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feat(studies): use approval_at_optimal in Merrill tables 1/2/4 (split 3/3 of #56) #84

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feat(studies): use approval_at_optimal in Merrill tables 1/2/4 (split 3/3 of #56) #84

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feat(studies): promote approval_at_optimal to a public rated-method h…
endolith Aug 1, 2026
f8f4f8d
examples: define Merrill table methods locally using approval_at_optimal
endolith Aug 2, 2026
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7 changes: 6 additions & 1 deletion elsim/studies/__init__.py
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Expand Up @@ -13,7 +13,11 @@
"""

from .backends import JoblibBackend, SerialBackend
from .condorcet_metrics import merrill_1984_comparison_methods, tally_condorcet_agreement
from .condorcet_metrics import (
approval_at_optimal,
merrill_1984_comparison_methods,
tally_condorcet_agreement,
)
from .parameters import expand_product, expand_rows, expand_zip
from .runner import merge_counters, run_batched
from .social_utility import (
Expand All @@ -31,6 +35,7 @@
"merge_counters",
"run_batched",
"merrill_1984_comparison_methods",
"approval_at_optimal",
"tally_condorcet_agreement",
"spatial_random_reference_utility_updates",
"random_society_utility_updates",
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13 changes: 10 additions & 3 deletions elsim/studies/condorcet_metrics.py
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Expand Up @@ -19,7 +19,14 @@
RatedMethod = Callable[..., Optional[int]]


def _approval_at_optimal(utilities: np.ndarray, tiebreaker: str) -> Optional[int]: # noqa: UP045
def approval_at_optimal(utilities: np.ndarray, tiebreaker: str = "random") -> Optional[int]: # noqa: UP045
"""
Rated-method helper: build an optimal approval ballot, then apply :func:`elsim.methods.approval`.

Intended for use in a ``rated_methods`` mapping passed to
:func:`tally_condorcet_agreement` or the spatial sweep helpers, so scripts
stay declarative without repeating lambdas.
"""
return approval(approval_optimal(utilities), tiebreaker)


Expand All @@ -44,7 +51,7 @@ def merrill_1984_comparison_methods() -> tuple[dict[str, RankedMethod], dict[str
}
rated_methods: dict[str, RatedMethod] = {
"SU max": utility_winner,
"Approval": _approval_at_optimal,
"Approval": approval_at_optimal,
}
return ranked_methods, rated_methods

Expand Down Expand Up @@ -76,7 +83,7 @@ def tally_condorcet_agreement(
Returns
-------
collections.Counter
Includes key ``\"CW\"`` when a Condorcet winner exists, plus one key per
Includes key ``"CW"`` when a Condorcet winner exists, plus one key per
supplied method name when that method's winner matches the Condorcet
winner.
"""
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9 changes: 7 additions & 2 deletions examples/merrill_1984_table_1_fig_1.py
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Expand Up @@ -32,14 +32,19 @@
from tabulate import tabulate

from elsim.elections import random_utilities
from elsim.methods import black, borda, coombs, fptp, irv, runoff, utility_winner
from elsim.strategies import honest_rankings
from elsim.studies import merrill_1984_comparison_methods, tally_condorcet_agreement
from elsim.studies import approval_at_optimal, tally_condorcet_agreement

n_elections = 10_000 # Roughly 15 seconds on a 2019 6-core i7-9750H
n_voters = 25
n_cands_list = (2, 3, 4, 5, 7, 10)

ranked_methods, rated_methods = merrill_1984_comparison_methods()
ranked_methods = {'Plurality': fptp, 'Runoff': runoff, 'Hare': irv,
'Borda': borda, 'Coombs': coombs, 'Black': black}

rated_methods = {'SU max': utility_winner,
'Approval': approval_at_optimal}

condorcet_winner_count = {key: Counter() for key in (
ranked_methods.keys() | rated_methods.keys() | {'CW'})}
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9 changes: 7 additions & 2 deletions examples/merrill_1984_table_2.py
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Expand Up @@ -38,14 +38,19 @@
from tabulate import tabulate

from elsim.elections import normal_electorate, normed_dist_utilities
from elsim.methods import black, borda, coombs, fptp, irv, runoff, utility_winner
from elsim.strategies import honest_rankings
from elsim.studies import expand_rows, merrill_1984_comparison_methods, tally_condorcet_agreement
from elsim.studies import approval_at_optimal, expand_rows, tally_condorcet_agreement

n_elections = 10_000 # Roughly 60 seconds on a 2019 6-core i7-9750H
n_voters = 201
n_cands = 5

ranked_methods, rated_methods = merrill_1984_comparison_methods()
ranked_methods = {'Plurality': fptp, 'Runoff': runoff, 'Hare': irv,
'Borda': borda, 'Coombs': coombs, 'Black': black}

rated_methods = {'SU max': utility_winner,
'Approval': approval_at_optimal}

# disp, corr, D
condition_rows = ((1.0, 0.5, 2),
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9 changes: 7 additions & 2 deletions examples/merrill_1984_table_4.py
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Expand Up @@ -36,18 +36,23 @@
from tabulate import tabulate

from elsim.elections import normal_electorate, normed_dist_utilities
from elsim.methods import black, borda, coombs, fptp, irv, runoff, utility_winner
from elsim.strategies import honest_rankings
from elsim.studies import (
approval_at_optimal,
expand_rows,
merrill_1984_comparison_methods,
spatial_random_reference_utility_updates,
)

n_elections = 10_000 # Roughly 60 seconds on a 2019 6-core i7-9750H
n_voters = 201
n_cands = 5

ranked_methods, rated_methods = merrill_1984_comparison_methods()
ranked_methods = {'Plurality': fptp, 'Runoff': runoff, 'Hare': irv,
'Borda': borda, 'Coombs': coombs, 'Black': black}

rated_methods = {'SU max': utility_winner,
'Approval': approval_at_optimal}

# disp, corr, D
condition_rows = ((1.0, 0.5, 2),
Expand Down