Incentivizing Consensus Propagation in Proof-of-Stake Based Consortium Blockchain Networks
Bibliographic record
Abstract
In proof-of-stake based consortium blockchain networks, pre-selected miners compete to solve a crypto-puzzle with a successfully mining probability proportional to the amount of their stakes. When the puzzle is solved, the miners are encouraged to take part in mined block propagation for verification to win a transaction fee from the blockchain user. The mined block should be propagated over wired or wireless networks, and be verified as quickly as possible to decrease consensus propagation delay. In this letter, we study incentivizing the consensus propagation considering the tradeoff between the network delay of block propagation process and offered transaction fee from the blockchain user. A Stackelberg game is then formulated to jointly maximize utility of the blockchain user and individual profit of the miners. The blockchain user acting as the leader sets the transaction fee for block verification. The miners acting as the followers decide on the number of recruited verifiers over wired or wireless networks. We apply the backward induction to analyze the existence and uniqueness of the Stackelberg equilibrium. Performance evaluation validates the feasibility and efficiency of the proposed game model in consensus propagation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".