifications (3+ perspectives with different types)
• Beneficiaries and victims (asymmetry detection)
• Test suite (demonstrates indexical variance)
• Model commentary (LLM's reasoning process)
• Omega variables (identified reasoning blockers)
• Alternative analysis (suppressed options)

Example outputs:

• columbia_2026_elections.pl - Election constraint topology
• blackstone_carried_interest_taxation.pl - Tax engineering analysis
• medieval_church_hegemony.pl - Historical institutional power
• ergo_storage_rent_mechanism.pl - Cryptocurrency protocol design
• kjv_linguistic_residue.pl - Translation effects on culture

Key feature: Auto-generates test suite proving indexical variance (same constraint → different types from different positions)

Layer 2: Logical Validation (Prolog, <10 seconds)

Actions:

1. Run: python domain_priors.py in ./prolog/testsets/
2. Add list to the end of domain_priors.p1 in ./prolog/
3. Run python validation_test_suite.py in ./prolog/ (if running everything in testsets)
4. Bash: swifl
5. Swifl: [v3_1_stack].
6. Swifl: [validation_suite]. or
7. Swifl: `run_scenario('./testsets/filename.pl', filename).

Input: Prolog model from Layer 1 (may contain syntax errors)
Process: Seven-step audit protocol

1. Auto-repair missing measurements (neutral 0.5 priors for novel domains)
2. Verify data integrity (schema validation)
3. Compute system gradients (temporal dynamics)
4. Ontological audit (claimed vs. actual type detection)
5. Extract Omega variables (reasoning blocker identification)
6. Generate executive summary (classifications, actions, signatures)
7. Produce LLM refinement manifest (gaps requiring human review)

Output: DR-Audit Executive Summary containing:

[CONSTRAINT INVENTORY: REALITY AUDIT]
Constraint | Claimed | Actual | Action
---------------------------------------------------------------
hoa_architectural_covenants | legal_contract | tangled_rope | reform

[STRUCTURAL SIGNATURE ANALYSIS]
hoa_architectural_covenants: constructed_constraint (confidence: medium)
→ Active enforcement (suppression=0.80, resistance=0.50)
→ Institutionally maintained rule requiring ongoing energy

[PERSPECTIVAL GAP ANALYSIS]
Analysis for: hoa_architectural_covenants
- Individual (Powerless): none
- Institutional (Manager): rope
→ Gap detected: Different agents experience different constraint types

Key feature: Detects ontological fraud (constraints claimed as "natural" that show constructed signatures)

Layer 3: Narrative Synthesis (Claude, 2-5 minutes)

1. Give Claude Prolog Input File, Prolog Output, other model input (Perplexity search) and uke_w
2. Claude generates report

Executes: UKE_W v1.0 protocol

Input: Layer 2 audit summary + web search results
Process: Evidence-anchored interpretation following formal specification

UKE_W Protocol Components:

§1. Substrate Alchemy - Transform evidence → argument

• Counterfactual test (every claim must be falsifiable)
• Omega collapse (warranted uncertainties → defensible assertions)
• Stakes anchor (why this matters beyond being true)

§2. Voice Architecture - Select presentation mode

• System Architect (technical/policy, warm temperature)
• Critical Essayist (cultural/intellectual, variable temperature)
• Temperature modulation (hot/warm/cool based on target)

§3. Structural Operations - Essay construction

• Knife sentence (sharp opening claim from substrate)
• Evidence ladder (concrete → pattern → argument)
• Anti-conclusion (escalation/reversal/indictment, not summary)

§6. Danger Management - Quality control

• Credibility ledger (limited budget for high-risk claims)
• Substrate gate (never invent claims evidence doesn't support)
• Enthusiasm as contrarianism (praise only when defending neglected)

§7. Quality Gates - Self-correction

• Simplicity gate (don't invent complexity when substrate shows simplicity)
• Counterfactual pass (can you state what would disprove this?)
• Stakes test (specific + consequential, not vague + trivial)

Output: Complete constraint story with:

[UKE_META]
protocol: UKE_W v1.0
substrate: hoa_covenants.pl | DR-Audit Executive Summary
voice: Critical Essayist
mode: sharp
temperature: hot

[COLLAPSED_UNCERTAINTIES]
Ω_property_value_correlation collapsed to: "5-6% premium functions as ransom"
Evidence: Cato Institute data, NAR 2024 study
Justification: Statistical reality allows coordination claim despite extraction

[KNIFE_SENTENCE]
"The modern HOA has evolved from voluntary coordination tool into inescapable 
layer of privatized governance treating 'home' as temporary lease on 
community-standardized asset."

