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* Add markdown linting - add linter default rules - adapt rules to current code - fix the code for linting to pass - add CI check fix #1243 * Fix markdown for Substrate * Fix tooling install * Fix workflow * Add documentation * Remove trailing spaces * Update .github/.markdownlint.yaml Co-authored-by: Oliver Tale-Yazdi <oliver.tale-yazdi@parity.io> * Fix mangled markdown/lists * Fix captalization issues on known words
57 lines
2.7 KiB
Markdown
57 lines
2.7 KiB
Markdown
# sp-npos-elections
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A set of election algorithms to be used with a Substrate runtime, typically within the staking sub-system. Notable
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implementation include:
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- [`seq_phragmen`]: Implements the Phragmén Sequential Method. An un-ranked, relatively fast election method that
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ensures PJR, but does not provide a constant factor approximation of the maximin problem.
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- [`phragmms`]: Implements a hybrid approach inspired by Phragmén which is executed faster but it can achieve a constant
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factor approximation of the maximin problem, similar to that of the MMS algorithm.
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- [`balance_solution`]: Implements the star balancing algorithm. This iterative process can push a solution toward being
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more `balanced`, which in turn can increase its score.
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## Terminology
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This crate uses context-independent words, not to be confused with staking. This is because the election algorithms of
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this crate, while designed for staking, can be used in other contexts as well.
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`Voter`: The entity casting some votes to a number of `Targets`. This is the same as `Nominator` in the context of
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staking. `Target`: The entities eligible to be voted upon. This is the same as `Validator` in the context of staking.
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`Edge`: A mapping from a `Voter` to a `Target`.
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The goal of an election algorithm is to provide an `ElectionResult`. A data composed of:
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- `winners`: A flat list of identifiers belonging to those who have won the election, usually ordered in some meaningful
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way. They are zipped with their total backing stake.
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- `assignment`: A mapping from each voter to their winner-only targets, zipped with a ration denoting the amount of
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support given to that particular target.
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```rust
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// the winners.
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let winners = vec![(1, 100), (2, 50)];
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let assignments = vec![
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// A voter, giving equal backing to both 1 and 2.
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Assignment {
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who: 10,
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distribution: vec![(1, Perbill::from_percent(50)), (2, Perbill::from_percent(50))],
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},
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// A voter, Only backing 1.
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Assignment { who: 20, distribution: vec![(1, Perbill::from_percent(100))] },
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];
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// the combination of the two makes the election result.
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let election_result = ElectionResult { winners, assignments };
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```
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The `Assignment` field of the election result is voter-major, i.e. it is from the perspective of the voter. The struct
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that represents the opposite is called a `Support`. This struct is usually accessed in a map-like manner, i.e. keyed by
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voters, therefore it is stored as a mapping called `SupportMap`.
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Moreover, the support is built from absolute backing values, not ratios like the example above. A struct similar to
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`Assignment` that has stake value instead of ratios is called an `StakedAssignment`.
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More information can be found at: https://arxiv.org/abs/2004.12990
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License: Apache-2.0
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