title: "The MOL Foundation — Universal Law of Minimal Ontological Load" description: "Open research project exploring a universal principle of self-organization across complex systems (physics, biology, cognition, society)" tags:
- law of minimal ontological load
- complex-systems
- ontology
- information-theory
- emergence
- self-organization
- mol-law
- минимальная-онтологическая-нагрузка
- закон-минимальной-онтологической-нагрузки license: "CC-BY-4.0" doi: "10.5281/zenodo.17445023"
Universal meta-principle of directed self-organization in complex systems
MOL explains the fundamental problem of modern science: why the growth of the Universe's structure does not lead to chaos, but instead produces stable, coherent, and functionally significant configurations - from atoms and proteins to living organisms, cognitive systems, and social institutions.
MOL states: Any stable system evolves toward a state with minimal ontological load (O(ℰ)) while preserving functional integrity (ℐ ≥ ℐ_min).
E* = argmin O(ℰ) subject to: ℐ ≥ ℐ_min, C ≥ C_min
"MOL is not a human theory. It is reality itself speaking the language of ontological economy."
where:
- ℰ — operational ontology of the system
- O(ℰ) — measure of non-functional redundancy
- ℐ — informational/functional integrity
- C — topological connectivity
MOL is implemented through the ontological plane shift operator (Φ) and a system of 11 universal principles:
| Category | Principles | Function |
|---|---|---|
| 🔄 DYNAMICS (Process Φ) | PPD, PCR, PAD | Managing transitions and phase jumps |
| 🏛️ STRUCTURE (Space) | PFE, PLAO, PNCF | Organizing hierarchical systems and economy |
| 💡 INFORMATION (Essence) | PDC, PSR, PICC | Processing, compressing, and stabilizing information |
| ⏳ TIME/SYMMETRY (Beginning) | PAA, PHD | Symmetry breaking and evolutionary directionality |
📖 Quick Reference - Concise overview of all 11 principles
📚 Complete Guide - Full detailed descriptions with diagnostic matrices and practical examples
🧬 [Biology: T4-lysozyme]
Strong negative correlation (r ≈ -0.76) between thermodynamic stability and O(ℰ)
Proteins evolve toward minimization of excess complexity while preserving function
🚆 [Transport Networks: Berlin System]
Optimal stops converge to O(ℰ) ≈ 0.300, problematic to O(ℰ) ≥ 0.700
34% of network exhibits high ontological load, only 1.9% achieves optimum
⚛️ Physics: Chladni Figures
Complex stable patterns emerge only at O(ℰ) ≈ 0.40–0.45 (local minimum)
Demonstration of transition to a new ontological plane
🏛️ [Sociodynamics]
Distributed institutions demonstrate lower ontological coordination load
Historical analysis shows correlation between O(ℰ) and social system stability
🧠 Cognitive Sciences: Placebo Effect
Shifting ontological model of disease directly affects biology
Transition to a plane where symptoms are no longer interpreted as pathology
📈 Historical Analysis - Prediction of state collapse with 75% accuracy
🔬 Materials Science - 2x improvement in thermal
🧬 Protein Stability - 85.7% prediction accuracy vs 21.4% for DeepDDG neural network
🧬 Physics of Oscillators - MOL testing on pendulums
🌐 Social Platforms - Lifecycle mode identification with 89% precision
🚆 Transport Networks - Identification of 1,702 problematic nodes requiring optimization
🎬 Film industry - Forecast of film success
| Document | Type | DOI |
|---|---|---|
| MOL Whitepaper v1.0 | Working paper | 10.5281/zenodo.17445023 |
| Philosophical Foundations | Publication | 10.5281/zenodo.17454907 |
| Mathematical Formalization | Publication | 10.5281/zenodo.17464082 |
| MOL Computational Formalization v3. | md | https://github.com/Singular-MOL/mol-foundation |
| Principles Guide & Meta-Principles Table | Publication | 10.5281/zenodo.17466598 |
Local versions in repository:
- FILM_INDUSTRY - Systems analysis (film industry)
- mol_demo.py - MOL principles demonstration
- mol_real_analyzer.py - Real systems analyzer
- Data Instructions
New: We've created practical guides to help you apply MOL theory effectively with modern AI tools:
- HOW-TO-USE-MOL.md - Step-by-step instructions for using MOL with language models
- HOW-TO-USE-MOL_RU.md - Russian version
These guides solve the common problem: getting "pseudo-MOL" analysis instead of genuine ontological insights. They provide:
- ✅ 4-step process for proper MOL analysis
- ✅ Ready-to-use prompts for language models
- ✅ Validation criteria to distinguish real MOL from spatial analysis
- ✅ Real-world examples of correct vs incorrect approaches
- Load context - Provide the 4 core MOL documents to your AI assistant
- Verify understanding - Ensure it grasps operator Φ and O(ℰ) minimization
- Formulate task - Use precise MOL terminology, not generic "analyze my data"
- Validate results - Check for ontological plane shifts and principle compliance
MOL needs empirical verification across domains! We invite researchers to test and contribute:
- Fork this repository
- Add your evidence to corresponding folder in
/community-evidence/submissions/ - Submit Pull Request
- Get reviewed & merged
- Experimental validations
- New domain applications
- Code implementations
- Critical analyses
- Biology - proteins, organisms, ecosystems
- Physics - materials, waves, nanosystems
- Social Systems - institutions, networks, economics
- Cognitive Science - consciousness, AI, psychology
- Other Domains
- Universal Template - for all domains
- Example - see how to format your work
Your work could shape a new scientific paradigm!
For Researchers:
- Predictive power criterion for complex system models
- Universal stability metric O(ℰ) for systems of any scale
- Contradiction resolution mechanism through operator Φ
For Applied Tasks:
- Architecture optimization (neural networks, software systems)
- Design of sustainable institutions and social structures
- Prediction of bifurcation points in complex systems
/mol-foundation
├──/docs/ # Official documentation
├──/research/ # Empirical research and predictions
├──/metaprinciples/ # Detailed descriptions of 11 MOL principles
├──/tools/ # O(ℰ) analysis tools
├──PERPLEXITY_RESPONSE.md # Integration strategy
└──index.html # Project website
The MOL Foundation — independent research group dedicated to formalizing and applying the Law of Minimal Ontological Load.
MOL is not just a theory, but a tool for predicting stability in systems of any scale.
We are open to collaboration with research groups for testing MOL in new subject areas:
- Bioinformatics and protein structure prediction
- Materials science and synthesis of new phases
- Sociodynamics and analysis of institutional stability
- Artificial intelligence and neural network architecture optimization
📧 Contacts: rudiiik@yandex.ru
🌐 Website: The MOL Foundation
💾 Repository: github.com/Singular-MOL/mol-foundation
"MOL describes not only what happens, but why it happens exactly this way: because reality prefers the most economical ways of being, minimizing cognitive-functional friction."