[CONTENT]
{Essay with evidence, indexical classifications, temporal predictions}

[STAKES_ANCHOR]
Pattern: Normalization of coordination failure as American baseline
Harm: Autonomy surrendered for 5% wealth premium
Propagation: If this spreads, "home" becomes monitored asset

[QUALITY_GATES]
Substrate Fidelity: ✓ All claims trace to audit/search
Counterfactual Test: ✓ Falsifiable predictions stated
Stakes: ✓ Specific (legislative caps) + Consequential (autonomy erosion)

[SUBSTRATE_TRACEABILITY]
- "70% new construction": NAR 2024, TownSq survey
- "5-6% premium": Cato Institute analysis
- "89% violation software": TownSq 2026 Industry Survey

Key feature: Every major claim traces to substrate evidence. Interpretations marked as interpretations. Predictions explicitly falsifiable.

---

Why This Works: Formal Specifications

The Compilation Metaphor

Layer 1 = Parser (unstructured domain → formal AST)
Layer 2 = Semantic analyzer (validate, optimize, detect errors)
Layer 3 = Code generator (emit human-readable output)

What Makes It Reproducible

1. Published Specifications

   • Layer 1: 60-page prompt template (constraint_story_generation_prompt.md)
   • Layer 2: Prolog validation logic (validation_suite.pl, drl_core.pl)
   • Layer 3: UKE_W protocol (uke_w.md)

2. Explicit Constraints

   • Layer 1 must generate valid Prolog (checked by Layer 2)
   • Layer 2 must output audit summary (consumed by Layer 3)
   • Layer 3 must trace claims to substrate (quality gates enforce)

3. Testable Outputs

   • Prolog models include test suites (prove indexical variance)
   • Essays include falsification criteria (predictions can be proven wrong)
   • All 80+ examples available for inspection

Human Role

You are not "prompting AI"—you are orchestrating a reasoning pipeline:

• Design framework (constraint taxonomy, indexical logic)
• Write specifications (60-page prompts, validation rules, synthesis protocols)
• Validate outputs (check substrate fidelity, verify logic, track predictions)
• Refine system (update specs based on failures, add quality gates)

The AIs execute your specifications. You ensure correctness.

