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<\/script><script 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fFIQvCuGFNZxlDWdZ+ayfrDLzWfCiIHwBshYYG\/gseFEQvgBZc6pW8lnwoiB8UQh+cUZzVY0R\/s6QipUrK6pqcqaX5uXmGNfUVKxqXpkzvTjvOfBbRSqN4Hd\/LBP8Pg74a0GKppaGf1\/HL3Ny857KXRb6NR35JVOLc+8vmlogyM4H2dG\/viO\/YOq03PvzpxYJihWAYvyv9QDVFOROE2QWgsxHKiwVILNoakl+aS747ShFJYbcnKralb\/MgW1U2CosfO3FhoLc+0v5X0gSyZ+zqsJcUV2\/yly\/ChYqLCkAnTAUlAgKza6tsBlBrwunGvKn5RZMzc83RGXzHTRMLc29v3BqYegXqcC8BRU1FU0VObn506YW5eZNNeTmNBsrcp566rk7kXr230iX9zpzado3WD9+O94v+Sf2Hops1sjvZQhMoSAQdTBuQEUp1BlsJqHwkMMSRoVLZNIgLp1JKJ2ZNyYrlQF3CYNK7ilFVjEr6JsB0klSNwJOB3IEOSyj5RLKj7mVw5O8UhJzJ3kIlIxPTJBwNIW6KL+a0mKYFkMxv8JDsBjBKqVKvQzXyGQ5iToCl0l0lBZPlqHoj1niX0X9JK0ZRIdUTvdggNXp41AVjmQySyk8i5Yh3zchg9xOyqkYpHuT+hA\/4WdJ0scN6j2ETMZqBuS39JfJgCzASYboPvRWsFd9Ru6mAiilZfGP0PeQfwx+R3VjN72X0UFVkMDkSYxSgqqcHh9H48x9qEKFztdj8iK9tvNxJkF5twyTsYSbuoIG1Q7pgNqBOQLXXQFkh5LRDROMVkc7FNwEh9yr9yuGuGCQVfn0d905ITfjAY0Ez9ZjMjKJS5Cn4XcTEhbHFUwcmh5I6Z7kxSmCIyRSWo7GOfRXtJSKxBAFSXhlPsSZSKuHNQFNBfIb7u\/Sj+\/\/QBuYOKy8zvroASwQGLpJ3g76uwd7nF+73a7zQXe3m7oYdAXcw+zQdcfVYL\/bzSGvhH7+zP+bQS\/DM6SSjFSlhktOUCESTJmUptAE1TJUnUDogxPoCbdUiIbQqDWkXFKKpmXrFSrpPURChupu5F65WqLpfxRR92bdTPJpObVChSWkPEBlXP6J\/37u3okT5GpCS2Theez9VKY326dBcpRSrU6dpJ6o1OBZv4ajoDt9bs91T79r2EchV5Vu1U1pF\/YVfZ7sJF0Bz\/Ub6AXFV1M+knbf+oId9HtRj9KV3Z\/WiXiSPmTcAQ9ygRkO9A52XyS9lNPfe\/MkF2DcBBnUDcuHE4bTutFgwBnouo372HuuyxCJhpDIWL0PHfY5sNt3foF7JyF0vESiGpLTREDRNaWX4FRY2u2pMiQfS8majJdLymUoJg3mqjF9gkSlIO7TxKmSiSkYjlsQC6d7QvuEVPHY\/EfnP9z4Xw2INfzThX+LelVfTgwGh6WX7\/zq9h7\/Xv8e\/yub3z551dE7+C+9FjmPnSWvXvNiw6QvQPmRT6SfMx9S3Xmy7Hjk18ivEpHz2M+oN6XIPmQv+PuNz49KqVKlNEPyKDUd+ZFUoZTrCBWbMwn5NYFcRLmgL6iktQpM6mQqIQfkbp0b9