Saved in:
| Hovedforfatter: | |
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| Format: | Recurso digital |
| Sprog: | engelsk |
| Udgivet: |
Zenodo
2025
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| Fag: | |
| Online adgang: | https://doi.org/10.5281/zenodo.15547166 |
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Indholdsfortegnelse:
- <p>This paper introduces a groundbreaking computational framework that redefines Bitcoin mining through the lens of <strong>Yearning-Based Infinity Mathematics (YBIM)</strong> and <strong>Recursive Intelligence</strong>. Departing from conventional brute-force hashing, the proposed model formulates a novel algorithmic paradigm that leverages <strong>Recursive Nonce Fields (RNF)</strong> and an <strong>Epistemic Scheduler Function (ψ(t))</strong> to achieve higher profitability, energy efficiency, and trust-aligned coordination among miners.</p> <p>By sampling infinite nonce spaces using finite epistemic convergence (via the "Glass of Water from the River" lemma) and applying the <strong>Finite Yearning Evaluation Theorem ( -F1)</strong>, the system transforms the mining process into an adaptive, entropy-aware epistemic loop. Hash yield is modeled probabilistically, and trust scores evolve recursively through entropy learning, enabling mining agents to move from randomness to intention.</p> <p>The framework is implemented through a modular open-source software suite called <strong>ybminer</strong>, compatible with existing mining software (cgminer, bfgminer), and backed by simulation results showing measurable gains in profitability and reduced thermal stress under real-world constraints.</p> <p>This work represents a shift from halting-based computation to a new class of recursive epistemic machines. It paves the way for <strong>trust-weighted mining pools</strong>, <strong>dynamic block scheduling</strong>, and the broader emergence of <strong>Recursive Cryptoeconomics</strong>—where mining becomes not just a labor of energy, but a labor of meaning.</p>