Pooled Mining is Driving Blockchains Toward Centralized Systems
Bibliographic record
Abstract
The decentralization property of blockchains stems from the fact that each miner accepts or refuses transactions and blocks based on its own verification results. However, pooled mining causes blockchains to evolve into centralized systems because pool participants delegate their decision-making rights to pool managers. In this paper, we established and validated a model for Proof-of-Work mining, introduced the concept of equivalent blocks, and quantitatively derived that pooling effectively lowers the income variance of miners. We also analyzed Bitcoin and Ethereum data to prove that pooled mining has become prevalent in the real world. The percentage of pool-mined blocks increased from 49.91% to 91.12% within four months in Bitcoin and from 76.9% to 92.2% within five months in Ethereum. In July 2018, Bitcoin and Ethereum mining were dominated by only six and five pools respectively.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".