{"id":4335,"date":"2026-04-07T00:03:54","date_gmt":"2026-04-06T15:03:54","guid":{"rendered":"https:\/\/blog.id774.net\/entry\/?p=4335"},"modified":"2026-04-07T00:16:13","modified_gmt":"2026-04-06T15:16:13","slug":"%e6%8a%bd%e8%b1%a1%e5%93%b2%e5%ad%a6%e3%82%92%e9%81%8b%e7%94%a8%e5%8f%af%e8%83%bd%e3%81%aa%e3%83%a2%e3%83%87%e3%83%ab%e3%81%b8%e5%a4%89%e6%8f%9b%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/blog.id774.net\/entry\/2026\/04\/07\/4335\/","title":{"rendered":"\u62bd\u8c61\u54f2\u5b66\u3092\u904b\u7528\u53ef\u80fd\u306a\u30e2\u30c7\u30eb\u3078\u5909\u63db\u3059\u308b"},"content":{"rendered":"

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\n

1. \u554f\u984c\u8a2d\u5b9a\u2015\u2015\u306a\u305c\u54f2\u5b66\u306f\u305d\u306e\u307e\u307e\u3067\u306f\u5b9f\u88c5\u3067\u304d\u306a\u3044\u306e\u304b<\/h2>\n

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\u6982\u5ff5<\/td>\n\u610f\u5473\u3001\u672c\u8cea\u3001\u7406\u89e3\u3001\u504f\u898b<\/td>\n\u5909\u6570\u3001\u72b6\u614b\u3001\u6f14\u7b97\u3001\u8a55\u4fa1\u95a2\u6570<\/td>\n\u6982\u5ff5\u3092\u5909\u6570\u3078\u843d\u3068\u3059<\/td>\n<\/tr>\n
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\u59a5\u5f53\u6027<\/td>\n\u8aac\u5f97\u529b\u3001\u6574\u5408\u6027<\/td>\n\u518d\u73fe\u6027\u3001\u89b3\u6e2c\u53ef\u80fd\u6027\u3001\u30ed\u30b0\u63a5\u7d9a<\/td>\n\u59a5\u5f53\u6027\u3092\u6e2c\u5b9a\u3078\u843d\u3068\u3059<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

2. \u6700\u5c0f\u30e2\u30c7\u30eb\u2015\u2015\u72b6\u614b\u3001\u89b3\u6e2c\u3001\u69cb\u9020\u3001\u640d\u5931\u3001\u30d0\u30a4\u30a2\u30b9<\/h2>\n

\u4ee5\u4e0b\u3067\u306f\u6700\u5c0f\u5909\u6570\u7cfb\u3092 5 \u3064\u306b\u9650\u5b9a\u3059\u308b\u3002\u72b6\u614b \\(S_t\\)\u3001\u89b3\u6e2c \\(O_t\\)\u3001\u69cb\u9020\u62bd\u51fa \\(\\Phi\\)\u3001\u640d\u5931 \\(L_t\\)\u3001\u30d0\u30a4\u30a2\u30b9 \\(\\operatorname{LossBias}_t\\) \u3067\u3042\u308b\u3002\u54f2\u5b66\u7684\u8b70\u8ad6\u3092\u5b9f\u88c5\u3078\u63a5\u7d9a\u3059\u308b\u306b\u306f\u3001\u3053\u306e 5 \u3064\u3088\u308a\u5148\u306b\u6982\u5ff5\u3092\u5897\u3084\u3055\u306a\u3044\u65b9\u304c\u3088\u3044\u3002\u5148\u306b\u5909\u6570\u3092\u5897\u3084\u3059\u3068\u3001\u7406\u89e3\u3057\u305f<\/a>\u6c17\u306b\u306a\u308b\u3060\u3051\u3067\u3001\u89b3\u6e2c\u3068\u66f4\u65b0\u306e\u95a2\u4fc2\u304c\u304b\u3048\u3063\u3066\u4e0d\u660e\u306b\u306a\u308b\u304b\u3089\u3067\u3042\u308b\u3002<\/p>\n

\u3053\u3053\u3067\u91cd\u8981\u306a\u306e\u306f\u3001\\(\\Phi\\) \u3092\u300c\u305f\u3060\u306e\u7279\u5fb4\u91cf\u5909\u63db\u300d\u3068\u898b\u306a\u3044\u3053\u3068\u3067\u3042\u308b\u3002\\(\\Phi\\) \u306f\u3001\u96d1\u591a\u306a\u89b3\u6e2c\u304b\u3089\u3001\u518d\u5229\u7528\u53ef\u80fd\u306a\u69cb\u9020\u3060\u3051\u3092\u629c\u304d\u51fa\u3059\u5199\u50cf\u3067\u3042\u308b\u3002\u3057\u305f\u304c\u3063\u3066 \\(\\Phi\\) \u306f\u3001\u5727\u7e2e\u3068\u56e0\u679c\u62bd\u51fa\u306e\u53cc\u65b9\u3092\u62c5\u3046\u5fc5\u8981\u304c\u3042\u308b\u3002\u307e\u305f \\(O_t\\) \u306f\u5358\u4e00\u89b3\u6e2c\u8005\u306e\u8996\u70b9\u3067\u306f\u306a\u3044\u3002\u8907\u6570\u4e3b\u4f53\u3001\u8907\u6570\u88c5\u7f6e\u3001\u8907\u6570\u5236\u5ea6\u304c\u5206\u6563\u7684\u306b\u751f\u6210\u3059\u308b\u89b3\u6e2c\u306e\u675f\u3067\u3042\u308b\u3002\\(L_t\\) \u306f\u5358\u306a\u308b\u8aa4\u5dee\u3067\u306f\u306a\u304f\u3001\u76ee\u6a19\u3001\u5236\u7d04\u3001\u30b3\u30b9\u30c8\u3001\u6642\u9593\u3001\u518d\u4f5c\u696d\u3092\u542b\u3080\u5e83\u3044\u640d\u5931\u3067\u3042\u308a\u3001\\(\\operatorname{LossBias}_t\\) \u306f\u305d\u306e\u8a55\u4fa1\u306b\u5165\u308a\u8fbc\u3080\u975e\u5bfe\u79f0\u6027\u3067\u3042\u308b\u3002<\/p>\n\n\n\n\n\n\n\n\n\n
\u5909\u6570<\/th>\n\u5f79\u5272<\/th>\n\u54f2\u5b66\u7684\u5bfe\u5fdc<\/th>\n\u5b9f\u88c5\u4e0a\u306e\u610f\u5473<\/th>\n<\/tr>\n<\/thead>\n
\\(S_t\\)<\/td>\n\u7cfb\u306e\u5185\u90e8\u72b6\u614b<\/td>\n\u5b58\u5728\u3001\u914d\u7f6e\u3001\u5c40\u9762<\/td>\n\u30e2\u30c7\u30eb\u5185\u90e8\u306e\u72b6\u614b\u8868\u73fe<\/td>\n<\/tr>\n
\\(O_t\\)<\/td>\n\u5916\u90e8\u304b\u3089\u5f97\u3089\u308c\u308b\u89b3\u6e2c<\/td>\n\u8a8d\u8b58\u3001\u7d4c\u9a13\u3001\u77e5\u899a<\/td>\n\u30bb\u30f3\u30b5\u30fc\u5024\u3001\u6587\u7ae0\u3001\u30ed\u30b0\u3001\u8a55\u4fa1\u5165\u529b<\/td>\n<\/tr>\n
\\(\\Phi\\)<\/td>\n\u69cb\u9020\u62bd\u51fa\u6f14\u7b97<\/td>\n\u7406\u89e3\u3001\u672c\u8cea\u62bd\u51fa<\/td>\n\u5727\u7e2e\u5668\u3001\u8868\u73fe\u5b66\u7fd2\u5668\u3001\u56e0\u679c\u62bd\u51fa\u5668<\/td>\n<\/tr>\n
\\(L_t\\)<\/td>\n\u76ee\u6a19\u304b\u3089\u306e\u305a\u308c<\/td>\n\u5931\u6557\u3001\u8aa4\u308a\u3001\u6b20\u640d<\/td>\n\u8aa4\u5dee\u3001\u6642\u9593\u3001\u8cbb\u7528\u3001\u518d\u4f5c\u696d\u306e\u7dcf\u91cf<\/td>\n<\/tr>\n
\\(\\operatorname{LossBias}_t\\)<\/td>\n\u640d\u5931\u8a55\u4fa1\u306e\u6b6a\u307f<\/td>\n\u504f\u898b\u3001\u5148\u5165\u89b3\u3001\u6050\u308c<\/td>\n\u975e\u5bfe\u79f0\u91cd\u307f\u4ed8\u3051\u3001\u904e\u5270\u9632\u885b\u3001\u73fe\u72b6\u7dad\u6301\u50be\u5411<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

3. \u69cb\u9020\u62bd\u51fa \\(\\Phi\\)\u2015\u2015\u672c\u8cea\u3092\u6f14\u7b97\u3078\u843d\u3068\u3059<\/h2>\n

\u54f2\u5b66\u7684\u8a9e\u5f59\u3067\u3044\u3046\u300c\u672c\u8cea\u300d\u306f\u3001\u5b9f\u88c5\u306e\u6587\u8108\u3067\u306f\u300c\u5727\u7e2e\u3057\u3066\u3082\u5931\u308f\u308c\u306a\u3044\u652f\u914d\u69cb\u9020\u300d\u3068\u8a00\u3044\u63db\u3048\u3089\u308c\u308b\u3002\u60c5\u5831\u7406\u8ad6\u306e\u89b3\u70b9\u304b\u3089\u898b\u308c\u3070\u3001\u826f\u3044\u69cb\u9020\u8868\u73fe\u3068\u306f\u3001\u5143\u306e\u89b3\u6e2c\u306b\u542b\u307e\u308c\u308b\u5197\u9577\u3055\u3092\u524a\u308a\u3064\u3064\u3001\u5c06\u6765\u306e\u4e88\u6e2c\u3084\u518d\u69cb\u6210\u306b\u5fc5\u8981\u306a\u60c5\u5831\u3092\u4fdd\u6301\u3059\u308b\u8868\u73fe\u3067\u3042\u308b[3]<\/a>[4]<\/a>\u3002\u6700\u77ed\u8a18\u8ff0\u9577\u306e\u8003\u3048\u65b9\u306f\u3001\u305d\u306e\u65b9\u5411\u3092\u3055\u3089\u306b\u660e\u78ba\u306b\u3057\u305f\u3002\u826f\u3044\u30e2\u30c7\u30eb\u3068\u306f\u3001\u30c7\u30fc\u30bf\u3092\u77ed\u304f\u8a18\u8ff0\u3057\u3064\u3064\u3001\u904e\u5270\u9069\u5408\u3092\u907f\u3051\u308b\u30e2\u30c7\u30eb\u3067\u3042\u308b[9]<\/a>\u3002\u307e\u305f\u3001\u60c5\u5831\u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u306e\u8b70\u8ad6\u306f\u3001\u4e88\u6e2c\u306b\u5fc5\u8981\u306a\u60c5\u5831\u3092\u4fdd\u3061\u306a\u304c\u3089\u8868\u73fe\u3092\u5727\u7e2e\u3059\u308b\u3068\u3044\u3046\u8996\u70b9\u3092\u4e0e\u3048\u3001\u3053\u306e\u7a2e\u306e\u69cb\u9020\u62bd\u51fa\u3092\u60c5\u5831\u7406\u8ad6\u7684\u306b\u6349\u3048\u76f4\u3057\u3066\u3044\u308b[15]<\/a>\u3002<\/p>\n

