| Project/Area Number |
17K03550
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Politics
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| Research Institution | Yokohama City University |
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Principal Investigator |
Wada Junichiro 横浜市立大学, 国際商学部, 教授 (30244502)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | 大選挙区単記非移譲式投票制 / 小選挙区相対多数制 / グラフ理論 / 合区 / α-ダイバージェンス / 分離可能性 / 一票の平等 / 死票 / visual cue / 大選挙区 / 単記非移譲式投票 / カルバック=ライブラー-ダイバージェンス / f-ダイバージェンス / 個人還元主義 / 選挙研究 |
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| Outline of Final Research Achievements |
We argue that a difference in district magnitude - the number of seats per district - causes variations in the way voters are affected by smiles in their voting behaviors. Using data from local assembly elections in an urban area in Japan, where both SMDP and MMDs/SNTV are used, we showed how a candidate’s smile is effective in attracting votes in SMDP, but it does not increase the vote share in the MMDs/SNTV system.
We show how to decompose the divergence from party votes share to party seats share into the inequality of vote value, so-called wasted votes, and geographical lessoning with α-divergence. We decompose the results for the US congressional elections and Japanese congressional elections with this identity, but without any help of causal inference. Using the optimization model, we identified optimal combined districts and evaluated the effects of the combined districts.
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| Academic Significance and Societal Importance of the Research Achievements |
とかく神学論争になりやすい“小選挙区多数原理”と“大選挙区比例原理”の対立に対し、投票の質という観点から“大選挙区比例原理”の優位性を示した。
大選挙区と小選挙区が混在する広域自治体に置いて重要になる死票問題を一括して扱える一票の不平等指数の可能性を示した。また、参院選挙区選挙の大選挙区・合区の手法を確立することにより、47都道府県の選挙区割りを人為的な手段に頼ることなく、明示された制約、目的関数の下でいつでも最適化することができる手段を確立した。
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