2019\u5e7412\u670811\u65e5\u306812\u65e5\u3001\u65e9\u7a32\u7530\u5927\u5b66\u30b9\u30fc\u30d1\u30fc\u30b0\u30ed\u30fc\u30d0\u30eb\u5927\u5b66\u5275\u6210\u652f\u63f4\u4e8b\u696d\u5b9f\u8a3c\u653f\u6cbb\u7d4c\u6e08\u5b66\u62e0\u70b9\u306f\u65e9\u7a32\u7530\u5927\u5b66\u5730\u57df\u30fb\u5730\u57df\u9593\u7814\u7a76\u6a5f\u69cb\u3068\u3068\u3082\u306b\u3001\u4e16\u754c\u9280\u884c\u30b3\u30f3\u30b5\u30eb\u30bf\u30f3\u30c8\u306eMomar Dieng\u535a\u58eb\u3092\u304a\u8fce\u3048\u3057\u7d71\u8a08\u30bb\u30df\u30ca\u30fc\u3092\u5b9f\u65bd\u3057\u307e\u3059\u3002\u521d\u65e5\u306f\u4e3b\u306b\u30af\u30a4\u30c3\u30af\u30fb\u30ab\u30a6\u30f3\u30c8\u3068\u9078\u6319\u79d1\u5b66\u8abf\u67fb\u306b\u7126\u70b9\u3092\u7f6e\u304d\u3001Stata\u3092\u7528\u3044\u305f\u5b9f\u7fd2\u3092\u884c\u3044\u307e\u3059\u3002\u7fcc\u65e5\u306b\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u306b\u3088\u308b\u9078\u6319\u4e88\u6e2c\u306b\u7126\u70b9\u3092\u7f6e\u3044\u305f\u30bb\u30df\u30ca\u30fc\u3092\u958b\u50ac\u81f4\u3057\u307e\u3059\u3002\u524d\u65e5\u306e\u30bb\u30df\u30ca\u30fc\u306b\u3054\u53c2\u52a0\u3067\u304d\u306a\u304b\u3063\u305f\u65b9\u3082\u3001\u662f\u975e\u3054\u53c2\u52a0\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u3010\u65e5\u6642\u301112\u670811\u65e5\uff08\u6c34\uff0910:40-14:30\u00a0 \uff08\u4f11\u61a9: 12:10-13:00\uff09\u300112\u670812\u65e5\uff08\u6728\uff0910:40-14:30\u00a0 \uff08\u4f11\u61a9: 12:10-13:00\uff09<\/p>\n
\u3010\u5834\u6240\u3011\u65e9\u7a32\u7530\u30ad\u30e3\u30f3\u30d1\u30b9 3\u53f7\u99288\u968e804\u6559\u5ba4<\/p>\n
\u3010\u8a00\u8a9e\u3011\u82f1\u8a9e<\/p>\n
\u3010\u5bfe\u8c61\u3011\u65e9\u7a32\u7530\u5927\u5b66\u5b66\u90e8\u751f\u3001\u5927\u5b66\u9662\u751f\u3001\u6559\u8077\u54e1\u3001\u4e00\u822c<\/p>\n
\u3010\u4e8b\u524d\u7533\u8fbc\u3011\u5fc5\u8981<\/p>\n
\u30bb\u30df\u30ca\u30fc\u5f53\u65e5\u306e\u30c7\u30fc\u30bf\u5171\u6709\u306e\u305f\u3081\u3001\u53c2\u52a0\u8005\u306f12\u67089\u65e5\u307e\u3067\u306b\u4ee5\u4e0b\u306e\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9\u307e\u3067\u304a\u540d\u524d\u3001\u3054\u6240\u5c5e\u3001\u30bb\u30df\u30ca\u30fc\u53c2\u52a0\u65e5\uff0812\u670811\u65e5 and\/or 12\u670812\u65e5\uff09\u3092\u304a\u9001\u308a\u304f\u3060\u3055\u3044\u3002<\/p>\n
takashi-wi\u25b2fuji.waseda.jp<\/a>\u00a0(\u25b2\u3092@\u306b\u5909\u63db\u3057\u3066\u3054\u4f7f\u7528\u304f\u3060\u3055\u3044\u3002)<\/p>\n \u3010\u767a\u8868\u8005\u3011Dr. Momar Dieng \u4e16\u754c\u9280\u884c\u30b3\u30f3\u30b5\u30eb\u30bf\u30f3\u30c8<\/p>\n <\/p>\n 12<\/strong>\u6708<\/strong>11<\/strong>\u65e5\uff08\u6c34\uff09<\/strong>10:40-14:30\u00a0<\/strong><\/p>\n \u3010\u30bf\u30a4\u30c8\u30eb\u3011<\/strong>Election-Night Forecasting I: Statistical methods in election observation<\/strong><\/p>\n Part 1 (10:40-12:10): Introduction to the quick count: overall methodology and statistical background<\/p>\n Part 2 (13:00-14:30): Quick count simulation exercise and related data forensics<\/p>\n \u3010\u8981\u65e8\u3011<\/p>\n Parallel Vote Tabulation (PVT), also known as a Quick Count, is an election observation methodology that forecasts election results based on a random, statistically representative sample of polling stations. It is used by political parties, civil society organizations and international election observers for independent verification (or challenge) of election results. This seminar will describe in detail how to plan for and conduct a quick count, including a hands-on simulation of the process with real election data. It will also introduce selected forensic data analysis techniques that can be used to audit the full election results once they are available.