Struct MaxMinLoadModel

pub struct MaxMinLoadModel;

The Max-Min Load Fair model ranks candidates to achieve a fair distribution of influence among all backers. It selects candidates sequentially to minimize the maximum load any backer bears, ensuring no single backer is disproportionately overrepresented.

Concept: Load-Balanced Candidate Selection

Each candidate is associated with a list of (Backer, Backed) pairs representing the support they receive. The algorithm repeatedly:

1. Computes, for each remaining candidate, the maximum load their selection would impose on their backers.
2. Selects the candidate whose election results in the smallest maximum load increase.
3. Updates backer loads accordingly.

Characteristics:

• Fairness-focused: Prioritizes balancing backer loads rather than simply maximizing totals.
• Deterministic: Tie-breaking is deterministic (first candidate in iteration).
• Context-free: Does not require any external configuration or parameters.

Applications:

• Governance or board elections where proportional influence is desired.
• Delegation-based or collaborative voting systems.
• Any scenario where minimizing backer overload improves fairness.

Trait Implementations

impl Debug for MaxMinLoadModel

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

Formats the value using the given formatter.

impl Default for MaxMinLoadModel

fn default() -> MaxMinLoadModel

Returns the “default value” for a type.

impl<Candidate, Backer, Backed, FairWeight, Input, Output> PurePluginModel<Input, (), Output> for MaxMinLoadModel

where Input: IntoIterator<Item = (Candidate, FairWeight)> + Clone, Output: FromIterator<Candidate>, FairWeight: IntoIterator<Item = (Backer, Backed)> + Clone, Backed: Clone + Zero + Ord + Add<Output = Backed>, Candidate: Clone + Eq, Backer: Clone + Eq + Hash + Ord,

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

Computes a fair ranking of candidates by minimizing maximum load per backer.

Steps :

1. Collect input into a vector for sequential processing.
2. Initialize backer loads to zero.
3. While there are unranked candidates: a. For each candidate, compute the maximum load increase across their backers. b. Select the candidate with the smallest maximum load. c. Remove the selected candidate from remaining candidates and append to ranking. d. Update backer loads by adding this candidate’s contributions.
4. Return the ranked candidates as an iterator.