A Theoretical Model for Fork Analysis in the Bitcoin Network
Why this work is in the frame
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Bibliographic record
Abstract
Blockchain networks which employ Proof-of-Work in their consensus mechanism may face inconsistencies in the form of forks. These forks are usually resolved through the application of block selection rules (such as the Nakamoto consensus). In this paper, we investigate the cause and length of forks for the Bitcoin network. We develop theoretical formulas which model the Bitcoin consensus and network protocols, based on an Erdös-Rényi random graph construction of the overlay network of peers. Our theoretical model addresses the effect of key parameters on the fork occurrence probability, such as block propagation delay, network bandwidth, and block size. We also leverage this model to estimate the weight of fork branches. Our model is implemented using the network simulator OMNET++ and validated by historical Bitcoin data. We show that under current conditions, Bitcoin will not benefit from increasing the number of connections per node.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it