import numpy as np
from embedded_voting.embeddings.embeddings_generator_polarized import EmbeddingsGeneratorPolarized
from embedded_voting.manipulation.collective_manipulation.manipulation_coalition_ordinal import ManipulationCoalitionOrdinal
from embedded_voting.ratings_from_embeddings.ratings_from_embeddings_correlated import RatingsFromEmbeddingsCorrelated
from embedded_voting.rules.singlewinner_rules.rule_positional_borda import RulePositionalBorda
from embedded_voting.rules.singlewinner_rules.rule_svd_nash import RuleSVDNash
[docs]class ManipulationCoalitionOrdinalBorda(ManipulationCoalitionOrdinal):
"""
This class do the coalition manipulation
analysis for the :class:`RulePositionalBorda` rule_positional.
Parameters
----------
ratings: Ratings or np.ndarray
The ratings of voters to candidates
embeddings: Embeddings
The embeddings of the voters
rule : Rule
The aggregation rule we want to analysis.
Examples
--------
>>> np.random.seed(42)
>>> ratings_dim_candidate = [[1, .2, 0], [.5, .6, .9], [.1, .8, .3]]
>>> embeddings = EmbeddingsGeneratorPolarized(10, 3)(.8)
>>> ratings = RatingsFromEmbeddingsCorrelated(coherence=0.8, ratings_dim_candidate=ratings_dim_candidate)(embeddings)
>>> manipulation = ManipulationCoalitionOrdinalBorda(ratings, embeddings, RuleSVDNash())
>>> manipulation.winner_
1
>>> manipulation.is_manipulable_
False
>>> manipulation.worst_welfare_
1.0
"""
def __init__(self, ratings, embeddings, rule=None):
if not isinstance(ratings, np.ndarray):
ratings = ratings.ratings
super().__init__(ratings, embeddings, rule_positional=RulePositionalBorda(ratings.shape[1]), rule=rule)