---

Repository Structure

structural_dynamics_model/
├── README.md
├── constraint_bridge.pl
├── constraint_indexing.pl
├── constraint_instances.pl
├── data_verification.pl
├── domain_priors.pl
├── drl_core.pl
├── drl_modal_logic.pl
├── intent_engine.pl
├── narrative_ontology.pl
├── pattern_analysis.pl
├── python_build_suite.py
├── python_test_suite.py
├── report_generator.pl
├── scenario_manager.pl
├── structural_signatures.pl
├── test_harness.pl
├── test_index.pl
├── testsets
│   ├── 26usc469.pl
│   ├── 26usc469_real_estate_exemption.pl
│   ├── ai_evaluators_matching.pl
│   ├── automatic_enrollment_defaults.pl
│   ├── biological_curiosity.pl
│   ├── blackstone_carried_interest_taxation.pl
│   ├── blackstone_conflicts_of_interest.pl
│   ├── blackstone_smd_control.pl
│   ├── blackstone_tax_receiveable_agreement.pl
│   ├── carbon_credit_markets_2026.pl
│   ├── choice_architecture_design.pl
│   ├── cloudflare_dual_class_asymmetry.pl
│   ├── coinbase_crypto_volatility.pl
│   ├── coinbase_regulatory_uncertainty.pl
│   ├── college_admissions_market.pl
│   ├── columbia_2026_elections - Copy.pl:Zone.Identifier
│   ├── columbia_2026_elections.pl
│   ├── copyleft_viral_licensing.pl
│   ├── copyright_protection.pl
│   ├── couples_residency_match.pl
│   ├── creative_commons_licensing.pl
│   ├── dark_patterns_manipulation.pl
│   ├── dead_sea_effect.md
│   ├── deferential_realism_core.pl
│   ├── domain_priors.py
│   ├── ergo_advanced_mechanisms.pl
│   ├── ergo_autolykos_asic_resistance.pl
│   ├── ergo_dexy_gold_protocol.pl
│   ├── ergo_lets_protocol.pl
│   ├── ergo_mixer_protocol.pl
│   ├── ergo_nipopows.pl
│   ├── ergo_rosen_bridge_protocol.pl
│   ├── ergo_sig_usd_protocol.pl
│   ├── ergo_storage_rent_mechanism.pl
│   ├── exploration_vs_exploitation.pl
│   ├── fair_use_doctrine.pl
│   ├── fix_citations.sh
│   ├── gale_shapley.pl
│   ├── genetic_algorithms_evolution.pl
│   ├── gita_kurukshetra.pl
│   ├── golden_handcuffs.pl
│   ├── hamiltonian_path_complexity.pl
│   ├── hammurabi.pl
│   ├── heuristic_optimization.pl
│   ├── hoa_covenants.pl
│   ├── information_foraging_theory.pl
│   ├── institutional_mutation_domestication.pl
│   ├── kidney_exchange_market.pl
│   ├── kjv_great_awakening.pl
│   ├── kjv_linguistic_residue.pl
│   ├── kjv_puritan_new_world_exit.pl
│   ├── kjv_textual_authority.pl
│   ├── lehman_repo_105.pl
│   ├── local_vs_global_optima.pl
│   ├── marriage_problem.pl
│   ├── matching_markets_general.pl
│   ├── max_flow.pl
│   ├── medical_residency_match.pl
│   ├── medieval_church_hegomony.pl
│   ├── non_compete_agreements.pl
│   ├── permissive_software_licensing.pl
│   ├── public_domain_commons.pl
│   ├── relativity_of_simultaneity.pl
│   ├── relativity_physical_invariance.pl
│   ├── rfc9293_interoperability.pl
│   ├── rfc9293_state_machine.pl
│   ├── rotation_seven_black_soil.pl
│   ├── rotation_seven_isolation.pl
│   ├── rotation_seven_kubo_ranking.pl
│   ├── s1_airbnb.pl
│   ├── s1_visa.pl
│   ├── s1_visa_judgment_sharing_agreement.pl
│   ├── shannon_entropy_limit.pl
│   ├── silicon_lexicon_overload.pl
│   ├── skills_based_hiring.pl
│   ├── sludge_bureaucratic_friction.pl
│   ├── sts86_ascent_checklist.pl
│   ├── trade_secret_law.pl
│   ├── traveling_salesperson_problem.pl
│   └── validator.py
├── uke_dr_bridge.pl
├── v3_1_coercion_projection.pl
├── v3_1_config.pl
├── v3_1_data_repair.pl
├── v3_1_stack.pl
├── v_3_2_signature_config.pl
└── validation_suite.pl
│
├── prompts/
│   └── constraint_story_generation_prompt.md  # Layer 1 specification (60 pages)
│
├── protocols/
│   └── uke_w.md                       # Layer 3 specification (UKE_W v1.0)
│
├── python/
│   ├── validator.py                   # Pipeline orchestration
│   └── domain_priors.py               # Prior extraction utilities
│
├── scripts/
│   └── [Shell utilities for batch processing]
│
├── docs/
│   ├── deferential_realism_core.md    # DR framework documentation
│   ├── omega_variables.md             # Reasoning blocker taxonomy
│   ├── indexical_relativity.md        # Multi-perspective theory
│   └── [Additional documentation]
│
└── examples/
    └── [Compiled analysis essays from Layer 3]