\u3057\u304b\u3057\u5727\u7e2e\u3060\u3051\u3067\u306f\u3001\u300c\u77ed\u304f\u8a00\u3044\u63db\u3048\u305f\u3060\u3051\u300d\u3067\u7d42\u308f\u308b\u3002\u54f2\u5b66\u3092\u5b9f\u88c5\u53ef\u80fd\u306b\u3059\u308b\u306b\u306f\u3001\u305d\u306e\u5727\u7e2e\u304c\u4ecb\u5165\u53ef\u80fd\u6027\u3092\u4fdd\u5b58\u3057\u3066\u3044\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u3059\u306a\u308f\u3061\u3001\\(\\Phi\\) \u306f\u76f8\u95a2\u306e\u584a\u3092\u4f5c\u308b\u3060\u3051\u3067\u306f\u4e0d\u5341\u5206\u3067\u3001\u300c\u3069\u306e\u69cb\u9020\u3092\u52d5\u304b\u3059\u3068\u3001\u3069\u306e\u72b6\u614b\u304c\u5909\u308f\u308b\u304b\u300d\u3068\u3044\u3046\u9aa8\u683c\u3092\u4fdd\u305f\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044[5]<\/a>[10]<\/a>\u3002\u3053\u306e\u305f\u3081 \\(\\Phi\\) \u306f\u3001\u518d\u69cb\u6210\u8aa4\u5dee\u3060\u3051\u3067\u306a\u304f\u3001\u56e0\u679c\u7684\u4e00\u8cab\u6027\u3082\u540c\u6642\u306b\u6700\u9069\u5316\u3059\u308b\u6f14\u7b97\u3068\u3057\u3066\u5b9a\u7fa9\u3059\u308b\u306e\u304c\u81ea\u7136\u3067\u3042\u308b\u3002<\/p>\n

\n\\[
\nZ_t = \\Phi(O_t)
\n\\]
\n\\[
\n\\Phi^\\ast = \\arg\\min_{\\Phi} \\left[ L_{\\mathrm{recon}}(O_t,\\hat{O}_t) + \\lambda L_{\\mathrm{causal}}(Z_t) \\right]
\n\\]\n<\/div>\n

\u3053\u3053\u3067 \\(L_{\\mathrm{recon}}\\) \u306f\u518d\u69cb\u6210\u8aa4\u5dee\u3001\\(L_{\\mathrm{causal}}\\) \u306f\u4ecb\u5165\u3084\u5206\u5e03\u5909\u5316\u306b\u5bfe\u3059\u308b\u4e0d\u5909\u6027\u306e\u6b20\u5982\u3092\u8868\u3059\u9805\u3067\u3042\u308b\u3002\u5b9f\u88c5\u4e0a\u306f\u3001\u81ea\u5df1\u7b26\u53f7\u5316\u5668\u3084\u5909\u5206\u81ea\u5df1\u7b26\u53f7\u5316\u5668\u306e\u3088\u3046\u306a\u8868\u73fe\u5b66\u7fd2\u304c\u5727\u7e2e\u306e\u5074\u3092\u62c5\u3044[11]<\/a>\u3001\u56e0\u679c\u30e2\u30c7\u30eb\u3084\u6642\u7cfb\u5217\u56e0\u679c\u89e3\u6790\u304c\u69cb\u9020\u306e\u5074\u3092\u62c5\u3046[5]<\/a>[6]<\/a>\u3002\u54f2\u5b66\u3067\u3044\u3046\u7406\u89e3\u3068\u306f\u3001\u96d1\u591a\u306a\u89b3\u6e2c\u3092\u77ed\u304f\u8981\u7d04\u3059\u308b\u80fd\u529b\u3067\u306f\u306a\u304f\u3001\u4ecb\u5165\u3057\u3066\u3082\u58ca\u308c\u306b\u304f\u3044\u69cb\u9020\u3092\u62bd\u51fa\u3059\u308b\u80fd\u529b\u3067\u3042\u308b\u3002<\/p>\n\n\n\n\n\n\n\n
\\(\\Phi\\) \u306e\u6a5f\u80fd<\/th>\n\u5727\u7e2e\u3068\u3057\u3066\u306e\u5074\u9762<\/th>\n\u56e0\u679c\u3068\u3057\u3066\u306e\u5074\u9762<\/th>\n\u5b9f\u88c5\u5019\u88dc<\/th>\n<\/tr>\n<\/thead>\n
\u5197\u9577\u6027\u306e\u524a\u6e1b<\/td>\n\u8a18\u8ff0\u9577\u306e\u77ed\u7e2e<\/td>\n\u4e0d\u8981\u5909\u6570\u306e\u9664\u53bb<\/td>\nMDL\u3001PCA\u3001\u8868\u73fe\u5b66\u7fd2<\/td>\n<\/tr>\n
\u652f\u914d\u69cb\u9020\u306e\u62bd\u51fa<\/td>\n\u5c11\u6570\u5909\u6570\u3078\u306e\u5c04\u5f71<\/td>\n\u4ecb\u5165\u3067\u52b9\u304f\u9aa8\u683c\u306e\u4fdd\u6301<\/td>\nSCM\u3001Granger \u56e0\u679c\u3001\u8868\u73fe\u56e0\u679c\u7d71\u5408<\/td>\n<\/tr>\n
\u518d\u5229\u7528\u53ef\u80fd\u6027<\/td>\n\u518d\u69cb\u6210\u3068\u4e88\u6e2c\u306e\u7dad\u6301<\/td>\n\u5206\u5e03\u5909\u5316\u4e0b\u3067\u306e\u9811\u5065\u6027<\/td>\n\u56e0\u679c\u8868\u73fe\u5b66\u7fd2\u3001\u30c9\u30e1\u30a4\u30f3\u4e0d\u5909\u5b66\u7fd2<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

4. \u89b3\u6e2c\u306e\u5206\u6563\u2015\u2015\\(O_t\\) \u306f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u3042\u308b<\/h2>\n

\u54f2\u5b66\u3084\u8a8d\u8b58\u8ad6\u306e\u8b70\u8ad6\u3067\u306f\u3001\u3057\u3070\u3057\u3070\u300c\u4e3b\u4f53\u304c\u4e16\u754c\u3092\u8a8d\u8b58\u3059\u308b\u300d\u3068\u3044\u3046 1 \u5bfe 1 \u306e\u56f3\u5f0f\u304c\u524d\u63d0\u306b\u306a\u308b\u3002\u3057\u304b\u3057\u73fe\u5b9f\u306e\u8a8d\u8b58\u306f\u3001\u5358\u4e00\u4e3b\u4f53\u306e\u5185\u90e8\u3067\u5b8c\u7d50\u3057\u306a\u3044\u3002\u7d44\u7e54\u3067\u306f\u30ec\u30d3\u30e5\u30fc\u3001\u627f\u8a8d\u3001\u5831\u544a\u306e\u7d4c\u8def\u3092\u901a\u3058\u3066\u60c5\u5831\u304c\u5909\u5f62\u3055\u308c\u308b\u3002SNS \u3067\u306f\u30d5\u30a9\u30ed\u30fc\u95a2\u4fc2\u3084\u5f15\u7528\u95a2\u4fc2\u3092\u901a\u3058\u3066\u8a8d\u8b58\u304c\u5897\u5e45\u3055\u308c\u308b\u3002\u7814\u7a76\u3067\u306f\u8ad6\u6587\u3001\u67fb\u8aad\u3001\u5f15\u7528\u3001\u518d\u89e3\u6790\u3092\u901a\u3058\u3066\u77e5\u8b58\u304c\u518d\u7de8\u3055\u308c\u308b\u3002\u3064\u307e\u308a \\(O_t\\) \u306f\u5358\u72ec\u89b3\u6e2c\u3067\u306f\u306a\u304f\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u4e0a\u306b\u5206\u6563\u3057\u305f\u89b3\u6e2c\u306e\u5834\u3067\u3042\u308b[7]<\/a>[8]<\/a>[16]<\/a>[17]<\/a>\u3002<\/p>\n

\n\\[
\nO_t = \\{ O_t^{(i)} \\}_{i \\in V}
\n\\]
\n\\[
\nO_{t+1}^{(i)} = f\\Bigl(O_t^{(i)}, \\{O_t^{(j)}\\}_{j \\in N(i)}, S_t\\Bigr)
\n\\]\n<\/div>\n

\u3053\u306e\u8868\u73fe\u306e\u610f\u5473\u306f\u5927\u304d\u3044\u3002\u307e\u305a\u3001\u8aa4\u8a8d\u8b58\u306f\u5358\u7d14\u306a\u500b\u4eba\u306e\u8aa4\u308a\u3067\u306f\u306a\u304f\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u306e\u7d50\u679c\u3068\u3057\u3066\u8aac\u660e\u3067\u304d\u308b\u3002\u5bc6\u306a\u63a5\u7d9a\u306f\u53ce\u675f\u3092\u65e9\u3081\u308b\u304c\u3001\u8aa4\u308a\u3082\u901f\u304f\u62e1\u6563\u3055\u305b\u308b\u3002\u758e\u306a\u63a5\u7d9a\u306f\u5c40\u6240\u591a\u69d8\u6027\u3092\u4fdd\u3064\u304c\u3001\u6574\u5408\u5f62\u6210\u3092\u9045\u3089\u305b\u308b\u3002\u30cf\u30d6\u69cb\u9020\u306f\u60c5\u5831\u6d41\u901a\u3092\u52b9\u7387\u5316\u3059\u308b\u4e00\u65b9\u3001\u504f\u3063\u305f\u4e2d\u5fc3\u30ce\u30fc\u30c9\u3092\u901a\u3058\u3066\u5168\u4f53\u306e\u8a8d\u8b58\u3092\u3086\u304c\u3081\u308b[7]<\/a>[8]<\/a>\u3002\u54f2\u5b66\u3067\u3044\u3046\u5171\u540c\u4e3b\u89b3\u3084\u5236\u5ea6\u7684\u8a8d\u8b58\u306f\u3001\u3053\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u3068\u3057\u3066\u518d\u8a18\u8ff0\u3067\u304d\u308b\u3002<\/p>\n\n\n\n\n\n\n\n
\u89b3\u6e2c\u306e\u5f62<\/th>\n\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020<\/th>\n\u8d77\u3053\u308a\u3084\u3059\u3044\u73fe\u8c61<\/th>\n\u54f2\u5b66\u7684\u518d\u8a18\u8ff0<\/th>\n<\/tr>\n<\/thead>\n
\u4e2d\u592e\u96c6\u6a29\u578b<\/td>\n\u5c11\u6570\u30cf\u30d6\u306b\u4f9d\u5b58<\/td>\n\u9ad8\u901f\u4f1d\u64ad\u3068\u4e00\u6975\u504f\u5411<\/td>\n\u6a29\u5a01\u4f9d\u5b58\u7684\u8a8d\u8b58<\/td>\n<\/tr>\n
\u5206\u6563\u578b<\/td>\n\u591a\u6570\u4e3b\u4f53\u304c\u5c40\u6240\u63a5\u7d9a<\/td>\n\u5c40\u6240\u591a\u69d8\u6027\u3068\u53ce\u675f\u9045\u5ef6<\/td>\n\u591a\u5143\u7684\u8a8d\u8b58<\/td>\n<\/tr>\n
\u5c0f\u4e16\u754c\u578b<\/td>\n\u5c40\u6240\u96c6\u7a4d\u3068\u5c11\u6570\u30b7\u30e7\u30fc\u30c8\u30ab\u30c3\u30c8<\/td>\n\u9ad8\u901f\u540c\u671f\u3068\u30af\u30e9\u30b9\u30bf\u30fc\u7dad\u6301<\/td>\n\u5171\u540c\u4e3b\u89b3\u306e\u5f62\u6210\u3068\u5206\u6975\u306e\u4e21\u7acb<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

