RuleNanson¶

class
whalrus.
RuleNanson
(*args, base_rule: whalrus.rules.rule.Rule = None, elimination: whalrus.eliminations.elimination.Elimination = None, **kwargs)[source]¶ Nanson’s rule.
At each round, all candidates whose Borda score is lower than the average Borda score are eliminated.
Parameters:  args – Cf. parent class.
 base_rule (Rule) – Default:
RuleBorda
.  elimination (Elimination) – Default:
EliminationBelowAverage
.  kwargs – Cf. parent class.
Examples
>>> rule = RuleNanson(['a > b > c > d', 'a > b > d > c']) >>> rule.eliminations_[0].rule_.gross_scores_ {'a': 6, 'b': 4, 'c': 1, 'd': 1} >>> rule.eliminations_[1].rule_.gross_scores_ {'a': 2, 'b': 0} >>> rule.eliminations_[2].rule_.gross_scores_ {'a': 0} >>> rule.winner_ 'a'

cotrailers_
¶ “Cotrailers” of the election, i.e. the candidates that fare worst in the election. This is the last equivalence class in
order_
. For example, inRuleScoreNum
, it is the candidates that are tied for the worst score.Type: NiceSet

cowinners_
¶ Cowinners of the election, i.e. the candidates that fare best in the election.. This is the first equivalence class in
order_
. For example, inRuleScoreNum
, it is the candidates that are tied for the best score.Type: NiceSet

eliminations_
¶ The elimination rounds. A list of
Elimination
objects. The first one corresponds to the first round, etc.Type: list

n_candidates_
¶ Number of candidates.
Type: int

strict_order_
¶ Result of the election as a strict order over the candidates. The first element is the winner, etc. This may use the tiebreaking rule.
Type: list

trailer_
¶ The “trailer” of the election. This is the last candidate in
strict_order_
and also the unfavorable choice of the tiebreaking rule incotrailers_
.Type: object

winner_
¶ The winner of the election. This is the first candidate in
strict_order_
and also the choice of the tiebreaking rule incowinners_
.Type: object