<\/p>\n \u30d5\u30e9\u30a4\u30e4\u30fc\u306f\u3053\u3061\u3089<\/a><\/p>\n 12<\/strong>\u6708<\/strong>12<\/strong>\u65e5\uff08\u6728\uff09<\/strong>10:40-14:30<\/strong><\/p>\n \u3010\u30bf\u30a4\u30c8\u30eb\u3011<\/strong>Election-Night Forecasting II: Machine learning methods in social science<\/strong><\/p>\n Part 1 (10:40-12:10): Introduction to machine learning methods (clustering algorithms)<\/p>\n Part 2 (13:00-14:30): Application to election-night forecasting (simulation exercise)<\/p>\n \u3010\u8981\u65e8\u3011<\/p>\n Artificial intelligence methods are increasingly used in the social sciences to tackle challenging predictive problems. The forecasting of election results is one such area of active research that is fueled by the impact of social media on politics, and the availability of near real-time information on, and from political actors and processes in unprecedented volumes and variety. In this seminar we will first review existing methodologies for pre-election forecasts (including exit polls) and election-night forecasts. We will then focus on election-night forecasting and explain how machine learning can be used to obtain very accurate predictions in record time from results obtained in an uncontrolled, and often non-random way, such as those reported by the news media. This stands in contrast to the random sampling conducted in classical parallel vote tabulations which relies on a predetermined set of polling stations. We will apply different machine learning (clustering) algorithms to real election returns data set and assess the performance of the different approaches by comparing their convergence to the results.<\/p>\n
![\u30d5\u30e9\u30a4\u30e4\u30fc\u306f\u3053\u3061\u3089<\/a><\/p>\n12<\/strong>\u6708<\/strong>12<\/strong>\u65e5\uff08\u6728\uff09<\/strong>10:40-14:30<\/strong><\/p>\n\u3010\u30bf\u30a4\u30c8\u30eb\u3011<\/strong>Election-Night Forecasting II: Machine learning methods in social science<\/strong><\/p>\nPart 1 (10:40-12:10): Introduction to machine learning methods (clustering algorithms)<\/p>\nPart 2 (13:00-14:30): Application to election-night forecasting (simulation exercise)<\/p>\n\u3010\u8981\u65e8\u3011<\/p>\nArtificial intelligence methods are increasingly used in the social sciences to tackle challenging predictive problems. The forecasting of election results is one such area of active research that is fueled by the impact of social media on politics, and the availability of near real-time information on, and from political actors and processes in unprecedented volumes and variety. In this seminar we will first review existing methodologies for pre-election forecasts (including exit polls) and election-night forecasts. We will then focus on election-night forecasting and explain how machine learning can be used to obtain very accurate predictions in record time from results obtained in an uncontrolled, and often non-random way, such as those reported by the news media. This stands in contrast to the random sampling conducted in classical parallel vote tabulations which relies on a predetermined set of polling stations. We will apply different machine learning (clustering) algorithms to real election returns data set and assess the performance of the different approaches by comparing their convergence to the results.