5. \u640d\u5931\u3068\u30d0\u30a4\u30a2\u30b9\u2015\u2015\u610f\u601d\u6c7a\u5b9a\u306e\u6b6a\u307f\u3092\u5909\u6570\u5316\u3059\u308b<\/h2>\n

\u7406\u89e3\u3084\u8a8d\u8b58\u3060\u3051\u3092\u30e2\u30c7\u30eb\u5316\u3057\u3066\u3082\u3001\u610f\u601d\u6c7a\u5b9a\u306e\u6b6a\u307f\u306f\u8aac\u660e\u3067\u304d\u306a\u3044\u3002\u4eba\u9593\u3084\u7d44\u7e54\u306f\u3001\u5358\u306b\u300c\u8aa4\u3063\u3066\u7406\u89e3\u3059\u308b\u300d\u306e\u3067\u306f\u306a\u304f\u3001\u300c\u640d\u3092\u6050\u308c\u308b\u4ed5\u65b9\u300d\u306b\u3088\u3063\u3066\u5224\u65ad\u3092\u5909\u3048\u308b\u3002\u3053\u3053\u3067\u5fc5\u8981\u306b\u306a\u308b\u306e\u304c \\(L_t\\) \u3068 \\(\\operatorname{LossBias}_t\\) \u3067\u3042\u308b\u3002\\(L_t\\) \u306f\u76ee\u6a19\u304b\u3089\u306e\u305a\u308c\u3067\u3042\u308a\u3001\\(\\operatorname{LossBias}_t\\) \u306f\u305d\u306e\u305a\u308c\u306e\u8a55\u4fa1\u306b\u5165\u308a\u8fbc\u3080\u975e\u5bfe\u79f0\u6027\u3067\u3042\u308b\u3002Kahneman \u3068 Tversky \u304c\u793a\u3057\u305f\u3088\u3046\u306b\u3001\u4eba\u9593\u306f\u540c\u3058\u671f\u5f85\u5024\u3067\u3082\u3001\u5229\u5f97\u3068\u3057\u3066\u63d0\u793a\u3055\u308c\u305f\u5834\u5408\u3068\u640d\u5931\u56de\u907f\u3068\u3057\u3066\u63d0\u793a\u3055\u308c\u305f\u5834\u5408\u3067\u9078\u629e\u3092\u5909\u3048\u308b[12]<\/a>\u3002\u307e\u305f Simon \u306e bounded rationality \u306f\u3001\u610f\u601d\u6c7a\u5b9a\u304c\u5b8c\u5168\u5408\u7406\u6027\u3067\u306f\u306a\u304f\u3001\u5236\u7d04\u4ed8\u304d\u63a2\u7d22\u3068\u3057\u3066\u884c\u308f\u308c\u308b\u3053\u3068\u3092\u5f37\u8abf\u3057\u305f[13]<\/a>\u3002\u3053\u306e\u89b3\u70b9\u306f\u3001\u5831\u916c\u6700\u5927\u5316\u3068\u8a66\u884c\u932f\u8aa4\u306e\u5f62\u5f0f\u3067\u610f\u601d\u6c7a\u5b9a\u3092\u6271\u3046\u5f37\u5316\u5b66\u7fd2\u3068\u3082\u63a5\u7d9a\u3067\u304d\u308b[18]<\/a>\u3002<\/p>\n

\n\\[
\nL_t = \\mathbb{E}\\bigl[\\ell(S_t, Z_t, a_t)\\bigr]
\n\\]
\n\\[
\n\\operatorname{LossBias}_t = \\alpha \\cdot \\mathrm{asym}(\\ell^+, \\ell^-)
\n\\]
\n\\[
\na_t^\\ast = \\arg\\min_{a} \\left( L_t(a) + \\operatorname{LossBias}_t(a) \\right)
\n\\]\n<\/div>\n

\u3053\u306e\u5f0f\u304c\u793a\u3057\u3066\u3044\u308b\u306e\u306f\u3001\u884c\u52d5\u304c\u300c\u4e16\u754c\u306e\u6b63\u3057\u3044\u8868\u73fe\u300d\u3060\u3051\u3067\u6c7a\u307e\u308b\u308f\u3051\u3067\u306f\u306a\u3044\u3068\u3044\u3046\u3053\u3068\u3067\u3042\u308b\u3002\u884c\u52d5\u306f\u3001\u640d\u5931\u3092\u3069\u3046\u6570\u3048\u308b\u304b\u3001\u305d\u306e\u640d\u5931\u3092\u3069\u308c\u307b\u3069\u6050\u308c\u308b\u304b\u3001\u3069\u306e\u7a0b\u5ea6\u306e\u63a2\u7d22\u30b3\u30b9\u30c8\u3092\u8a31\u5bb9\u3059\u308b\u304b\u306b\u3088\u3063\u3066\u6c7a\u307e\u308b\u3002\u3057\u305f\u304c\u3063\u3066\u54f2\u5b66\u3067\u3044\u3046\u504f\u898b\u3084\u5148\u5165\u89b3\u3084\u6050\u308c\u306f\u3001\u5358\u306a\u308b\u502b\u7406\u7684\u6b20\u9665\u3067\u306f\u306a\u304f\u3001\u640d\u5931\u95a2\u6570\u306e\u6b6a\u307f\u3068\u3057\u3066\u8868\u73fe\u3067\u304d\u308b\u3002\u969c\u5bb3\u5bfe\u5fdc\u3067\u904e\u5270\u9632\u885b\u306b\u8d70\u308b\u3001\u610f\u601d\u6c7a\u5b9a\u3067\u65b0\u898f\u6848\u3092\u907f\u3051\u308b\u3001\u5b9f\u9a13\u8a2d\u8a08\u3067\u5931\u6557\u3092\u6050\u308c\u3066\u63a2\u7d22\u3092\u6b62\u3081\u308b\u3001\u3068\u3044\u3063\u305f\u632f\u308b\u821e\u3044\u306f\u3001\\(\\operatorname{LossBias}_t\\) \u306e\u4e0a\u6607\u3068\u3057\u3066\u8aad\u3081\u308b\u3002<\/p>\n\n\n\n\n\n\n
\u9805<\/th>\n\u610f\u5473<\/th>\n\u5178\u578b\u4f8b<\/th>\n\u89b3\u6e2c\u53ef\u80fd\u306a\u6319\u52d5<\/th>\n<\/tr>\n<\/thead>\n
\\(L_t\\)<\/td>\n\u76ee\u6a19\u304b\u3089\u306e\u7dcf\u305a\u308c<\/td>\n\u8aa4\u5dee\u3001\u6642\u9593\u8d85\u904e\u3001\u518d\u4f5c\u696d\u3001\u8cbb\u7528\u5897<\/td>\n\u5931\u6557\u7387\u3001\u9045\u5ef6\u3001\u624b\u623b\u308a\u7387<\/td>\n<\/tr>\n
\\(\\operatorname{LossBias}_t\\)<\/td>\n\u640d\u5931\u56de\u907f\u306e\u975e\u5bfe\u79f0\u6027<\/td>\n\u73fe\u72b6\u7dad\u6301\u3001\u904e\u5270\u9632\u885b\u3001\u65b0\u898f\u56de\u907f<\/td>\n\u5909\u66f4\u5fcc\u907f\u3001\u904e\u5270 rollback\u3001\u63a2\u7d22\u7e2e\u5c0f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

6. \u6570\u5f0f\u30ec\u30d9\u30eb\u2015\u2015\u30e2\u30c7\u30eb\u306e\u610f\u5473\u3092\u56fa\u5b9a\u3059\u308b<\/h2>\n

\u3053\u3053\u307e\u3067\u306e\u8b70\u8ad6\u3092\u6570\u5f0f\u30ec\u30d9\u30eb\u3067\u4e00\u5ea6\u56fa\u5b9a\u3059\u308b\u3002\u6570\u5f0f\u306e\u5f79\u5272\u306f\u7f8e\u3057\u3055\u3067\u306f\u306a\u3044\u3002\u8a9e\u306e\u610f\u5473\u3092\u9003\u304c\u3055\u306a\u3044\u3053\u3068\u3067\u3042\u308b\u3002\u4ee5\u4e0b\u3067\u306f\u3001\u72b6\u614b\u66f4\u65b0\u3001\u69cb\u9020\u62bd\u51fa\u3001\u89b3\u6e2c\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3001\u640d\u5931\u6700\u9069\u5316\u3092\u6700\u5c0f\u9650\u306e\u5f62\u3067\u307e\u3068\u3081\u308b\u3002<\/p>\n

\u69cb\u9020\u62bd\u51fa\uff1a\u89b3\u6e2c\u304b\u3089\u518d\u5229\u7528\u53ef\u80fd\u306a\u69cb\u9020\u8868\u73fe\u3092\u5f97\u308b\u3002<\/p>\n

\n\\[
\nZ_t = \\Phi(O_t)
\n\\]\n<\/div>\n

\u5206\u6563\u89b3\u6e2c\uff1a\u8907\u6570\u4e3b\u4f53\u30fb\u8907\u6570\u88c5\u7f6e\u306b\u3088\u308b\u89b3\u6e2c\u306e\u96c6\u5408\u3068\u305d\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u3002<\/p>\n

