Modeling the Fork Probability of Blockchains: Did EIP-1559 Improve Ethereum?
Bibliographic record
Abstract
There is a natural tendency for many public blockchain systems to undergo forks. When they are resolved, they result in a chain reorganization (reorg) where the transactions from the side fork are discarded. This causes delays since clients are required to wait for more confirmations before processing transactions. In order to improve security, speed, and efficiency of any blockchain network, it is desirable to reduce the probability of forking as much as possible. For this purpose, it is necessary to have a thorough understanding of the behavior of blockchain systems in order to optimize parameters in accordance with this goal [1], [2]. This study proposes a new fork model that incorporates parameters not previously considered, such as network delay and validation degree. We have conducted several experiments to verify the validity of our proposed method, using the BlockSim simulator on the Ethereum network and EIP-1559. According to the results of this study, decreasing the validation degree and increasing the miners' marginal cost (as in EIP-1559) reduces the likelihood of a fork occurring by around 10 percent. Additionally, the experimental results validate the precision of our method in predicting the probability of forking.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| 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".