- class svvamp.RuleNanson(**kwargs)[source]#
Nanson method.
Options#
>>> RuleNanson.print_options_parameters() cm_option: ['fast', 'exact']. Default: 'fast'. icm_option: ['exact']. Default: 'exact'. iia_subset_maximum_size: is_number. Default: 2. im_option: ['lazy', 'exact']. Default: 'lazy'. tm_option: ['lazy', 'exact']. Default: 'exact'. um_option: ['lazy', 'exact']. Default: 'lazy'.
Notes
At each round, all candidates with a Borda score strictly lower than average are simultaneously eliminated. When all remaining candidates have the same Borda score, it means that the matrix of duels (for this subset of candidates) has only ties. Then the candidate with lowest index is declared the winner. Since a Condorcet winner has always a Borda score higher than average, Nanson method meets the Condorcet criterion.
is_cm_()
: Deciding CM is NP-complete. Non-polynomial or non-exact algorithms from superclassRule
.is_icm_()
: Exact in polynomial time.is_im_()
: Deciding IM is NP-complete. Non-polynomial or non-exact algorithms from superclassRule
.is_iia_()
: Exact in polynomial time.is_tm_()
: Exact in polynomial time.is_um_()
: Non-polynomial or non-exact algorithms from superclassRule
.
References
‘Complexity of and algorithms for the manipulation of Borda, Nanson’s and Baldwin’s voting rules’, Jessica Davies, George Katsirelos, Nina Narodytska, Toby Walsh and Lirong Xia, 2014.
Examples
>>> profile = Profile(preferences_ut=[ ... [ 0. , -0.5, -1. ], ... [ 1. , -1. , 0.5], ... [ 0.5, 0.5, -0.5], ... [ 0.5, 0. , 1. ], ... [-1. , -1. , 1. ], ... ], preferences_rk=[ ... [0, 1, 2], ... [0, 2, 1], ... [1, 0, 2], ... [2, 0, 1], ... [2, 1, 0], ... ]) >>> rule = RuleNanson()(profile) >>> rule.meets_condorcet_c_rk_ctb True >>> rule.demo_results_(log_depth=0) ************************ * * * Election Results * * * ************************ *************** * Results * *************** profile_.preferences_ut (reminder) = [[ 0. -0.5 -1. ] [ 1. -1. 0.5] [ 0.5 0.5 -0.5] [ 0.5 0. 1. ] [-1. -1. 1. ]] profile_.preferences_rk (reminder) = [[0 1 2] [0 2 1] [1 0 2] [2 0 1] [2 1 0]] ballots = [[0 1 2] [0 2 1] [1 0 2] [2 0 1] [2 1 0]] scores = [[ 6. 4. 5.] [ 3. inf 2.] [ 0. inf inf]] candidates_by_scores_best_to_worst [0 2 1] scores_best_to_worst [[ 6. 5. 4.] [ 3. 2. inf] [ 0. inf inf]] w = 0 score_w = [6. 3. 0.] total_utility_w = 1.0 ********************************* * Condorcet efficiency (rk) * ********************************* w (reminder) = 0 condorcet_winner_rk_ctb = 0 w_is_condorcet_winner_rk_ctb = True w_is_not_condorcet_winner_rk_ctb = False w_missed_condorcet_winner_rk_ctb = False condorcet_winner_rk = 0 w_is_condorcet_winner_rk = True w_is_not_condorcet_winner_rk = False w_missed_condorcet_winner_rk = False *************************************** * Condorcet efficiency (relative) * *************************************** w (reminder) = 0 condorcet_winner_ut_rel_ctb = 0 w_is_condorcet_winner_ut_rel_ctb = True w_is_not_condorcet_winner_ut_rel_ctb = False w_missed_condorcet_winner_ut_rel_ctb = False condorcet_winner_ut_rel = 0 w_is_condorcet_winner_ut_rel = True w_is_not_condorcet_winner_ut_rel = False w_missed_condorcet_winner_ut_rel = False *************************************** * Condorcet efficiency (absolute) * *************************************** w (reminder) = 0 condorcet_admissible_candidates = [ True False False] w_is_condorcet_admissible = True w_is_not_condorcet_admissible = False w_missed_condorcet_admissible = False weak_condorcet_winners = [ True False False] w_is_weak_condorcet_winner = True w_is_not_weak_condorcet_winner = False w_missed_weak_condorcet_winner = False condorcet_winner_ut_abs_ctb = 0 w_is_condorcet_winner_ut_abs_ctb = True w_is_not_condorcet_winner_ut_abs_ctb = False w_missed_condorcet_winner_ut_abs_ctb = False condorcet_winner_ut_abs = 0 w_is_condorcet_winner_ut_abs = True w_is_not_condorcet_winner_ut_abs = False w_missed_condorcet_winner_ut_abs = False resistant_condorcet_winner = nan w_is_resistant_condorcet_winner = False w_is_not_resistant_condorcet_winner = True w_missed_resistant_condorcet_winner = False >>> rule.demo_manipulation_(log_depth=0) ***************************** * * * Election Manipulation * * * ***************************** ********************************************* * Basic properties of the voting system * ********************************************* with_two_candidates_reduces_to_plurality = True is_based_on_rk = True is_based_on_ut_minus1_1 = False meets_iia = False **************************************************** * Manipulation properties of the voting system * **************************************************** Condorcet_c_ut_rel_ctb (False) ==> Condorcet_c_ut_rel (False) || || || Condorcet_c_rk_ctb (True) ==> Condorcet_c_rk (True) || || || || || || || V V || || V V Condorcet_c_ut_abs_ctb (True) ==> Condorcet_ut_abs_c (True) || || || || || V V || || maj_fav_c_rk_ctb (True) ==> maj_fav_c_rk (True) || || || || || V V V V majority_favorite_c_ut_ctb (True) ==> majority_favorite_c_ut (True) || || V V IgnMC_c_ctb (True) ==> IgnMC_c (True) || || V V InfMC_c_ctb (True) ==> InfMC_c (True) ***************************************************** * Independence of Irrelevant Alternatives (IIA) * ***************************************************** w (reminder) = 0 is_iia = True log_iia: iia_subset_maximum_size = 2.0 example_winner_iia = nan example_subset_iia = nan ********************** * c-Manipulators * ********************** w (reminder) = 0 preferences_ut (reminder) = [[ 0. -0.5 -1. ] [ 1. -1. 0.5] [ 0.5 0.5 -0.5] [ 0.5 0. 1. ] [-1. -1. 1. ]] v_wants_to_help_c = [[False False False] [False False False] [False False False] [False False True] [False False True]] ************************************ * Individual Manipulation (IM) * ************************************ is_im = nan log_im: im_option = lazy candidates_im = [ 0. 0. nan] ********************************* * Trivial Manipulation (TM) * ********************************* is_tm = False log_tm: tm_option = exact candidates_tm = [0. 0. 0.] ******************************** * Unison Manipulation (UM) * ******************************** is_um = nan log_um: um_option = lazy candidates_um = [ 0. 0. nan] ********************************************* * Ignorant-Coalition Manipulation (ICM) * ********************************************* is_icm = False log_icm: icm_option = exact candidates_icm = [0. 0. 0.] necessary_coalition_size_icm = [0. 6. 4.] sufficient_coalition_size_icm = [0. 6. 4.] *********************************** * Coalition Manipulation (CM) * *********************************** is_cm = False log_cm: cm_option = fast, um_option = lazy, tm_option = exact candidates_cm = [0. 0. 0.] necessary_coalition_size_cm = [0. 1. 3.] sufficient_coalition_size_cm = [0. 2. 3.]
- property candidates_by_scores_best_to_worst_#
1d array of integers. Candidates are sorted according to their order of elimination. When several candidates are eliminated during the same round, they are sorted by Borda score at that round and, in case of a tie, by their index (highest indexes are eliminated first).
- property scores_#
2d array of integers.
scores[r, c]
is the Borda score of candidatec
at elimination roundr
.By convention, if candidate
c
does not participate to roundr
, thenscores[r, c] = numpy.inf
.