Issue #20: bimodal winner distributions, 3-2-1, partisan primaries, Score/STAR ranges#50
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endolith wants to merge 2 commits into
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Issue #20: bimodal winner distributions, 3-2-1, partisan primaries, Score/STAR ranges#50endolith wants to merge 2 commits into
endolith wants to merge 2 commits into
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…ries - bimodal_electorate() for two spatial clusters; partisan primary/runoff helpers - three_two_one() with Good/OK/Bad ballots; honest_321_ratings() strategy - honest_normed_scores(max_score, *, min_score) with tied-utility rows at min - Examples: bimodal winner histograms (turnout variants), Score vs STAR ranges, bimodal approval strategy comparison - Tests for bimodal electorate, 3-2-1, and invalid tiebreakers Co-authored-by: endolith <endolith@gmail.com>
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- Exercise nominee_restricted_plurality, pairwise_majority, open/closed primaries, top_two_runoff_reduced_turnout (including sntv edge cases) - Cover three_two_one pairwise branches and 2D validation - Cover honest_321_ratings and honest_normed_scores invalid min/max range Co-authored-by: endolith <endolith@gmail.com>
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This PR implements the simulations and mechanisms discussed in issue #20.
Library additions
bimodal_electorate(elsim.elections): two voter clusters and matching candidate pools at ±separationon the first axis (documented indexing convention for left/right voters and candidates).three_two_one(elsim.methods): 3-2-1 voting with ballots encoded as 0 = Bad, 1 = OK, 2 = Good (party-specific semifinalist rules omitted as optional for simulations).honest_321_ratings(elsim.strategies): per-voter tertiles → Good/OK/Bad.honest_normed_scores(utilities, max_score=5, *, min_score=0): keyword-onlymin_score; keeps positionalmax_scorefor backward compatibility; rows with no utility spread map tomin_score.elsim.methods): restricted plurality nominees, pairwise majority between finalists, open vs closed partisan flows, top-two runoff with a subset electorate in the pairwise round.Examples (same style as
distributions_by_method.py)examples/bimodal_winner_distributions.py: 5k+5k voters, two clusters of candidates, dispersion 0.5, reduced-turnout primaries (~3.8k each side) and runoff (~8.5k voters). Methods include FPTP, contingent runoff, IRV, Score/STAR (0–5), Black, open/closed partisan primaries, 3-2-1, approval (mean threshold and vote-for-half), reference histograms.examples/score_star_range_comparison.py: same unimodal spatial setup as existing 1D plots; compares Score vs STAR for ranges 0–5, 0–10, 1–10, five-grade (0–4), 13- and 15-point scales.examples/bimodal_approval_strategy_comparison.py: optimal approval vs vote-for-half vs vote-for-one on the bimodal electorate.Tests
bimodal_electorateshape/means;three_two_oneedge cases; extended invalid tiebreaker coverage.All tests pass (
pytest tests/).