Simulate an Election

This function simulates an election by creating matrices representing candidate votes (X) and voters' demographic group (W) across a specified number of ballot-boxes. It either (i) receives as input or (ii) generates a probability matrix (prob), indicating how likely each demographic group is to vote for each candidate.

By default, the number of voters per ballot box (ballot_voters) is set to a vector of 100 with length num_ballots. You can optionally override this by providing a custom vector.

Optional parameters are available to control the distribution of votes:

• group_proportions: A vector of length num_groups specifying the overall proportion of each demographic group. Entries must sum to one and be non-negative.

• prob: A user-supplied probability matrix of dimension (num_groups \(\times\) num_candidates). If provided, this matrix is used directly. Otherwise, voting probabilities for each group are drawn from a Dirichlet distribution.

simulate_election(
  num_ballots,
  num_candidates,
  num_groups,
  ballot_voters = rep(100, num_ballots),
  lambda = 0.5,
  seed = NULL,
  group_proportions = rep(1/num_groups, num_groups),
  prob = NULL
)

Arguments

num_ballots

Number of ballot boxes (b).

num_candidates

Number of candidates (c).

num_groups

Number of demographic groups (g).

ballot_voters

A vector of length num_ballots representing the number of voters per ballot box. Defaults to rep(100, num_ballots).

lambda

A numeric value between 0 and 1 that represents the fraction of voters that are randomly assigned to ballot-boxes. The remaining voters are assigned sequentially according to their demographic group.

• lambda = 0: The assignment of voters to ballot-boxes is fully sequential in terms of their demographic group. This leads to a high heterogeneity of the voters' groups across ballot-boxes.

• lambda = 1: The assignment of voters to ballot-boxes is fully random. This leads to a low heterogeneity of the voters' group across ballot-boxes.

Default value is set to 0.5. See Shuffling Mechanish for more details.

seed

If provided, overrides the current global seed. Defaults to NULL.

group_proportions

Optional. A vector of length num_groups that indicates the fraction of voters that belong to each group. Default is that all groups are of the same size.

prob

Optional. A user-supplied probability matrix of dimension (g x c). If provided, this matrix is used as the underlying voting probability distribution. If not supplied, each row is sampled from a Dirichlet distribution with each parameter set to one.

Value

An eim object with three attributes:

X

A (b x c) matrix with candidates' votes for each ballot box.

W

A (b x g) matrix with voters' groups for each ballot-box.

real_prob

A (g x c) matrix with the probability that a voter from each group votes for each candidate. If prob is provided, it would equal such probability.

outcome

A (b x g x c) array with the number of votes for each candidate in each ballot box, broken down by group.

Shuffling Mechanism

Without loss of generality, consider an order relation of groups and ballot-boxes. The shuffling step is controlled by the lambda parameter and operates as follows:

1. Initial Assignment: Voters are assigned to each ballot-box sequentially according to their demographic group. More specifically, the first ballot-boxes receive voters from the first group. Then, the next ballot-boxes receive voters from the second group. This continues until all voters have been assigned. Note that most ballot-boxes will contain voters from a single group (as long as the number of ballot-boxes exceeds the number of groups).

2. Shuffling: A fraction lambda of voters who have already been assigned is selected at random. Then, the ballot-box assignment of this sample is shuffled. Hence, different lambda values are interpreted as follows:

   • lambda = 0 means no one is shuffled (the initial assignment remains).

   • lambda = 1 means all individuals are shuffled.

   • Intermediate values like lambda = 0.5 shuffle half the voters.

Using a high level of lambda (greater than 0.7) is not recommended, as this could make identification of the voting probabilities difficult. This is because higher values of lambda induce similar ballot-boxes in terms of voters' group.

References

The algorithm is fully explained in 'Thraves, C. Ubilla, P. and Hermosilla, D.: "A Fast Ecological Inference Algorithm for the R×C Case".

Examples

# Example 1: Default usage with 200 ballot boxes, each having 100 voters
result1 <- simulate_election(
    num_ballots = 200,
    num_candidates = 3,
    num_groups = 5
)

# Example 2: Using a custom ballot_voters vector
result2 <- simulate_election(
    num_ballots = 340,
    num_candidates = 3,
    num_groups = 7,
    ballot_voters = rep(200, 340)
)

# Example 3: Supplying group_proportions
result3 <- simulate_election(
    num_ballots = 93,
    num_candidates = 3,
    num_groups = 4,
    group_proportions = c(0.3, 0.5, 0.1, 0.1)
)

# Example 4: Providing a user-defined prob matrix
custom_prob <- matrix(c(
    0.9,  0.1,
    0.4,  0.6,
    0.25, 0.75,
    0.32, 0.68,
    0.2,  0.8
), nrow = 5, byrow = TRUE)

result4 <- simulate_election(
    num_ballots = 200,
    num_candidates = 2,
    num_groups = 5,
    lambda = 0.3,
    prob = custom_prob
)

result4$real_prob == custom_prob
#>      [,1] [,2]
#> [1,] TRUE TRUE
#> [2,] TRUE TRUE
#> [3,] TRUE TRUE
#> [4,] TRUE TRUE
#> [5,] TRUE TRUE
# The attribute of the output real_prob matches the input custom_prob.