\n\\[
\nO_t = \\{ O_t^{(i)} \\}_{i \\in V}, \\quad G = (V,E)
\n\\]\n<\/div>\n

\u89b3\u6e2c\u66f4\u65b0\uff1a\u72b6\u614b\u3068\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u4f9d\u5b58\u3057\u3066\u89b3\u6e2c\u304c\u5909\u5316\u3059\u308b\u3002<\/p>\n

\n\\[
\nO_{t+1} = F(O_t, S_t, G)
\n\\]\n<\/div>\n

\u72b6\u614b\u66f4\u65b0\uff1a\u69cb\u9020\u8868\u73fe\u3068\u884c\u70ba\u306b\u3088\u3063\u3066\u7cfb\u306e\u72b6\u614b\u304c\u66f4\u65b0\u3055\u308c\u308b\u3002<\/p>\n

\n\\[
\nS_{t+1} = M(S_t, Z_t, a_t)
\n\\]\n<\/div>\n

\u610f\u601d\u6c7a\u5b9a\uff1a\u640d\u5931\u3068\u305d\u306e\u30d0\u30a4\u30a2\u30b9\u3092\u542b\u3081\u305f\u8a55\u4fa1\u3092\u6700\u5c0f\u5316\u3059\u308b\u884c\u70ba\u3092\u9078\u629e\u3059\u308b\u3002<\/p>\n

\n\\[
\na_t^\\ast = \\arg\\min_a \\left( L_t(a) + \\operatorname{LossBias}_t(a) \\right)
\n\\]\n<\/div>\n

\u3053\u306e\u5f62\u306b\u3057\u3066\u304a\u304f\u3068\u3001\u54f2\u5b66\u7684\u6982\u5ff5\u306e\u5bfe\u5fdc\u5148\u304c\u660e\u78ba\u306b\u306a\u308b\u3002\u7406\u89e3\u306f \\(\\Phi\\)\u3001\u8a8d\u8b58\u306f \\(O_t\\)\u3001\u4e16\u754c\u50cf\u306f \\(Z_t\\)\u3001\u884c\u70ba\u306f \\(a_t\\)\u3001\u5931\u6557\u306f \\(L_t\\)\u3001\u504f\u898b\u306f \\(\\operatorname{LossBias}_t\\) \u3067\u3042\u308b\u3002\u3055\u3089\u306b\u3001Friston \u306e\u81ea\u7531\u30a8\u30cd\u30eb\u30ae\u30fc\u539f\u7406\u306e\u3088\u3046\u306a\u67a0\u7d44\u307f\u306f\u3001\u8a8d\u8b58\u3001\u884c\u70ba\u3001\u5b66\u7fd2\u3092\u7d71\u4e00\u7684\u306a\u6700\u9069\u5316\u554f\u984c\u3068\u3057\u3066\u6349\u3048\u308b\u65b9\u5411\u3092\u4e0e\u3048\u308b[14]<\/a>\u3002\u3053\u3053\u3067\u91cd\u8981\u306a\u306e\u306f\u3001\u54f2\u5b66\u3092\u305d\u306e\u307e\u307e\u795e\u7d4c\u79d1\u5b66\u3078\u9084\u5143\u3059\u308b\u3053\u3068\u3067\u306f\u306a\u3044\u3002\u6700\u9069\u5316\u3068\u66f4\u65b0\u306e\u4e00\u822c\u5f62\u5f0f\u3092\u5171\u6709\u3055\u305b\u308b\u3053\u3068\u3067\u3042\u308b\u3002<\/p>\n

\u4ee5\u4e0a\u3092\u307e\u3068\u3081\u308b\u3068\u3001\u672c\u7a3f\u306e\u6700\u5c0f\u30e2\u30c7\u30eb\u306f\u300c\u89b3\u6e2c \u2192 \u69cb\u9020\u62bd\u51fa \u2192 \u72b6\u614b\u66f4\u65b0 \u2192 \u610f\u601d\u6c7a\u5b9a\u300d\u3068\u3044\u3046\u9589\u30eb\u30fc\u30d7\u3068\u3057\u3066\u8a18\u8ff0\u3055\u308c\u308b\u3002<\/p>\n\n\n\n\n\n\n\n\n
\u54f2\u5b66\u8a9e<\/th>\n\u6570\u5f0f\u4e0a\u306e\u4f4d\u7f6e<\/th>\n\u610f\u5473\u306e\u56fa\u5b9a\u306e\u4ed5\u65b9<\/th>\n<\/tr>\n<\/thead>\n
\u7406\u89e3<\/td>\n\\(\\Phi\\)<\/td>\n\u89b3\u6e2c\u304b\u3089\u69cb\u9020\u3092\u62bd\u51fa\u3059\u308b\u6f14\u7b97\u3068\u3057\u3066\u56fa\u5b9a\u3059\u308b<\/td>\n<\/tr>\n
\u8a8d\u8b58<\/td>\n\\(O_t\\)<\/td>\n\u4e3b\u4f53\u9593\u306b\u5206\u6563\u3057\u305f\u89b3\u6e2c\u96c6\u5408\u3068\u3057\u3066\u56fa\u5b9a\u3059\u308b<\/td>\n<\/tr>\n
\u610f\u5473<\/td>\n\\(Z_t\\)<\/td>\n\u5727\u7e2e\u3068\u56e0\u679c\u306e\u4e21\u65b9\u3092\u4fdd\u6301\u3059\u308b\u8868\u73fe\u3068\u3057\u3066\u56fa\u5b9a\u3059\u308b<\/td>\n<\/tr>\n
\u5224\u65ad<\/td>\n\\(a_t\\)<\/td>\n\u640d\u5931\u3068\u30d0\u30a4\u30a2\u30b9\u306e\u548c\u3092\u6700\u5c0f\u5316\u3059\u308b\u9078\u629e\u3068\u3057\u3066\u56fa\u5b9a\u3059\u308b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

7. \u5b9f\u9a13\u8a2d\u8a08\u30ec\u30d9\u30eb\u2015\u2015\u6e2c\u308c\u308b\u5f62\u3078\u843d\u3068\u3059<\/h2>\n

\u6570\u5f0f\u304c\u3042\u308b\u3060\u3051\u3067\u306f\u3001\u307e\u3060\u7406\u8ad6\u306f\u9589\u3058\u306a\u3044\u3002\u6b21\u306b\u5fc5\u8981\u306a\u306e\u306f\u3001\u300c\u305d\u306e\u5909\u6570\u3092\u3069\u3046\u6e2c\u308b\u304b\u300d\u3067\u3042\u308b\u3002\u3053\u3053\u3067\u91cd\u8981\u306a\u306e\u306f\u3001\u54f2\u5b66\u7684\u6982\u5ff5\u306b\u5bfe\u5fdc\u3065\u3051\u305f\u5909\u6570\u3092\u3001\u5b9f\u9a13\u7684\u306b\u5206\u96e2\u53ef\u80fd\u306a\u64cd\u4f5c\u3078\u843d\u3068\u3059\u3053\u3068\u3060\u3002\u305f\u3068\u3048\u3070 \\(\\Phi\\) \u306f\u3001\u7570\u306a\u308b\u5727\u7e2e\u5668\u3084\u8868\u73fe\u5b66\u7fd2\u5668\u3092\u7528\u610f\u3057\u3001\u518d\u69cb\u6210\u7cbe\u5ea6\u3001\u5c06\u6765\u4e88\u6e2c\u7cbe\u5ea6\u3001\u4ecb\u5165\u5f8c\u306e\u9811\u5065\u6027\u3092\u6bd4\u8f03\u3059\u308b\u3053\u3068\u3067\u8a55\u4fa1\u3067\u304d\u308b\u3002\u3082\u3057\u3042\u308b\u8868\u73fe\u304c\u77ed\u3044\u8a18\u8ff0\u3092\u4e0e\u3048\u3066\u3082\u3001\u5c11\u3057\u5206\u5e03\u304c\u5909\u308f\u308b\u3060\u3051\u3067\u5d29\u308c\u308b\u306a\u3089\u3001\u305d\u308c\u306f\u672c\u8cea\u62bd\u51fa\u3067\u306f\u306a\u304f\u3001\u305f\u3060\u306e\u5c40\u6240\u7684\u8fd1\u4f3c\u3067\u3042\u308b\u3002<\/p>\n

