sample_posterior {elections.dtree}

Draw election outcomes from the posterior distribution.

Description

sample_posterior draws sets of ballots from independent realizations of the Dirichlet-tree posterior, then determines the probability for each candidate being elected by aggregating the results of the social choice function. See Everest et al. (2022) for details.

Usage

sample_posterior(
  dtree,
  n_elections,
  n_ballots,
  n_winners = 1,
  replace = FALSE,
  n_threads = NULL
)

Arguments

dtree

A dirichlet_tree object.

n_elections

An integer representing the number of elections to generate. A higher number yields higher precision in the output probabilities.

n_ballots

An integer representing the total number of ballots cast in the election.

n_winners

The number of candidates elected in each election.

replace

A boolean indicating whether or not we should re-use the observed ballots in the monte-carlo integration step to determine the posterior probabilities.

n_threads

The maximum number of threads for the process. The default value of NULL will default to 2 threads. Inf will default to the maximum available, and any value greater than or equal to the maximum available will result in the maximum available.

Value

A numeric vector containing the probabilities for each candidate being elected.

References

Everest F, Blom M, Stark PB, Stuckey PJ, Teague V, Vukcevic D (2023). “Ballot-Polling Audits of Instant-Runoff Voting Elections with a Dirichlet-Tree Model.” In Computer Security. ESORICS 2022 International Workshops, 525–540. ISBN 978-3-031-25460-4..

Everest F, Blom M, Stark PB, Stuckey PJ, Teague V, Vukcevic D (2022). “Auditing Ranked Voting Elections with Dirichlet-Tree Models: First Steps.” doi:10.15157/diss/021..