Dynamic Process: Robustness to the Share of Updating Voters (with CW) (C.2)

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from poisson_approval import *

N_SAMPLES = 10000
N_MAX_EPISODES = 1000

update_ratios = {
    '1': 1,
    '0.5': 0.5,
    'one_over_log_log_t_plus_fourteen': one_over_log_log_t_plus_fourteen,
    'one_over_log_t_plus_one': one_over_log_t_plus_one,
    'one_over_sqrt_t': one_over_sqrt_t,
    'one_over_t': one_over_t,
}

rand_profile = RandConditional(
    RandProfileHistogramUniform(n_bins=1),
    test=is_condorcet, n_trials_max=None
)

Condorcet consistency:

table_cond = pd.DataFrame()
table_cond.index.name = 'Share of updating voters'
for update_ratio_name, update_ratio in update_ratios.items():
    results = monte_carlo_fictitious_play(
        factory=rand_profile,
        n_samples=N_SAMPLES,
        n_max_episodes=N_MAX_EPISODES,
        voting_rules=VOTING_RULES,
        init='random_tau',
        ballot_update_ratio=update_ratio,
        monte_carlo_settings=[
            MCS_FREQUENCY_CW_WINS,
        ],
        file_save='sav/ballot_update_%s_with_CW.sav' % update_ratio_name,
    )
    for voting_rule in VOTING_RULES:
        table_cond.loc[update_ratio_name, voting_rule] = float(results[voting_rule]['mean_frequency_cw_wins'])
table_cond

	Approval	Plurality	Anti-plurality
Share of updating voters
1	0.997838	0.6670	0.102871
0.5	0.999552	0.6697	0.267519
one_over_log_log_t_plus_fourteen	0.999749	0.6730	0.257903
one_over_log_t_plus_one	0.999908	0.6628	0.496961
one_over_sqrt_t	0.999800	0.6697	0.570528
one_over_t	0.999700	0.6573	0.603082