# Gitcoin RPG notes

###### tags: `gitcoin` `Notes`

:::info
Updated by June 2021
:::

## Goals

- To perform a dry-run of the technical anti-sybil workstream during round
- To make it fun and educative to manage the anti-sybil work

## Roles

- Contribution data generator: Danilo
- Sub-roles: dishonest contribution generator & honest contribution generator
- Machine Learning operator: Jesse Tao
- Human Evaluator: Jiajia
- Responsible for manually flagging select labels that were predicted from the lastest run
- MC: Zargham
- Subrole: Final report generator

## Rules of the game

- Win conditions
- For dishonest generators: funelling money away through the sybil tax
- For honest generators:

## Procedures

### Bootstraping cycle

1.

### Flagging Cycles (iterative rounds)

1. Data generators create additional rows and provide metadata (labels) to the MC
2. Machine Learning Operator trains the subset-supervised ML algorithm and uses it to extrapolate over the all the existing dataset
- (possible to change heuristics over time)
- The result of the extrapolation is a tabular data with users and ids and containing the model results (eg: label and confidence)
3. Machine Learning Operator selects a subset of the extrapolation results and hand over to the human evaluator
- (eg: the subset could be 20 'random' users)
4. Human Evaluator manually flags each selected user by looking into the available data sources (contributions graph, account links, etc)

### Judgement & Sanction (end round)

1. Metadata provided by the data generators are checked against the final extrapolation results

## Notes

- How the evaluator is going to handle the resulting data?
- Right now, it is through google sheets
- Improvements
- add github fields to the test sheet
- The dry run will perform better by having a subset of the full contributions graph
- Needs a better way to generate the created_on events
- Make the generated user names less obvious

## References

- Gitcoin Data Sources: [[https://hackmd.io/hVg9pm_LQN6HjJ6-uV2ZBg]]
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