---

Evidence: 80+ Compiled Domains

The compiler has been tested on radically different constraint types:

Finance & Economics

• blackstone_carried_interest_taxation.pl - Tax engineering via partnership structure
• blackstone_conflicts_of_interest.pl - Dual mandate extraction
• lehman_repo_105.pl - Accounting fraud mechanics
• coinbase_regulatory_uncertainty.pl - Crypto compliance constraints
• carbon_credit_markets_2026.pl - Emissions trading dynamics
• golden_handcuffs.pl - Retention through unvested equity

Governance & Law

• columbia_2026_elections.pl - Electoral system constraint topology
• hoa_covenants.pl - Homeowner association governance
• non_compete_agreements.pl - Post-employment restrictions
• 26usc469.pl - US tax code passive loss rules
• 26usc469_real_estate_exemption.pl - Tax shelter mechanics
• automatic_enrollment_defaults.pl - Choice architecture defaults

Protocols & Standards

• ergo_storage_rent_mechanism.pl - Blockchain state bloat prevention
• ergo_nipopows.pl - Non-interactive proof of proof-of-work
• ergo_mixer_protocol.pl - Transaction privacy via mixing
• ergo_sig_usd_protocol.pl - Algorithmic stablecoin design
• rfc9293_state_machine.pl - TCP connection state transitions
• copyright_protection.pl - Intellectual property constraints
• creative_commons_licensing.pl - Permissive copyright alternatives

History & Religion

• medieval_church_hegemony.pl - Institutional power maintenance (1200 CE)
• kjv_linguistic_residue.pl - Translation choices → cultural effects
• kjv_puritan_new_world_exit.pl - Exit options via colonization
• hammurabi.pl - Ancient legal code constraint structure
• gita_kurukshetra.pl - Dharma vs. duty in Bhagavad Gita

Algorithms & Theory

• gale_shapley.pl - Stable matching algorithm
• medical_residency_match.pl - NRMP constraint topology
• kidney_exchange_market.pl - Paired donation mechanics
• couples_residency_match.pl - Joint matching constraints
• traveling_salesperson_problem.pl - Computational complexity
• hamiltonian_path_complexity.pl - NP-completeness structure
• shannon_entropy_limit.pl - Information theory boundaries

Organizational Dynamics

• dead_sea_effect.md - Talent drainage patterns
• institutional_mutation_domestication.pl - How orgs coopt movements
• sludge_bureaucratic_friction.pl - Administrative burden as control
• dark_patterns_manipulation.pl - UI design for exploitation
• skills_based_hiring.pl - Credential inflation dynamics

Each domain:

• Compiles in ~60 seconds (Layer 1)
• Validates in <10 seconds (Layer 2)
• Full essay in 2-5 minutes (Layer 3)

---

Falsifiable Predictions

Colombia 2026 Election (Generated January 2026)

Model predicts forced binary convergence:

Will fail if:

• Fajardo breaks 15% first-round vote share
• Undecided bloc stabilizes above 20%
• Traditional conservative consolidates (Cabal/Holguín >20% combined)
• Petro approval recovers above 45%

Will succeed if:

• First round: <4 point margin between top two candidates
• Runoff: Left continuity vs. right restoration
• Centrist space remains suppressed (<12%)
• Winner governs with 50-52% facing hostile opposition

Falsification dates:

• May 31, 2026 (first round)
• June 28, 2026 (runoff if needed)

Structural claim: Constitutional term limit (Mountain) + runoff math (Noose) + coalition fragmentation (Rope entropy) → forced bipolar equilibrium

If predictions fail: Model's constraint topology analysis is wrong, indexical classification insufficient, or temporal gradient computation flawed.

