--- tags: Reports --- # Sybil Report on Gitcoin Grants Rounds 13 :::warning !! WE NEED TO REDO USING THE CORRECT RAW DATA - this will interefere with any contribution related summary statistics !! ::: ## Summary An total of 11.9% of the Gitcoin users, representing `TODO`% of the contributions, were flagged during R13. The Sybil Incidence during this round is significantly lower than R12, with an estimate of being 70% of it was before. The Flagging Efficiency was 84% (lower boundary: 77% and upper boundary: 93%) which means that the combined process is underflagging sybils compared to what humans would do. ## Links - Past reports - GR12: https://hackmd.io/HXQ3yWfBQN6QGQX4cmOAxQ - GR11: https://hackmd.io/kOEbdDNeSdCkYXGY2wJExQ - GR10: https://hackmd.io/XdCMH5DtRHyPNLCGDFZ-oQ - GR09: https://hackmd.io/smLQ-QXdTse2dz1c6-Z85w ## Parameters Algorithm Aggressiveness: 30% Threshold for marking human evals as "Sybil or "Not Sybil": 90% :::info Algorithm Aggressiveness tunes how much sensitive / specific the flagging model should be. When it is set to 50%, then it will be optimized for accuracy, while closer values to 0% means that it will be optimized for minimizing false detection rate ::: ## Statistics ### Sybil Incidence & Detection #### Sybil Incidence *Estimated Sybil Incidence per Evaluation Round* ![](https://i.imgur.com/JJgtgrT.png) - **Estimated Sybil Incidence: 14.1% +/- 1.3% (95% CI)** - Estimated Sybil Users: 2453 (between 2227 and 2680, 95% CI) ___ - **Flagged Users Fraction: 11.9%** - Flagged Sybil Users: 2071 #### Sybil Flags - ML generated flags: 53 - Heuristic generated flags: 1067 - Human provided flags: 951 - Total flags: 2071 #### Sybil Evaluations *Relative distributions of scores per category* `TODO`: upload histogram ___ Total users evaluated by humans: 6405 (36.8% of total) Users marked as true by humans: 951 (14.8%) Users marked as false by humans: 5454 (85.2%) ___ Total users evaluated by heuristics: 1180 (6.8% of total) Users marked as true by heuristics: 1067 (90.4%) Users marked as false by heuristics: 113 (9.6%) ___ Total users evaluated by algorithms: 9818 (56.4% of total) Users marked as true by algorithms: 53 (0.5%) Users marked as false by algorithms: 9765 (99.5%) ___ Total users evaluated: 17403 (100.0% of total) Users marked as true: 2071 (11.9%) Users marked as false: 15332 (88.1%) ___ *Comparison between Evaluation / Prediction / Aggregate score* ![](https://i.imgur.com/zNdk5wQ.png) ### Summary Statistics on Scenarios #### Contributors Total Contributions (original): `TODO` Total Contributions (modified): `TODO` Total Contributions (removed): `TODO` ___ Matched Contributions (original): `TODO` Matched Contributions (modified): `TODO` Matched Contributions (removed): `TODO` Change: `TODO` Total Contributors (original): `TODO` Total Contributors (modified): `TODO` Total Contributors (removed): `TODO` ___ Sum of USDT Amount (original): `TODO` Sum of USDT Amount (removed): `TODO` ___ Median of Median USDT Amount per User (original): `TODO` Median of Median USDT Amount per User (modified): `TODO` Median of Median USDT Amount per User (removed): `TODO` ___ Median Contribution Count per User (original): `TODO` Median Contribution Count per User (modified): `TODO` Median Contribution Count per User (removed): `TODO` ___ Mean Count per User (original): `TODO` Mean Count per User (modified): `TODO` Mean Count per User (removed): `TODO` ___ *Histogram of the Contribution per User aggregates* ![](https://i.imgur.com/mfQCUGX.png)