Description Usage Arguments Details Value References Examples
The original Dodgson method checks the number of
votes each candidate has to rob from other candidates;
the winner is with the smallest number. However, the
function cdc_dodgson uses two alternative methods
rather than the original Dodgson method. The two methods
are Tideman score method and Dodgson Quick method.
See Details.
1 | cdc_dodgson(x, allow_dup = TRUE, min_valid = 1, dq_t = "dq")
|
x |
it accepts the following types of input:
1st, it can be an object of class |
allow_dup |
whether ballots with duplicated score values are taken into account. Default is TRUE. |
min_valid |
default is 1. If the number of valid entries of a ballot is less than this value, it will not be used. |
dq_t |
the alternative Dodgson methods to be used. Default is "dq", for Dodgson Quick method; it can also be "t", Tideman score method. |
Suppose the candidates are A, B, C and D. If A wins B in pairwise comparison or has equal votes with B, then add 0 to A. If C wins A, then add to A adv(C, A), that is, the number of voters that prefer C than A, minus the number of voters that prefer A than A. Again, if D wins A, then add to A that number. Then, we sum up the points belong to A. We do the same thing to B, C and D. The one gets the least points is the winner. This is what we do in Tideman score method. In Dodgson Quick method, we first compute the number of votes, then divide it by 2 and get the ceiling, and sum all of them up.
a condorcet object, which is essentially
a list.
(1) call the function call.
(2) method the counting method.
(3) candidate candidate names.
(4) candidate_num number of candidate.
(5) ballot_num number of ballots in x. When
x is not a vote object, it may be NULL.
(6) valid_ballot_num number of ballots that are
actually used to compute the result. When
x is not a vote object, it may be NULL.
(7) winner the winners.
(8) input_object the class of x.
(9) cdc the Condorcet matrix which is actually used.
(10) dif the score difference matrix. When
x is not a vote object, it may be NULL.
(11) binary win and loss recorded with 1 (win),
0 (equal) and -1 (loss).
(12) summary_m times of win (1), equal (0)
and loss (-1).
(13) other_info a list with four elements. The 1st
indicates the method used to compute score. The 2nd is the score
for pairwise comparison
(number of votes one has to rob). The 3rd is Tideman score
summary (the smaller the better).
The 4th is Dodgson Quick summary (the smaller the better).
McCabe-Dansted, J. & Slinko, A. 2008. Approximability of Dodgson's Rule. Social Choice and Welfare, Feb, 1-26.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | raw <- list2ballot(
x = list(
c('A', 'B', 'C', 'D', 'E', 'F'),
c('F', 'A', 'B', 'C', 'D', 'E'),
c('E', 'D', 'C', 'B', 'F', 'A'),
c('B', 'A', 'C', 'D', 'E', 'F'),
c('F', 'E', 'D', 'C', 'B', 'A'),
c('F', 'B', 'A', 'C', 'D', 'E'),
c('E', 'D', 'C', 'A', 'F', 'B'),
c('E', 'B', 'A', 'C', 'D', 'F'),
c('F', 'D', 'C', 'A', 'E', 'B'),
c('D', 'B', 'A', 'C', 'E', 'F'),
c('F', 'E', 'C', 'A', 'D', 'B')
),
n = c(19, 12, 12, 9, 9, 10, 10 , 10 , 10, 10, 10)
)
vote <- create_vote(raw, xtype = 3, candidate = c('A', 'B', 'C', 'D', 'E', 'F'))
win1 <- cdc_simple(vote) # no winner
win2 <- cdc_dodgson(vote, dq_t = "dq") # A
win2 <- cdc_dodgson(win1, dq_t = "dq") # A
win3 <- cdc_dodgson(vote, dq_t = "t") # B
win3 <- cdc_dodgson(win2, dq_t = "t") # B
|
MATCHING NAMES AND SCORES
COUNTING NA AND DUP VALUES
MAKING CONDORCET TABLE
COLLECTING RESULT
DONE
CREATING CDC MATRIX
------USE CDC MATRIX WITH NA IN x
EXTRACTING INFO
SELECTING
COLLECTING RESULT
DONE
CREATING CDC MATRIX
------USE CDC MATRIX WITH NA IN x
EXTRACTING INFO
SELECTING
COLLECTING RESULT
DONE
CREATING CDC MATRIX
EXTRACTING INFO
SELECTING
COLLECTING RESULT
DONE
CREATING CDC MATRIX
------USE CDC MATRIX WITH NA IN x
EXTRACTING INFO
SELECTING
COLLECTING RESULT
DONE
CREATING CDC MATRIX
EXTRACTING INFO
SELECTING
COLLECTING RESULT
DONE
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