\\(O_t\\) \u306b\u3064\u3044\u3066\u306f\u3001\u89b3\u6e2c\u4e3b\u4f53\u3092\u8907\u6570\u7528\u610f\u3057\u3001\u63a5\u7d9a\u69cb\u9020\u3092\u5909\u3048\u308c\u3070\u3088\u3044\u3002\u5bc6\u306a\u30b0\u30e9\u30d5\u3001\u758e\u306a\u30b0\u30e9\u30d5\u3001\u968e\u5c64\u30b0\u30e9\u30d5\u3001\u5c0f\u4e16\u754c\u30b0\u30e9\u30d5\u3092\u4f5c\u308a\u3001\u540c\u4e00\u306e\u5bfe\u8c61\u3092\u89b3\u6e2c\u3055\u305b\u3001\u610f\u898b\u53ce\u675f\u901f\u5ea6\u3001\u8aa4\u60c5\u5831\u6b8b\u5b58\u7387\u3001\u30af\u30e9\u30b9\u30bf\u5206\u6975\u5ea6\u3092\u6bd4\u8f03\u3059\u308b\u3002\u3059\u308b\u3068\u3001\u8aa4\u308a\u304c\u500b\u4eba\u7531\u6765\u306a\u306e\u304b\u3001\u69cb\u9020\u7531\u6765\u306a\u306e\u304b\u3092\u533a\u5225\u3057\u3084\u3059\u304f\u306a\u308b\u3002\\(L_t\\) \u306f\u30a8\u30e9\u30fc\u7387\u3001\u4f5c\u696d\u6642\u9593\u3001\u518d\u8a66\u884c\u56de\u6570\u3001\u4fee\u6b63\u56de\u6570\u3068\u3057\u3066\u89b3\u6e2c\u3067\u304d\u308b\u3002 \\(\\operatorname{LossBias}_t\\) \u306f gain framing \u3068 loss framing \u3092\u4e0e\u3048\u305f\u3068\u304d\u306e\u9078\u629e\u78ba\u7387\u5dee\u3084\u53cd\u5fdc\u6642\u9593\u5dee\u3068\u3057\u3066\u8fd1\u4f3c\u3067\u304d\u308b[12]<\/a>\u3002<\/p>\n\n\n\n\n\n\n\n\n
\u5bfe\u8c61<\/th>\n\u5b9f\u9a13\u64cd\u4f5c<\/th>\n\u4e3b\u8981\u6307\u6a19<\/th>\n\u898b\u305f\u3044\u3082\u306e<\/th>\n<\/tr>\n<\/thead>\n
\\(\\Phi\\)<\/td>\n\u5727\u7e2e\u5668\u3084\u8868\u73fe\u5b66\u7fd2\u5668\u3092\u5207\u308a\u66ff\u3048\u308b<\/td>\n\u518d\u69cb\u6210\u8aa4\u5dee\u3001\u4e88\u6e2c\u7cbe\u5ea6\u3001\u4ecb\u5165\u9811\u5065\u6027<\/td>\n\u672c\u8cea\u62bd\u51fa\u304b\u5358\u7d14\u8fd1\u4f3c\u304b<\/td>\n<\/tr>\n
\\(O_t\\)<\/td>\n\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u3092\u5909\u3048\u308b<\/td>\n\u53ce\u675f\u901f\u5ea6\u3001\u8aa4\u60c5\u5831\u6b8b\u5b58\u7387\u3001\u5206\u6975\u5ea6<\/td>\n\u8a8d\u8b58\u306e\u6b6a\u307f\u304c\u69cb\u9020\u7531\u6765\u304b\u3069\u3046\u304b<\/td>\n<\/tr>\n
\\(L_t\\)<\/td>\n\u8ab2\u984c\u96e3\u5ea6\u3084\u5236\u7d04\u3092\u5909\u3048\u308b<\/td>\n\u5931\u6557\u7387\u3001\u6240\u8981\u6642\u9593\u3001\u4fee\u6b63\u56de\u6570<\/td>\n\u640d\u5931\u306e\u69cb\u6210\u6210\u5206<\/td>\n<\/tr>\n
\\(\\operatorname{LossBias}_t\\)<\/td>\ngain\/loss framing \u3092\u5207\u308a\u66ff\u3048\u308b<\/td>\n\u9078\u629e\u78ba\u7387\u5dee\u3001\u53cd\u5fdc\u6642\u9593\u5dee\u3001\u518d\u9078\u629e\u7387<\/td>\n\u640d\u5931\u56de\u907f\u306e\u5f37\u3055\u3068\u65b9\u5411<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u3053\u306e\u6bb5\u968e\u3067\u91cd\u8981\u306a\u306e\u306f\u3001\u5fc3\u7406\u5909\u6570\u3092\u300c\u30a2\u30f3\u30b1\u30fc\u30c8\u4e0a\u306e\u4e3b\u89b3\u300d\u306b\u9589\u3058\u306a\u3044\u3053\u3068\u3067\u3042\u308b\u3002\u3082\u3061\u308d\u3093\u81ea\u5df1\u5831\u544a\u306f\u4f7f\u3048\u308b\u304c\u3001\u305d\u308c\u3060\u3051\u3067\u306f\u5b9f\u88c5\u306b\u8010\u3048\u306a\u3044\u3002\u5fc5\u8981\u306a\u306e\u306f\u3001\u884c\u52d5\u306e\u5909\u5316\u3068\u3057\u3066\u8868\u308c\u308b\u6307\u6a19\u3067\u3042\u308b\u3002\u305f\u3068\u3048\u3070\u3001\u540c\u3058\u671f\u5f85\u5024\u306a\u306e\u306b loss framing \u306e\u3068\u304d\u3060\u3051\u4fdd\u5b88\u7684\u9078\u629e\u304c\u5897\u3048\u308b\u306a\u3089\u3001\u305d\u308c\u306f \\(\\operatorname{LossBias}_t\\) \u306e\u4e0a\u6607\u3068\u3057\u3066\u6271\u3048\u308b\u3002\u3053\u308c\u306f\u54f2\u5b66\u7684\u306b\u8a00\u3048\u3070\u300c\u6050\u308c\u300d\u3084\u300c\u4fdd\u8eab\u300d\u3067\u3042\u308a\u3001\u5b9f\u9a13\u8a2d\u8a08\u7684\u306b\u8a00\u3048\u3070\u30d5\u30ec\u30fc\u30df\u30f3\u30b0\u52b9\u679c\u3067\u3042\u308b\u3002<\/p>\n

\n

8. \u904b\u7528\u30ed\u30b0\u30ec\u30d9\u30eb\u2015\u2015\u5b9f\u74b0\u5883\u3078\u63a5\u7d9a\u3059\u308b<\/h2>\n

\u7406\u8ad6\u3092\u673a\u4e0a\u306e\u7a7a\u8ad6\u3067\u7d42\u308f\u3089\u305b\u306a\u3044\u305f\u3081\u306b\u306f\u3001\u6700\u5f8c\u306b\u904b\u7528\u30ed\u30b0\u3078\u63a5\u7d9a\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u3053\u3053\u3067\u3044\u3046\u904b\u7528\u30ed\u30b0\u3068\u306f\u3001\u5358\u306a\u308b\u30b7\u30b9\u30c6\u30e0\u30ed\u30b0\u3060\u3051\u3067\u306f\u306a\u3044\u3002\u30e1\u30c3\u30bb\u30fc\u30b8\u5c65\u6b74\u3001\u30ec\u30d3\u30e5\u30fc\u5c65\u6b74\u3001\u627f\u8a8d\u30d5\u30ed\u30fc\u3001\u30c1\u30b1\u30c3\u30c8\u3001\u518d\u30aa\u30fc\u30d7\u30f3\u3001\u969c\u5bb3\u5831\u544a\u3001\u4fee\u6b63\u30b3\u30df\u30c3\u30c8\u3001\u518d\u8a66\u884c\u5c65\u6b74\u3082\u542b\u3080\u3002\u3053\u308c\u3089\u306f\u3059\u3079\u3066\u3001\\(O_t\\)\u3001\\(L_t\\)\u3001\\(\\operatorname{LossBias}_t\\) \u306e\u4ee3\u7406\u89b3\u6e2c\u306b\u306a\u308a\u3046\u308b\u3002\u7279\u306b\u30a4\u30d9\u30f3\u30c8\u30ed\u30b0\u304b\u3089\u5b9f\u969b\u306e\u696d\u52d9\u904e\u7a0b\u3092\u5fa9\u5143\u3059\u308b process mining \u306e\u767a\u60f3\u306f\u3001\u3053\u306e\u63a5\u7d9a\u3092\u904b\u7528\u30ec\u30d9\u30eb\u3067\u5177\u4f53\u5316\u3059\u308b[19]<\/a>\u3002<\/p>\n

\u305f\u3068\u3048\u3070 \\(\\Phi\\) \u306f\u3001\u30ed\u30b0\u7cfb\u5217\u3092 embedding \u5316\u3057\u3001\u30c8\u30d4\u30c3\u30af\u5206\u89e3\u3084\u5909\u5316\u70b9\u691c\u51fa\u3092\u884c\u3046\u3053\u3068\u3067\u8fd1\u4f3c\u3067\u304d\u308b\u3002\u7570\u5e38\u6642\u306b\u3060\u3051\u51fa\u73fe\u3059\u308b\u69cb\u9020\u3001\u5e73\u5e38\u6642\u306b\u306f\u96a0\u308c\u3066\u3044\u308b\u4f9d\u5b58\u95a2\u4fc2\u3001\u3042\u308b\u5909\u66f4\u306e\u524d\u5f8c\u3067\u8df3\u306d\u308b\u7279\u5fb4\u91cf\u306a\u3069\u304c\u898b\u3048\u308c\u3070\u3001\u305d\u308c\u306f\u69cb\u9020\u62bd\u51fa\u306e\u4e00\u90e8\u3067\u3042\u308b\u3002 \\(O_t\\) \u306f\u3001\u30c1\u30fc\u30e0\u5185\u306e\u30e1\u30c3\u30bb\u30fc\u30b8\u7d4c\u8def\u3084\u30ec\u30d3\u30e5\u30fc\u4f9d\u5b58\u95a2\u4fc2\u3092\u30b0\u30e9\u30d5\u5316\u3059\u308b\u3053\u3068\u3067\u8fd1\u4f3c\u3067\u304d\u308b\u3002\u8ab0\u304c\u30cf\u30d6\u304b\u3001\u3069\u3053\u3067\u6ede\u7559\u3059\u308b\u304b\u3001\u3069\u306e\u7d4c\u8def\u3067\u8aa4\u89e3\u304c\u56fa\u5b9a\u5316\u3059\u308b\u304b\u304c\u898b\u3048\u308b\u3002 \\(L_t\\) \u306f\u3001\u5931\u6557\u7387\u3001MTTR\u3001\u518d\u8a66\u884c\u7387\u3001\u30c1\u30b1\u30c3\u30c8\u518d\u30aa\u30fc\u30d7\u30f3\u7387\u3001\u5dee\u3057\u623b\u3057\u7387\u306a\u3069\u3067\u89b3\u6e2c\u3067\u304d\u308b\u3002 \\(\\operatorname{LossBias}_t\\) \u306f\u3001\u969c\u5bb3\u5f8c\u306b\u4fdd\u5b88\u7684\u5909\u66f4\u304c\u904e\u5270\u306b\u5897\u3048\u308b\u304b\u3001\u65b0\u898f\u6a5f\u80fd\u304c\u51cd\u7d50\u3055\u308c\u308b\u304b\u3001rollback \u304c\u5b9a\u5e38\u7684\u306b\u5897\u3048\u308b\u304b\u3001\u3068\u3044\u3063\u305f\u884c\u52d5\u504f\u308a\u3068\u3057\u3066\u8fd1\u4f3c\u3067\u304d\u308b\u3002<\/p>\n\n\n\n\n\n\n\n\n
\u5909\u6570<\/th>\n\u904b\u7528\u30ed\u30b0\u4e0a\u306e\u4ee3\u7406\u6307\u6a19<\/th>\n\u8aad\u3081\u308b\u3053\u3068<\/th>\n\u5178\u578b\u4f8b<\/th>\n<\/tr>\n<\/thead>\n
\\(\\Phi\\)<\/td>\nembedding\u3001\u30af\u30e9\u30b9\u30bf\u3001\u5909\u5316\u70b9<\/td>\n\u96a0\u308c\u305f\u69cb\u9020\u3084\u7570\u5e38\u30d1\u30bf\u30fc\u30f3<\/td>\n\u969c\u5bb3\u524d\u5f8c\u3067\u306e\u30ed\u30b0\u5206\u5e03\u306e\u9077\u79fb<\/td>\n<\/tr>\n
\\(O_t\\)<\/td>\n\u901a\u4fe1\u5c65\u6b74\u3001\u30ec\u30d3\u30e5\u30fc\u7d4c\u8def\u3001\u627f\u8a8d\u30d5\u30ed\u30fc<\/td>\n\u89b3\u6e2c\u306e\u504f\u5728\u3068\u60c5\u5831\u4f1d\u64ad<\/td>\n\u7279\u5b9a\u30ec\u30d3\u30e5\u30a2\u30fc\u3078\u306e\u4f9d\u5b58\u96c6\u4e2d<\/td>\n<\/tr>\n
\\(L_t\\)<\/td>\n\u5931\u6557\u7387\u3001MTTR\u3001\u518d\u30aa\u30fc\u30d7\u30f3\u7387<\/td>\n\u76ee\u6a19\u304b\u3089\u306e\u305a\u308c\u306e\u5927\u304d\u3055<\/td>\n\u5fa9\u65e7\u6642\u9593\u306e\u6162\u6027\u7684\u80a5\u5927\u5316<\/td>\n<\/tr>\n
\\(\\operatorname{LossBias}_t\\)<\/td>\n\u5909\u66f4\u5fcc\u907f\u3001\u904e\u5270 rollback\u3001\u63a2\u7d22\u505c\u6b62<\/td>\n\u640d\u5931\u56de\u907f\u306e\u5f37\u3055<\/td>\n\u969c\u5bb3\u5f8c\u306e\u904e\u5270\u4fdd\u5b88\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u3053\u306e\u30ec\u30d9\u30eb\u307e\u3067\u6765\u308b\u3068\u3001\u54f2\u5b66\u7684\u6982\u5ff5\u306f\u5358\u306a\u308b\u6bd4\u55a9\u3067\u306f\u306a\u304f\u306a\u308b\u3002\u305f\u3068\u3048\u3070\u300c\u5171\u540c\u4e3b\u89b3\u300d\u306f\u30ec\u30d3\u30e5\u30fc\u7d4c\u8def\u306e\u504f\u5728\u3068\u3057\u3066\u3001\u300c\u504f\u898b\u300d\u306f\u640d\u5931\u975e\u5bfe\u79f0\u306e\u6301\u7d9a\u3068\u3057\u3066\u3001\u300c\u7406\u89e3\u300d\u306f\u7570\u5e38\u3092\u77ed\u304f\u8aac\u660e\u3059\u308b\u8868\u73fe\u306e\u8cea\u3068\u3057\u3066\u8aad\u3081\u308b\u3002\u3053\u3053\u3067\u521d\u3081\u3066\u3001\u54f2\u5b66\u306f\u7d44\u7e54\u8a2d\u8a08\u3001\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u904b\u7528\u3001\u7814\u7a76\u8a08\u753b\u3001\u8a8d\u77e5\u5b9f\u9a13\u306e\u8a9e\u5f59\u3068\u3057\u3066\u518d\u5229\u7528\u53ef\u80fd\u306b\u306a\u308b\u3002\u3055\u3089\u306b\u3001\u8a08\u7b97\u7406\u8ad6\u30fb\u8868\u73fe\u30fb\u5b9f\u88c5\u306e 3 \u6c34\u6e96\u3092\u533a\u5225\u3059\u308b Marr \u306e\u6574\u7406\u306f\u3001\u62bd\u8c61\u6982\u5ff5\u3092\u5b9f\u88c5\u3078\u843d\u3068\u3059\u969b\u306e\u968e\u5c64\u5316\u306b\u3082\u5bfe\u5fdc\u3057\u3066\u3044\u308b[20]<\/a>\u3002<\/p>\n

