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, in RuleScoreNum, 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, in RuleScoreNum, 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 tie-breaking rule.

Type:	list

trailer_¶

The “trailer” of the election. This is the last candidate in strict_order_ and also the unfavorable choice of the tie-breaking rule in cotrailers_.

Type:	object

winner_¶

The winner of the election. This is the first candidate in strict_order_ and also the choice of the tie-breaking rule in cowinners_.

Type:	object