<\/p>\n\u30d5\u30e9\u30a4\u30e4\u30fc\u306f\u3053\u3061\u3089<\/a><\/p>\n\u3010\u767a\u8868\u8005\u30d7\u30ed\u30d5\u30a3\u30fc\u30eb\u3011<\/p>\nDieng\u535a\u58eb\u306f\u3001\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u5927\u5b66\u30c7\u30a3\u30d3\u30b9\u6821\u306b\u3066\u6570\u5b66\u306e\u535a\u58eb\u53f7\u3092\u53d6\u5f97\u3057\u3001\u30cf\u30fc\u30d0\u30fc\u30c9\u5927\u5b66\u30b1\u30cd\u30c7\u30a3\u30fc\u30b9\u30af\u30fc\u30eb\u306b\u3066\u516c\u5171\u653f\u7b56\u30fb\u958b\u767a\u63f4\u52a9\u3092\u5b66\u3073\u3001\u30cf\u30fc\u30d0\u30fc\u30c9\u5927\u5b66\u6559\u80b2\u7814\u7a76\u79d1\u306b\u3066\u56fd\u969b\u6559\u80b2\u653f\u7b56\u306e\u4fee\u58eb\u53f7\u3092\u53d6\u5f97\u3057\u3066\u3044\u308b\u3002\u30ea\u30d9\u30ea\u30a2\u306e\u56fd\u9023\u958b\u767a\u8a08\u753b\uff08UNDP\uff09\u306b\u3066\u4e0a\u7d1a\u653f\u7b56\u30a2\u30c9\u30d0\u30a4\u30b6\u30fc\u3001\u30cf\u30fc\u30d0\u30fc\u30c9\u5927\u5b66\u30fb\u56fd\u969b\u958b\u767a\u30bb\u30f3\u30bf\u30fc\u3067\u7814\u7a76\u54e1\u3001\u30bb\u30cd\u30ac\u30eb\u653f\u5e9c\u306e\u6559\u80b2\u5927\u81e3\u306e\u4e0a\u7d1a\u30a2\u30c9\u30d0\u30a4\u30b6\u30fc\u3092\u6b74\u4efb\u3057\u3001\u4e16\u754c\u9280\u884c\u306e\u30b3\u30f3\u30b5\u30eb\u30bf\u30f3\u30c8\u3092\u52d9\u3081\u308b\u3002\u958b\u767a\u63f4\u52a9\u3084\u6559\u80b2\u653f\u7b56\u4ee5\u5916\u306b\u3082\u3001\u7d71\u8a08\u306e\u5c02\u9580\u77e5\u8b58\u304c\u3042\u308a\u3001\u6bce\u5e74\u30cf\u30fc\u30d0\u30fc\u30c9\u5927\u5b66\u3067\u3001\u91cf\u7684\u5206\u6790\u306e\u8b1b\u7fa9\u3092\u884c\u3063\u3066\u3044\u308b\u3002\u9078\u6319\u79d1\u5b66\u8abf\u67fb\u3084\u9078\u6319\u4e88\u6e2c\u3082\u5c02\u9580\u3068\u3057\u30012016\u5e74\u306b\u306f\u30a2\u30ea\u30be\u30ca\u5927\u5b66\u306eBartlett Memorial Lecturer\u306b\u9078\u51fa\u3055\u308c\u305f\u3002<\/p>\n\u3010\u554f\u5408\u305b\u3011takashi-wi\u25b2fuji.waseda.jp<\/a>\u00a0(\u25b2\u3092@\u306b\u5909\u63db\u3057\u3066\u3054\u4f7f\u7528\u304f\u3060\u3055\u3044\u3002)<\/p>\n\u3010\u5171\u50ac\u3011\u65e9\u7a32\u7530\u5927\u5b66\u5730\u57df\u30fb\u5730\u57df\u9593\u7814\u7a76\u6a5f\u69cb<\/p>\n","protected":false},"excerpt":{"rendered":"2019\u5e7412\u670811\u65e5\u306812\u65e5\u3001\u65e9\u7a32\u7530\u5927\u5b66\u30b9\u30fc\u30d1\u30fc\u30b0\u30ed\u30fc\u30d0\u30eb\u5927\u5b66\u5275\u6210\u652f\u63f4\u4e8b\u696d\u5b9f\u8a3c\u653f\u6cbb\u7d4c\u6e08\u5b66\u62e0\u70b9\u306f\u65e9\u7a32\u7530\u5927\u5b66\u5730\u57df\u30fb\u5730\u57df\u9593\u7814\u7a76\u6a5f\u69cb\u3068\u3068\u3082\u306b\u3001\u4e16\u754c\u9280\u884c\u30b3\u30f3\u30b5\u30eb\u30bf\u30f3\u30c8\u306eMomar Dieng\u535a\u58eb\u3092\u304a\u8fce\u3048\u3057\u7d71\u8a08\u30bb\u30df\u30ca\u30fc\u3092\u5b9f\u65bd\u3057\u307e\u3059 […]<\/p>\n","protected":false},"author":3,"featured_media":6664,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[53,98],"class_list":["post-6658","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-through-2023","tag-events","tag-eape"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/posts\/6658","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/comments?post=6658"}],"version-history":[{"count":2,"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/posts\/6658\/revisions"}],"predecessor-version":[{"id":6669,"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/posts\/6658\/revisions\/6669"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/media\/6664"}],"wp:attachment":[{"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/media?parent=6658"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/categories?post=6658"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/sgu\/wp-json\/wp\/v2\/tags?post=6658"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/www.waseda.jp\/inst\/sgu\/assets\/uploads\/2019\/11\/19.11.20.SGU_Seminar_I-1.png\"){kind=link}