\n

9. 3 \u5c64\u306e\u5bfe\u5fdc\u8868\u2015\u2015\u6570\u5f0f\u3001\u5b9f\u9a13\u3001\u30ed\u30b0\u3092\u9589\u3058\u308b<\/h2>\n

\u3053\u3053\u307e\u3067\u306e\u8b70\u8ad6\u306e\u6838\u5fc3\u306f\u3001\u540c\u3058\u6982\u5ff5\u3092 3 \u3064\u306e\u30ec\u30d9\u30eb\u3067\u5d29\u3055\u305a\u306b\u5bfe\u5fdc\u3065\u3051\u308b\u3053\u3068\u306b\u3042\u308b\u3002\u6570\u5f0f\u3060\u3051\u3067\u306f\u691c\u8a3c\u3067\u304d\u305a\u3001\u5b9f\u9a13\u3060\u3051\u3067\u306f\u30b9\u30b1\u30fc\u30eb\u305b\u305a\u3001\u30ed\u30b0\u3060\u3051\u3067\u306f\u69cb\u9020\u304c\u898b\u3048\u306a\u3044\u30023 \u5c64\u3092\u9589\u3058\u308b\u3053\u3068\u3067\u3001\u62bd\u8c61\u6982\u5ff5\u304c\u8a2d\u8a08\u8a00\u8a9e\u306b\u306a\u308b\u3002<\/p>\n\n\n\n\n\n\n\n\n
\u6982\u5ff5<\/th>\n\u6570\u5f0f\u30ec\u30d9\u30eb<\/th>\n\u5b9f\u9a13\u8a2d\u8a08\u30ec\u30d9\u30eb<\/th>\n\u904b\u7528\u30ed\u30b0\u30ec\u30d9\u30eb<\/th>\n<\/tr>\n<\/thead>\n
\u69cb\u9020\u62bd\u51fa<\/td>\n\\(Z_t = \\Phi(O_t)\\)<\/td>\n\u518d\u69cb\u6210\u7cbe\u5ea6\u3001\u4e88\u6e2c\u7cbe\u5ea6\u3001\u4ecb\u5165\u9811\u5065\u6027<\/td>\nembedding\u3001\u5909\u5316\u70b9\u3001\u7570\u5e38\u30af\u30e9\u30b9\u30bf<\/td>\n<\/tr>\n
\u5206\u6563\u89b3\u6e2c<\/td>\n\\(O_t = \\{O_t^{(i)}\\}\\), \\(G=(V,E)\\)<\/td>\n\u63a5\u7d9a\u69cb\u9020\u3092\u5909\u3048\u305f\u7fa4\u6bd4\u8f03<\/td>\n\u901a\u4fe1\u7d4c\u8def\u3001\u30ec\u30d3\u30e5\u30fc\u7d4c\u8def\u3001\u627f\u8a8d\u30d5\u30ed\u30fc<\/td>\n<\/tr>\n
\u640d\u5931<\/td>\n\\(L_t = \\mathbb{E}[\\ell(\\cdot)]\\)<\/td>\n\u5931\u6557\u7387\u3001\u6642\u9593\u3001\u4fee\u6b63\u56de\u6570<\/td>\nMTTR\u3001\u518d\u8a66\u884c\u7387\u3001\u518d\u30aa\u30fc\u30d7\u30f3\u7387<\/td>\n<\/tr>\n
\u30d0\u30a4\u30a2\u30b9<\/td>\n\\(\\operatorname{LossBias}_t\\)<\/td>\nframing \u52b9\u679c\u3001\u53cd\u5fdc\u6642\u9593\u5dee<\/td>\n\u5909\u66f4\u5fcc\u907f\u3001\u904e\u5270 rollback\u3001\u63a2\u7d22\u7e2e\u5c0f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u3053\u3053\u3067\u91cd\u8981\u306a\u306e\u306f\u3001\u540c\u4e00\u306e\u6982\u5ff5\u304c\u6570\u5f0f\u3001\u5b9f\u9a13\u3001\u904b\u7528\u306e\u5404\u30ec\u30d9\u30eb\u3067\u4e00\u8cab\u3057\u305f\u5f79\u5272\u3092\u6301\u3064\u3053\u3068\u3067\u3042\u308b\u3002\u3082\u3057\u3042\u308b\u6982\u5ff5\u304c\u6570\u5f0f\u3067\u306f\u5b9a\u7fa9\u3067\u304d\u3066\u3082\u3001\u5b9f\u9a13\u3067\u6e2c\u5b9a\u3067\u304d\u305a\u3001\u904b\u7528\u30ed\u30b0\u306b\u3082\u73fe\u308c\u306a\u3044\u306a\u3089\u3001\u305d\u308c\u306f\u5b9f\u88c5\u4e0d\u53ef\u80fd\u306a\u6982\u5ff5\u3067\u3042\u308b\u3002\u9006\u306b\u30013 \u5c64\u3059\u3079\u3066\u3067\u5bfe\u5fdc\u304c\u53d6\u308c\u308b\u306a\u3089\u3001\u305d\u306e\u6982\u5ff5\u306f\u8a2d\u8a08\u5bfe\u8c61\u3068\u3057\u3066\u9589\u3058\u3066\u3044\u308b\u3002\u3053\u306e\u610f\u5473\u3067\u3001\u672c\u7a3f\u306e\u30e2\u30c7\u30eb\u306f\u3001\u54f2\u5b66\u7684\u6982\u5ff5\u3092\u5b9f\u88c5\u53ef\u80fd\u306a\u5909\u6570\u7cfb\u3078\u5909\u63db\u3059\u308b\u6700\u5c0f\u9589\u5305\u3092\u4e0e\u3048\u3066\u3044\u308b\u3002<\/p>\n

\n

10. \u5b9f\u88c5\u9806\u5e8f\u2015\u2015\u3069\u3053\u304b\u3089\u59cb\u3081\u308b\u3079\u304d\u304b<\/h2>\n

\u3053\u306e\u30e2\u30c7\u30eb\u3092\u5b9f\u969b\u306b\u767a\u5c55\u3055\u305b\u308b\u306a\u3089\u3001\u5b9f\u88c5\u9806\u5e8f\u306f\u660e\u78ba\u3067\u3042\u308b\u3002\u7b2c 1 \u306b \\(\\Phi\\) \u3092\u5b9a\u7fa9\u3059\u308b\u3002\u4f55\u3092\u69cb\u9020\u3068\u3057\u3066\u629c\u304d\u51fa\u3059\u304b\u304c\u5b9a\u307e\u3089\u306a\u3051\u308c\u3070\u3001\u89b3\u6e2c\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3082\u640d\u5931\u3082\u610f\u5473\u3092\u6301\u305f\u306a\u3044\u304b\u3089\u3067\u3042\u308b\u3002\u7b2c 2 \u306b \\(O_t\\) \u306e\u5206\u6563\u69cb\u9020\u3092\u5b9a\u7fa9\u3059\u308b\u3002\u8a8d\u8b58\u306e\u504f\u308a\u304c\u500b\u4f53\u7531\u6765\u306a\u306e\u304b\u69cb\u9020\u7531\u6765\u306a\u306e\u304b\u306f\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u5165\u308c\u306a\u3044\u3068\u898b\u3048\u306a\u3044\u3002\u7b2c 3 \u306b \\(L_t\\) \u3068 \\(\\operatorname{LossBias}_t\\) \u3092\u5b9f\u9a13\u3084\u30ed\u30b0\u3078\u63a5\u7d9a\u3059\u308b\u3002\u3053\u3053\u3067\u521d\u3081\u3066\u3001\u7406\u8ad6\u306f\u8a55\u4fa1\u3068\u6539\u5584\u306e\u5bfe\u8c61\u306b\u306a\u308b\u3002<\/p>\n\n\n\n\n\n\n\n
\u9806\u5e8f<\/th>\n\u4f5c\u696d<\/th>\n\u5148\u306b\u884c\u3046\u7406\u7531<\/th>\n\u5f8c\u7d9a\u3078\u306e\u5f71\u97ff<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>\n\\(\\Phi\\) \u306e\u5b9a\u7fa9<\/td>\n\u4f55\u3092\u69cb\u9020\u3068\u547c\u3076\u304b\u3092\u56fa\u5b9a\u3057\u306a\u3044\u3068\u5168\u4f53\u304c\u5b9a\u7fa9\u3067\u304d\u306a\u3044<\/td>\n\u89b3\u6e2c\u3001\u640d\u5931\u3001\u30d0\u30a4\u30a2\u30b9\u306e\u5bfe\u8c61\u304c\u5b9a\u307e\u308b<\/td>\n<\/tr>\n
2<\/td>\n\\(O_t\\) \u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5316<\/td>\n\u8a8d\u8b58\u306e\u6b6a\u307f\u306e\u8d77\u6e90\u3092\u533a\u5225\u3059\u308b\u305f\u3081<\/td>\n\u96c6\u56e3\u7684\u932f\u8aa4\u3084\u5236\u5ea6\u7684\u77e5\u8b58\u3092\u6271\u3048\u308b<\/td>\n<\/tr>\n
3<\/td>\n\\(L_t\\) \u3068 \\(\\operatorname{LossBias}_t\\) \u306e\u6e2c\u5b9a\u5316<\/td>\n\u6539\u5584\u53ef\u80fd\u6027\u3092\u4f5c\u308b\u305f\u3081<\/td>\n\u5b9f\u9a13\u3068\u904b\u7528\u30ed\u30b0\u3078\u63a5\u7d9a\u3067\u304d\u308b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

