Comments (3)
Updates on the algorithm design.
We have two metrics that we can measure each having different confidence level.
Given the total number of transactions finalised by the miner/validator - NT
- Violations. For that we need to record and report the total number of violations (NV). Relative violations (RV) = NV/ NT.
- Outliers. Specifically we are interested only in extremely fast transactions, which are in top quantile compared to all other transactions. Calculate number of such outliers - NO. We can use RO = NO / NT.
Finally, the miner/validator who has produced many blocks and finalised many transactions should have higher score than miners/validators who only create few blocks.
For that we introduce miner productivity coefficient comparing. This coefficient shows how the miner compares against the top finalising miner.
We use this coefficient with a sigmoid function to introduce a decay to the miner
Two metrics on outliers and violations can/should be reported in the dashboard: RO + NO, RV + NV.
Finally, the miner score is calculated as:
Time aspect
Taking into account only last
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Seems like a reasonable alternative that is relatively easy to understand and explain. Though, we should dig into the literature for fraud-resistant reputation function more in the future.
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Done
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Related Issues (16)
- RPC Router HOT 1
- Dashboard redesign HOT 1
- Database optimization HOT 2
- Miner deanonymization HOT 1
- Scalable mempool collection engine HOT 1
- Attack on Polygon
- Fetch Cardano data for the dashboard
- Improve the deanon process
- Transactions pre-confirmation dashboard
- Simple transaction pre-confirmation algorithm HOT 1
- Normalize the transactions DB
- Move mempool data to Timescale DB HOT 1
- Add pending transactions table
- Transaction pre-confirmation endpoint
- Transaction pre-confirmation Web UI HOT 1
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