frame_plugins::elections::fair

Struct BalancedModel

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pub struct BalancedModel;
Expand description

The BalancedFair model evaluates candidates by combining both the total backing and the average backing per backer. This approach balances candidates who have a few very large backers versus those with many smaller backers.

Concept: Total + Average External Fairness

Each candidate has a list of (Backer, Backed) pairs. The model computes:

  1. total = sum of all Backed values
  2. average = total / number of backers
  3. score = total + average

Candidates are then sorted in descending order by this combined score.

§Characteristics:

  • Balanced aggregation: Rewards both high total support and broad distribution.
  • No self-weighting: Excludes candidate self-contributions.
  • Overflow-safe: Uses saturating arithmetic.
  • Deterministic ordering: Sorted descending by combined score.
  • Context-free: Stateless and reproducible ranking.

§Applications:

  • Governance systems balancing “big backers” vs “broad support”.
  • Collaborative projects or token-based delegation systems.
  • Reward allocation where both total and average contributions matter.

§References:

Trait Implementations§

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impl Debug for BalancedModel

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl Default for BalancedModel

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fn default() -> BalancedModel

Returns the “default value” for a type. Read more
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impl<Candidate, FairWeight, Backer, Backed, Input, Output> PurePluginModel<Input, (), Output> for BalancedModel
where Input: IntoIterator<Item = (Candidate, FairWeight)> + Clone, Output: FromIterator<Candidate>, FairWeight: IntoIterator<Item = (Backer, Backed)> + Clone, Backed: Clone + Ord + Zero + Saturating + Add<Output = Backed> + Div<usize, Output = Backed>, Candidate: Clone,

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fn compute(&self, input: Input, _context: &()) -> Output

Computes the balanced fair rank of candidates imperatively.

Steps:

  1. Iterate over each candidate and collect all (Backer, Backed) pairs.
  2. Compute total backing as the saturating sum of Backed.
  3. Compute average backing per backer.
  4. Compute score = total + average.
  5. Sort candidates by descending score.
  6. Return only the ordered candidates.

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