11. \u9650\u754c\u2015\u2015\u307e\u3060\u4f55\u304c\u672a\u89e3\u6c7a\u306a\u306e\u304b<\/h2>\n

\u3053\u306e\u67a0\u7d44\u307f\u306f\u4e07\u80fd\u3067\u306f\u306a\u3044\u3002\u7b2c 1 \u306b\u3001\\(\\Phi\\) \u306e\u6b63\u3057\u3044\u5b9a\u7fa9\u306f\u6587\u8108\u4f9d\u5b58\u3067\u3042\u308a\u3001\u552f\u4e00\u306e\u5f62\u306b\u56fa\u5b9a\u3057\u306b\u304f\u3044\u3002\u5727\u7e2e\u6027\u80fd\u304c\u9ad8\u3044\u8868\u73fe\u304c\u3001\u305d\u306e\u307e\u307e\u56e0\u679c\u7684\u306b\u6709\u610f\u5473\u3068\u306f\u9650\u3089\u306a\u3044\u3002\u7b2c 2 \u306b\u3001\u904b\u7528\u30ed\u30b0\u306f\u89b3\u6e2c\u53ef\u80fd\u306a\u5916\u90e8\u6319\u52d5\u3057\u304b\u4e0e\u3048\u305a\u3001\u4e3b\u89b3\u7d4c\u9a13\u305d\u306e\u3082\u306e\u3092\u76f4\u63a5\u306f\u4e0e\u3048\u306a\u3044\u3002\u7b2c 3 \u306b\u3001\\(\\operatorname{LossBias}_t\\) \u306e\u3088\u3046\u306a\u9805\u306f\u3001\u591a\u304f\u306e\u5834\u5408\u3001\u8907\u6570\u306e\u5fc3\u7406\u6a5f\u69cb\u306e\u6df7\u5408\u3068\u3057\u3066\u3057\u304b\u89b3\u6e2c\u3067\u304d\u306a\u3044\u3002\u7b2c 4 \u306b\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u306f\u6642\u9593\u3068\u3068\u3082\u306b\u5909\u5316\u3059\u308b\u305f\u3081\u3001\u9759\u7684\u30b0\u30e9\u30d5\u3067\u306f\u4e0d\u5341\u5206\u306a\u5834\u5408\u304c\u591a\u3044\u3002<\/p>\n

\u305d\u308c\u3067\u3082\u3001\u3053\u306e\u67a0\u7d44\u307f\u306b\u306f\u6c7a\u5b9a\u7684\u306a\u5229\u70b9\u304c\u3042\u308b\u3002\u66d6\u6627\u306a\u54f2\u5b66\u8a9e\u3092\u3001\u305d\u306e\u307e\u307e\u5de5\u5b66\u3078\u8f38\u5165\u3057\u306a\u3044\u3067\u6e08\u3080\u3053\u3068\u3067\u3042\u308b\u3002\u4ee3\u308f\u308a\u306b\u3001\u54f2\u5b66\u8a9e\u3092\u3001\u72b6\u614b\u3001\u89b3\u6e2c\u3001\u69cb\u9020\u3001\u640d\u5931\u3001\u30d0\u30a4\u30a2\u30b9\u3068\u3044\u3046\u5c11\u6570\u5909\u6570\u3078\u5206\u89e3\u3057\u3001\u305d\u306e\u5f8c\u306b\u5b9f\u9a13\u3084\u30ed\u30b0\u3078\u63a5\u7d9a\u3059\u308b\u3002\u3053\u308c\u306b\u3088\u3063\u3066\u3001\u54f2\u5b66\u306f\u300c\u5206\u304b\u3063\u305f\u6c17\u306b\u306a\u308b\u8a9e\u5f59\u300d\u304b\u3089\u3001\u300c\u3069\u3053\u304c\u5931\u6557\u3057\u3066\u3044\u308b\u304b\u3092\u5207\u308a\u5206\u3051\u308b\u8a9e\u5f59\u300d\u3078\u5909\u308f\u308b\u3002<\/p>\n\n\n\n\n\n\n\n\n
\u672a\u89e3\u6c7a\u70b9<\/th>\n\u5185\u5bb9<\/th>\n\u554f\u984c\u306e\u672c\u8cea<\/th>\n\u4eca\u5f8c\u306e\u65b9\u5411\u6027<\/th>\n<\/tr>\n<\/thead>\n
\\(\\Phi\\) \u306e\u5b9a\u7fa9<\/td>\n\u5727\u7e2e\u30fb\u56e0\u679c\u62bd\u51fa\u306e\u5f62\u5f0f\u304c\u6587\u8108\u4f9d\u5b58<\/td>\n\u60c5\u5831\u5727\u7e2e\u3068\u56e0\u679c\u6027\u304c\u4e00\u81f4\u3057\u306a\u3044<\/td>\n\u56e0\u679c\u5236\u7d04\u4ed8\u304d\u8868\u73fe\u5b66\u7fd2\u3078\u306e\u7d71\u5408<\/td>\n<\/tr>\n
\u4e3b\u89b3\u7d4c\u9a13<\/td>\n\u30ed\u30b0\u306f\u5916\u90e8\u6319\u52d5\u306e\u307f\u3092\u8a18\u9332<\/td>\n\u30af\u30aa\u30ea\u30a2<\/a>\u304c\u76f4\u63a5\u89b3\u6e2c\u3067\u304d\u306a\u3044<\/td>\n\u81ea\u5df1\u5831\u544a\u30fb\u751f\u7406\u6307\u6a19\u3068\u306e\u7d71\u5408<\/td>\n<\/tr>\n
\\(\\operatorname{LossBias}_t\\)<\/td>\n\u8907\u6570\u306e\u8a8d\u77e5\u30d0\u30a4\u30a2\u30b9<\/a>\u304c\u6df7\u5408\u3057\u3066\u89b3\u6e2c\u3055\u308c\u308b<\/td>\n\u5206\u96e2\u4e0d\u53ef\u80fd\u306a\u6f5c\u5728\u5909\u6570\u554f\u984c<\/td>\n\u6f5c\u5728\u5909\u6570\u30e2\u30c7\u30eb\u30fb\u56e0\u679c\u5206\u89e3\u306e\u5c0e\u5165<\/td>\n<\/tr>\n
\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020<\/td>\n\u6642\u9593\u5909\u5316\u3059\u308b\u52d5\u7684\u69cb\u9020<\/td>\n\u9759\u7684\u30b0\u30e9\u30d5\u3067\u306f\u8868\u73fe\u4e0d\u8db3<\/td>\n\u6642\u7cfb\u5217\u30b0\u30e9\u30d5\u30fb\u52d5\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30c7\u30eb<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

12. \u7d50\u8ad6\u2015\u2015\u54f2\u5b66\u306f\u5b9f\u88c5\u53ef\u80fd\u306a\u8a2d\u8a08\u8a00\u8a9e\u3067\u3042\u308b<\/h2>\n

\u672c\u7a3f\u306e\u7d50\u8ad6\u306f\u5358\u7d14\u3067\u3042\u308b\u3002\u54f2\u5b66\u3092\u5b9f\u88c5\u3059\u308b\u3068\u306f\u3001\u54f2\u5b66\u3092\u6368\u3066\u308b\u3053\u3068\u3067\u306f\u306a\u3044\u3002\u54f2\u5b66\u7684\u6982\u5ff5\u3092\u3001\u69cb\u9020\u66f4\u65b0\u3092\u6271\u3046\u5909\u6570\u3068\u6f14\u7b97\u3078\u843d\u3068\u3057\u76f4\u3059\u3053\u3068\u3067\u3042\u308b\u3002\u7406\u89e3\u306f \\(\\Phi\\)\u3001\u8a8d\u8b58\u306f\u5206\u6563\u89b3\u6e2c \\(O_t\\)\u3001\u5931\u6557\u306f \\(L_t\\)\u3001\u504f\u898b\u306f \\(\\operatorname{LossBias}_t\\) \u3068\u3057\u3066\u518d\u5b9a\u7fa9\u3067\u304d\u308b\u3002\u3053\u306e\u518d\u5b9a\u7fa9\u306b\u3088\u3063\u3066\u3001\u54f2\u5b66\u306f\u5358\u306a\u308b\u89e3\u91c8\u53f2\u3084\u7acb\u5834\u6bd4\u8f03\u3067\u306f\u306a\u304f\u3001\u89b3\u6e2c\u3001\u66f4\u65b0\u3001\u4ecb\u5165\u3001\u8a55\u4fa1\u3092\u8cab\u304f\u4e00\u822c\u7406\u8ad6\u306e\u8a9e\u5f59\u3068\u3057\u3066\u518d\u5229\u7528\u3067\u304d\u308b\u3002<\/p>\n

