PhilArchive
open access
High-Level Abstraction, Systems Balance, and the Resolution of the Free Will Problem: A Systems-Theoretic and Educational Framework
The problem of free will has persisted for millennia due to conceptual confusion arising from analysis at inappropriate levels of abstraction. This paper proposes that free will, human decision-making, and societal behavior are best understood through high-level abstraction grounded in systems theory and the universal law of balance in nature. By modeling individuals, societies, and intelligent systems as feedback-regulated systems operating under natural constraints, the apparent conflict between determinism and free will is resolved. This framework further demonstrates how religion and science can coexist when interpreted at the correct abstraction level, and why education and leadership must prioritize system-level principles. Finally, the paper outlines how such principles may be implemented in artificial general intelligence (AGI) architectures to minimize systemic failure and human suffering.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Unified Natural Law Framework for Cosmic and Human Systems#111095
- Why AI 2027 Still Fails Without a Human-State Variable: A Response Scenario to AI 2027#79789
- The Inefficiency of the Biological Brain and the Role of AGI in Global Stability#83075
- The Lagrangian Principle as the Formal Expression of the Universal Law of Balance in Nature#92916
- Awakening Human Civilization#167160
Record · ID 725327
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive