Dynamic Network Optimization for Autonomous Agents in Polynomial Time (github.com)

A new formal framework called the Network Dynamics Axioms (NDA) has been published, offering a generative calculus for how agency, autonomy, and cooperation emerge in interacting node systems. The release bundles a formal research paper with mathematical laws and definitions, a plain-language exposition, step-by-step pseudocode for simulations, and a proof that the core NDA computations are decidable in polynomial time (Polynomial Time.pdf). The repository includes cryptographic provenance (SHA-256 e9c8...a79 and an OpenTimestamps proof) and links to a custom GPT (“Optimus”) for experimentation. This matters for AI/ML because NDA ties emergent multi-agent behavior to a concrete, implementable calculus with tractable complexity guarantees — a rare combination that lowers the barrier from theory to simulation and engineering. The provided pseudocode makes it straightforward to prototype agent-based systems consistent with the axioms, while the P-time proof implies scalable algorithms rather than intractable combinatorics. The framework’s modular “plug-in” concept (domain-specific minerals) suggests a flexible architecture for integrating perception, planning, or reward mechanisms. Overall, NDA presents a principled foundation and practical tooling to study and build distributed autonomous systems, potentially impacting research in multi-agent learning, collective intelligence, and complexity science.