HOA Governance (Generated January 2026)

Model predicts legislative expansion of caps:

Will fail if:

• 2026-2028 sees HOA power expansion or deregulation
• State legislatures repeal existing fine caps
• Courts strike down solar/native plant protections

Will succeed if:

• More states pass AB 130-style fine caps
• Federal intervention on solar panel restrictions
• Litigation increases around "aesthetic harmony" definitions

Structural claim: Tangled Rope (coordination + extraction) experiencing automated enforcement acceleration (89% violation software) → regulatory pushback inevitable

---

Known Limitations

Methodological

• Not peer-reviewed: Framework developed through internal iteration (80+ test cases), not academic validation
• Domain dependency: Requires sufficient structural description (won't work on purely aesthetic/emotional domains)
• Measurement boundaries: High extraction + low transparency = lowest certainty (fundamental, not fixable)

Technical

• Speed-accuracy tradeoff: 60-second models may miss nuances weeks of expert analysis would catch
• Auto-repair heuristics: Layer 2 imputes neutral (0.5) priors for novel domains—may be incorrect
• Classification edge cases: Tangled Rope boundaries debatable (Rope vs. Noose with coordination elements)

Structural

• No dynamic generation: Assumes finite, identifiable options (doesn't handle emergent alternatives or strategic gaming)
• Temporal snapshots: Gradient computation predicts trends but can't model sudden regime shifts
• Power-observation coupling: Where you most need analysis (opaque extraction), you have least access to evidence

Quality Control

• Substrate gate critical: Layer 3 must not invent claims Layer 2 doesn't support
• Omega collapse risk: Warranted assertions can drift into overconfidence
• Credibility budget: Limited tolerance for contrarian claims before losing audience

---

Contributing

This is research infrastructure under active development, not production software.

Good Contributions

New Domain Compilations

• Run Layer 1 on novel domain
• Validate with Layer 2
• Document any auto-repair failures
• Compare predictions against outcomes

Falsification Tracking

• Colombia 2026: Track actual vs. predicted
• HOA legislation: Monitor state/federal action
• Any domain: Document where model failed

Specification Improvements

• Better Layer 1 prompt (reduce syntax errors)
• Smarter Layer 2 auto-repair (domain-specific priors)
• Refined Layer 3 quality gates (catch substrate violations)

Cross-Framework Comparison

• How does DR classification compare to Marxist analysis?
• What does game theory reveal that indexical logic misses?
• Where do traditional frameworks outperform constraint compilation?

Bad Contributions

• "Rewrite in Python" - Misses the point (Prolog's logic programming enables auto-repair + validation)
• "Just use LLM directly" - Loses formal specification (no reproducibility guarantee)
• "Add more constraint types" - Complexity inflation (current five types sufficient)
• "Make it user-friendly" - Premature (validate methodology before UX)

Contribution Process

1. Open issue describing what you're testing
2. Document substrate (what evidence supports your compilation?)
3. Show Layer 1 output (Prolog model)
4. Include Layer 2 audit (validation results)
5. Provide Layer 3 essay (if synthesized)
6. Track predictions (falsification criteria)

---

Citation

If this methodology proves useful:

@software{constraint_compiler_2026,
  author = {[cafebedouin]},
  title = {Structural Dynamics Model: Reproducible Structural Analysis via 
           Four-Layer AI Collaboration},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/cafebedouin/structural_dynamics_model},
  note = {Framework: Deferential Realism (DR). 
          Implementation: Prolog + Gemini + Claude. 
          Testing: 80+ domains (2025-2026).}
}

Framework: Deferential Realism (indexical epistemology, constraint taxonomy)
Layer 1: Gemini executing 60-page Prolog generation prompt
Layer 2: SWI-Prolog validation suite (auto-repair + DR-Audit)
Layer 3: Claude executing UKE_W v1.0 synthesis protocol

---

Roadmap

Short-Term (Q1-Q2 2026)

• ✓ Complete 80+ domain compilations
• ⧖ Track Colombia 2026 election predictions (falsification test)
• ⧖ Monitor HOA legislative trends (2026-2028)
• ⧗ Automate full pipeline (single command: domain → essay)
• ⧗ External peer review and replication

Medium-Term (Q3-Q4 2026)

• ⧗ Expand to 150+ domains (increase coverage)
• ⧗ Cross-cultural validation (non-Western constraint structures)
• ⧗ Real-time policy analysis interface (analyze as you read)
• ⧗ Comparative framework study (DR vs. Marxist/game-theoretic/etc.)
• ⧗ Publication: "Indexical Realism as Compilation Target"

Long-Term (2027+)

• ⧗ Constraint compiler as service (API endpoint)
• ⧗ Multi-constraint interaction modeling (how Nooses capture Ropes)
• ⧗ Temporal evolution prediction (Rope→Noose gradient forecasting)
• ⧗ Institutional adoption (policy analysis, legal review, protocol design)
• ⧗ Academic validation (peer review, independent replication)

Legend: ✓ Complete | ⧖ In Progress | ⧗ Planned

---

Philosophical Foundations

For those interested in the epistemological grounding:

Indexical Realism

Claim: Truth is relative to position (index), but facts within each position are objective.

Not relativism: Different indices don't mean "all perspectives equally valid"
Not absolutism: No universal view from nowhere

Example: Traffic light is Mountain (unchangeable) for individual driver, Rope (coordination tool) for city engineer, both simultaneously true.

Why Indices Are Necessary

Without explicit WHO/WHEN/WHERE/HOW MUCH specification, "What type of constraint is this?" collapses into equivocation. Same statement can be true for multiple index-sets → unindexed answer is logically incomplete.

Five Constraint Types

Type	Origin	Maintenance	Strategy
Mountain	Physics/logic/biology	None (immutable)	Navigate, accept
Rope	Coordination need	Active (energy input)	Maintain, reform carefully
Tangled Rope	Rope + captured extraction	Active (mixed incentives)	Reform (remove extraction, preserve coordination)
Noose	Power concentration	Active (enforcement)	Replace, cut, exit
Zombie	Institutional inertia	Passive (coordination failure)	Bypass, coordinate removal

Omega Variables: Reasoning Blockers

Ω_E (Empirical): Missing observable data → Resolution: Measure/experiment
Ω_C (Conceptual): Ambiguous terms → Resolution: Define/specify framework
Ω_P (Preference): Value conflicts → Resolution: Stakeholder decision

Why taxonomy matters: Data can't resolve conceptual confusion. Definitions can't reveal values. Caring doesn't generate facts. Matching resolution strategy to blocker type prevents wasted effort.

Multi-Level Hierarchical Analysis

Constraints operate simultaneously at four levels:

Level	Influence	What It Measures
Structural	40%	System-wide patterns, meta-rules
Organizational	30%	Institutional dynamics, enforcement
Class	20%	Group power, collective action capacity
Individual	10%	Personal experience, navigation options

Critical insight: Same constraint can be Mountain (individual), Rope (organizational), Noose (class) simultaneously. Single-level analysis yields contradictions.

For Complete Philosophy

See docs/ directory:

• deferential_realism_core.md - Full framework
• omega_variables.md - Reasoning blocker taxonomy
• indexical_relativity.md - Multi-perspective theory
• structural_signatures.md - Origin detection logic

---

Status & Contact

Current State: Internal validation complete, 80+ domains tested, awaiting external replication and falsification tracking

Specifications Published:

• Layer 1: prompts/constraint_story_generation_prompt.md (60 pages)
• Layer 2: prolog/validation_suite.pl + drl_core.pl
• Layer 3: protocols/uke_w.md (UKE_W v1.0)

Next Milestone: Colombia 2026 election results (May-June 2026) provide first major falsification test

Contact: [Your preferred contact method]

Last Updated: January 19, 2026

---

License

Creative Commons Zero v1.0 Universal

You can copy, modify, and distribute this work, even for commercial purposes, without asking permission. The specifications are public infrastructure.

If you improve the methodology, consider publishing your refinements so others can benefit.

---

Built by: Human framework design + AI compilation execution
Tested on: 80+ domains spanning finance, governance, protocols, history, algorithms
Falsifiable: Colombia 2026 predictions, HOA legislation trends
Reproducible: All specifications published, all 80+ examples available

Reasoning infrastructure for structural constraint analysis.