\u6570\u5f0f\u30ec\u30d9\u30eb\u3067\u610f\u5473\u3092\u56fa\u5b9a\u3057\u3001\u5b9f\u9a13\u8a2d\u8a08\u30ec\u30d9\u30eb\u3067\u6e2c\u5b9a\u53ef\u80fd\u6027\u3092\u4f5c\u308a\u3001\u904b\u7528\u30ed\u30b0\u30ec\u30d9\u30eb\u3067\u73fe\u5834\u3078\u63a5\u7d9a\u3059\u308b\u3002\u3053\u306e 3 \u5c64\u3092\u9589\u3058\u308b\u306a\u3089\u3001\u54f2\u5b66\u306f\u300c\u5b9f\u88c5\u3067\u304d\u306a\u3044\u62bd\u8c61\u300d\u3067\u306f\u306a\u304f\u306a\u308b\u3002\u3080\u3057\u308d\u9006\u3067\u3042\u308b\u3002\u8907\u96d1\u306a\u73fe\u8c61\u304b\u3089\u4f55\u3092\u672c\u8cea\u3068\u307f\u306a\u3057\u3001\u3069\u306e\u640d\u5931\u3092\u4e0b\u3052\u3001\u3069\u306e\u6b6a\u307f\u3092\u88dc\u6b63\u3059\u308b\u304b\u3092\u6c7a\u3081\u308b\u8a00\u8a9e\u3068\u3057\u3066\u3001\u54f2\u5b66\u306f\u5b9f\u88c5\u306e\u524d\u6bb5\u306b\u7acb\u3064\u3002\u305d\u3053\u307e\u3067\u9032\u3081\u3070\u3001\u54f2\u5b66\u306f\u5358\u306a\u308b\u601d\u60f3\u53f2\u3067\u306f\u306a\u304f\u3001\u69cb\u9020\u66f4\u65b0\u3092\u6271\u3046\u8a2d\u8a08\u8a00\u8a9e\u3068\u3057\u3066\u4f4d\u7f6e\u3065\u3051\u3089\u308c\u308b\u3002<\/p>\n

\u3057\u305f\u304c\u3063\u3066\u3001\u54f2\u5b66\u3068\u306f\u89e3\u91c8\u3067\u306f\u306a\u304f\u8a2d\u8a08\u3067\u3042\u308b\u3002\u4f55\u3092\u89b3\u6e2c\u3057\u3001\u4f55\u3092\u69cb\u9020\u3068\u307f\u306a\u3057\u3001\u3069\u306e\u640d\u5931\u3092\u4e0b\u3052\u3001\u3069\u306e\u6b6a\u307f\u3092\u88dc\u6b63\u3059\u308b\u304b\u3092\u6c7a\u3081\u308b\u8a00\u8a9e\u3067\u3042\u308b\u3002<\/p>\n\n\n\n\n\n\n\n\n\n
\u9818\u57df<\/th>\n\u9069\u7528\u53ef\u5426<\/th>\n\u7406\u7531<\/th>\n<\/tr>\n<\/thead>\n
\u610f\u601d\u6c7a\u5b9a\u30fb\u884c\u52d5<\/a><\/td>\n\u53ef\u80fd<\/td>\n\u640d\u5931 \\(L_t\\) \u3068\u89b3\u6e2c \\(O_t\\) \u306b\u3088\u3063\u3066\u8a55\u4fa1\u3067\u304d\u308b<\/td>\n<\/tr>\n
\u8a8d\u77e5\u30fb\u7406\u89e3<\/td>\n\u53ef\u80fd<\/td>\n\u69cb\u9020\u62bd\u51fa \\(\\Phi\\) \u3068\u30d0\u30a4\u30a2\u30b9\u3067\u8a18\u8ff0\u53ef\u80fd<\/td>\n<\/tr>\n
\u793e\u4f1a\u30b7\u30b9\u30c6\u30e0<\/td>\n\u6761\u4ef6\u4ed8\u304d\u3067\u53ef\u80fd<\/td>\n\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u3068\u89b3\u6e2c\u306e\u5236\u7d04\u306b\u4f9d\u5b58<\/td>\n<\/tr>\n
\u4e3b\u89b3\u7d4c\u9a13\uff08\u30af\u30aa\u30ea\u30a2<\/a>\uff09<\/td>\n\u4e0d\u5b8c\u5168<\/td>\n\u76f4\u63a5\u89b3\u6e2c\u3067\u304d\u305a\u3001\u5916\u90e8\u6307\u6a19\u306b\u9084\u5143\u3055\u308c\u308b<\/td>\n<\/tr>\n
\u4fa1\u5024\u305d\u306e\u3082\u306e\u306e\u8d77\u6e90<\/td>\n\u56f0\u96e3<\/td>\n\u640d\u5931\u95a2\u6570\u306e\u5916\u90e8\u306b\u4f4d\u7f6e\u3059\u308b\u305f\u3081\u5b9a\u7fa9\u56f0\u96e3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u53c2\u8003\u6587\u732e<\/h2>\n
    \n
  1. id774. \u69cb\u9020\u632f\u52d5\u30e2\u30c7\u30eb\u3092\u6570\u7406\u30e2\u30c7\u30eb\u3068\u3057\u3066\u5b9a\u7fa9\u3059\u308b\uff082026-04-05\uff09. https:\/\/blog.id774.net\/entry\/2026\/04\/05\/4318\/<\/a><\/li>\n
  2. id774. \u54f2\u5b66\u306f\u306a\u305c\u69cb\u9020\u306e\u63fa\u308c\u3092\u6570\u7406\u30e2\u30c7\u30eb\u3067\u8aac\u660e\u3067\u304d\u308b\u306e\u304b\uff082026-04-06\uff09. https:\/\/blog.id774.net\/entry\/2026\/04\/06\/4328\/<\/a><\/li>\n
  3. Shannon, Claude E. \u201cA Mathematical Theory of Communication.\u201d https:\/\/people.math.harvard.edu\/~ctm\/home\/text\/others\/shannon\/entropy\/entropy.pdf<\/a><\/li>\n
  4. Cover, Thomas M., and Joy A. Thomas. Elements of Information Theory<\/i>. Wiley. https:\/\/onlinelibrary.wiley.com\/doi\/book\/10.1002\/047174882X<\/a><\/li>\n
  5. Pearl, Judea. Causality: Models, Reasoning, and Inference<\/i>. Cambridge University Press. https:\/\/www.cambridge.org\/core\/books\/causality\/B0046844FAE10CBF274D4ACBDAEB5F5B<\/a><\/li>\n
  6. Granger, C. W. J. \u201cInvestigating Causal Relations by Econometric Models and Cross-spectral Methods.\u201d https:\/\/ideas.repec.org\/a\/ecm\/emetrp\/v37y1969i3p424-38.html<\/a><\/li>\n
  7. Watts, Duncan J., and Steven H. Strogatz. \u201cCollective Dynamics of \u2018Small-world\u2019 Networks.\u201d https:\/\/www.nature.com\/articles\/30918<\/a><\/li>\n
  8. Barab\u00e1si, Albert-L\u00e1szl\u00f3, and R\u00e9ka Albert. \u201cEmergence of Scaling in Random Networks.\u201d https:\/\/arxiv.org\/abs\/cond-mat\/9910332<\/a><\/li>\n
  9. Rissanen, Jorma. \u201cModeling by Shortest Data Description.\u201d https:\/\/www.sciencedirect.com\/science\/article\/pii\/0005109878900055<\/a><\/li>\n
  10. Rubin, Donald B. \u201cEstimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.\u201d https:\/\/www.ovid.com\/journals\/jedup\/pdf\/00004760-197410000-00007~estimating-causal-effects-of-treatments-in-randomized-and<\/a><\/li>\n
  11. Kingma, Diederik P., and Max Welling. \u201cAuto-Encoding Variational Bayes.\u201d https:\/\/arxiv.org\/abs\/1312.6114<\/a><\/li>\n
  12. Kahneman, Daniel, and Amos Tversky. \u201cProspect Theory: An Analysis of Decision under Risk.\u201d https:\/\/web.mit.edu\/curhan\/www\/docs\/Articles\/15341_Readings\/Behavioral_Decision_Theory\/Kahneman_Tversky_1979_Prospect_theory.pdf<\/a><\/li>\n
  13. Simon, Herbert A. \u201cA Behavioral Model of Rational Choice.\u201d https:\/\/cooperative-individualism.org\/simon-herbert_a-behavioral-model-of-rational-choice-1955-feb.pdf<\/a><\/li>\n
  14. Friston, Karl. \u201cThe Free-energy Principle: A Unified Brain Theory?\u201d https:\/\/www.nature.com\/articles\/nrn2787<\/a><\/li>\n
  15. Tishby, Naftali, Fernando C. Pereira, and William Bialek. \u201cThe Information Bottleneck Method.\u201d https:\/\/arxiv.org\/abs\/physics\/0004057<\/a><\/li>\n
  16. Newman, M. E. J. Networks: An Introduction<\/i>. Oxford University Press. https:\/\/global.oup.com\/academic\/product\/networks-9780199206650<\/a><\/li>\n
  17. Hutchins, Edwin. Cognition in the Wild<\/i>. MIT Press. https:\/\/mitpress.mit.edu\/9780262581462\/cognition-in-the-wild\/<\/a><\/li>\n
  18. Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction<\/i>. MIT Press. https:\/\/mitpress.mit.edu\/9780262039246\/reinforcement-learning\/<\/a><\/li>\n
  19. van der Aalst, Wil M. P. Process Mining: Data Science in Action<\/i>. Springer. https:\/\/link.springer.com\/book\/10.1007\/978-3-662-49851-4<\/a><\/li>\n
  20. Marr, David. Vision<\/i>. W. H. Freeman. https:\/\/mitpress.mit.edu\/9780262514620\/vision\/<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    \u672c\u7a3f\u306f\u3001\u69cb\u9020\u632f\u52d5\u30e2\u30c7\u30eb\u306e\u6570\u7406\u7684\u5b9a\u7fa9\u3068\u3001\u305d\u306e\u54f2\u5b66\u7684\u80cc\u666f\u3092\u6574\u7406\u3057\u305f\u5148\u884c\u8a18\u4e8b\u3092\u524d\u63d0\u3068\u3057\u3066\u304a\u308a\u3001\u305d\u308c\u3089\u3092\u6570\u5f0f\u3001\u5b9f\u9a13\u3001\u904b\u7528\u306e 3 \u5c64\u3078\u63a5\u7d9a\u3059\u308b\u3053\u3068\u3067\u3001\u62bd\u8c61\u54f2\u5b66\u304b\u3089\u904b\u7528\u53ef\u80fd\u306a\u30e2\u30c7\u30eb\u3078\u306e\u79fb\u884c\u3092\u660e\u793a\u3059\u308b\u3082\u306e\u3067\u3042\u308b[1][2]\u3002 \u54f2\u5b66\u306f\u9577 … \u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26,23,24],"tags":[],"class_list":["post-4335","post","type-post","status-publish","format-standard","hentry","category-math","category-philosophy","category-science"],"_links":{"self":[{"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/posts\/4335","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/comments?post=4335"}],"version-history":[{"count":16,"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/posts\/4335\/revisions"}],"predecessor-version":[{"id":4357,"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/posts\/4335\/revisions\/4357"}],"wp:attachment":[{"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/media?parent=4335"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/categories?post=4335"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.id774.net\/entry\/wp-json\/wp\/v2\/tags?post=4335"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}