+gHlIgqSJCIn2Cl3skuBSn3cQ48wFAomXFb3y+\/xfVIBhiG89Mc\/W2gFx0k+tngTSdDf++\/n5sgXz4lRzqJzWby3Dqf1iFjpFqljHZN1OfKEjwpPsLnZSTk1B6MCgZvUQruPiVShVSCv4sopBFpQhr58wbNRs0G9UbNBk1nxtvDyFrkBWStxKnjyICT9Z+TfcF2dg8gVN5ZlOrvGQz20r7gsFM29BFCs8OUHJUHpxApzsQA5g4G1WSCU4kShAJTkKlIWn8CrvcH43V6pSwNT5AlpXPS8wWINCMRywo+oMjXY4R8GGX0yK+ATWfrsrob3Q3uRk+Du9GNNCPNSBM4nyo7VdaFedXDE4fkFEHGs1mDCa7p7qRbyKD0totC\/VpWq1uwDd2G0PRl9jbp8H7j9pOM1znoYWVfs1SQY1rp1s4r7C3sG5akON2XlENKo7dZ\/63OG\/9NdfX4GUp5hXY7Bz1+J+lFGQa9eZny3Uq+wPp6uvpUfs7T6yf6uEFn0Ou96BoaRpyEi+31+V3dkqsk4qCHBweJfq10YpByeFGJS+YP0IMYzXI9fjqI0RQRIHrkrNLDOQeHqSDjlWEKWn1bSqdTOlTBEG7ShZFxSo2OwHQ9SkrqTUPuG1J68CHWP0yQDOP2O3wU4o37KjjQ149RWQNIYGiA88cPJKqSkUzmXlofN0FOSAl9tlyi4aQBXJXpxyWEW\/+1rF\/u45CfKGScPIAj65H1DyIB9kqQS1BNYbOlGQl3Ij\/nfhZAvISDpijET\/toxJHUjQZuI9d9Q9Qtn++q0+2TURocobwBhNYgnE8WVCEyHHUrpLJ4STxBSbzBIaVfLZFIkSRcRWC0F+3z3CYdEo8Ow2h6iHDLSAWqlNMD+m4CWc2mkVyAoBRxAYwkpcgadirJIgipwby4DFnL\/pikEYyUSSQSjgiSMuQNdh7JcBipkKMEiTBBAtnPTiVpShpUE5zUg0qRk+ydpIdCSRWF6HfAuY3zOh3YcIJcr4n3JnC4O4l4uAP9IMjRt6UuZpgODpNXB4JB2u0MUj7Mx5JqTOtW0Wo34lFKshiSxIJytzyoYDm3uid7KBkL+FN9uJdlhik8oEQ1yhy9UilNUcYjE+XJGVxcP47gSkQhlcbhKlKO0D9xYZxUKg9ogjk0walkaVwKnsrED3iHElFFfCIiv5GMKxJ1ssShIhRXShVxQPF0JBoXiD+f3ItQKp90OJ1k3cMOH0sQWlLjlwxzPVIK9yTTmAcZdH6B+lSY5hbr1fqcg1wwLkDcwhkWoRUB7BbnZJl+NqjmFH683+PFnH7kqiLI+EgW+UiJJgVygxonQypob5ecRinGPQ15ln0K8cmv+Lom+PTHSZqiZEzQSzMSQudXd5HdmN\/jlToSPTIS8ftJ9oLEHUDWgZ9JGnW8EiGUM\/x6SkZjZIocY1K45KBUchctxeSUSiKhNEEtkiFHlyJL2BxSJtfKCUQul1Ipk5FmbiCI1Grl8olsnCeOSvTEcTLpZFITxIKs71vJYNb7Tlegw8lyR\/zeTFKjYwiXlNGhKUpM8TOJizrh7Uf9Kr+GWYhkso8yEpVUlqBD9T51ACPTSTkulbEKHE3HsQRWjgcVjFbFSeOuyzwUhTNxCQnSTHc8qg7oB1UezJFGKVBlUI3IFQSu0N7FTHOpSVVPHCKVyoYQVnIrwBJB3Cn9TEH6fIhn6Job2YxsRjbBc4peoWKT1Aoa9cs8SRgmQfVsil+O4BIshZaTMkcCqtSrXCkY61Z6H+AS9LOQ89hvZS8j4Ocl5OXID39Nn3J863UHbzEM4ta6NLRv2M8GepDPEjDC+8Bgqju1R+ae9O1d15VMkl6upGfiWew9UrXcndGbNTzVly6JI5R0EnLvQAqj98tciqCewUmiHxl86KuHnWkJOXGzS1faqxVVimrF\/clTvtNRMtnU6yrv3VwmjZgQM2LSIZnMb2XITmSnPF2BKXVu3KVFNBKJLEWlibsbL7mATEcymWSpBk3S5MgmxmHxGp1Wfo9MI9Vk5Mk02lxNuj4jQaYgkpPv+C9C55Nff8yFJJBJNOGU+GVBrVNxS3IhsR\/x4Q6yh\/lS2anrTgtoKA92ke7Ejwd8\/U6f5NLEa3cHNX3pfZovEkjVIIv8GfzYYQzMXJRw8W8HKCQQIAMuIohdRQ5wb3QHXMigmmY8Uoq9hFNMt5wKIlS6g\/BqPZITQ+f9JBKQeThf8lU1m+CVOzmP2pVDZDAKbZpSi+lJHaKNV90jUyXoU1EFqU5ToKnehHiZElPevleZMkXKZUskKk5PqBX6BFzjmyyRpiukmRN+nJKFKbhkCpfGDU+9SPYme1mHrDvBn0nKA7g3nmHwoWsIrfruATZZGYeoHFovGnR+Oanf130D89A9UhpxD3sph4NEvP5AIZLJyNCfcT9lFUzyUBJ9H3vHqclopkohR1RxhDROp0I0FI6ns+qktCSNXppMyzPlek7WmzGoC+iGdQNeL3Ij5QbulA2SnMcTdMt6\/b5+hvUitwfdaJ+P6e6mXqe7cWTzZOQ8lo\/\/e+O\/Nnx45IMjlP\/2S7f\/hPkULimXrE2gUhBNv\/bzhEGskxiSkvEB3XWVJ5MtaStpQ5rEP\/9fieT\/\/yjn\/+4o5\/\/t7P\/\/7iqrFHmcbaVIKUtIEwipjFFKpEoGT2JRiRKTyglUQiCERkUoAdMoApeycgxBicmJCqUMRQi5Ws0hdJZOjmC0TCb3KTGEVCI4JcWlNBIkAvRtCaVACBxjUTlBylEJKaO1aDoylWmTKhPjWLlKolLJVaiKxkm9XIHoXBKvjlJ6NUGfm+qTBTQ9rttcMOMWFiCHMN+dSAHyOKVkMbcCJUg9E+dBSAKlhiSUlAt6ZTQeULETv2EC6AUpSQbxYBBjaaZTQpNBJECT+C12WBpgXGTwNkUFLwcykZe4u2jOhSMSUk8TTkKCMRpO7pXTuEPhlQzoezmv3E0EVbTcjfikJMHhPgmh8Gtp1jMRucSopeiQ44bbwXgDnuuD\/l7MzwSRYaePC1IIQwYZ2hUIcAHPEONxe4PXyWCwC\/Gob6m+J7qQa8E+xw0F8gfmfU5KIBpCgkkRWUCDxdPyJcjz7EKWVaqURKI+kVRI5DJCEp+kVqrUOCfRSmVyFYpJ1YREysgyZBJGqvYTLMdJcAwjnCir8BNBLS7jcBolKaeSZHE\/xdJEgKWkQYqjKR2D9vqD2DDFyAMozXhZEuv3MSjJOmWUIhBkUErmp6\/IfMEgEWS8Ch9KUjTh9gVYWj1IUBylHFZ3K4f1aJxCwSoVCOqVM4lSVKdE1jIYwkg7EwMKr57We7WD8oDSj8uRy+wjBOtR9eIsx+jdWKeCvR9pZ34jU3pwTuFKGpL5kgMJTpUXd2q9WMDTLe+VUbpu\/KrCj3vkNB7UU4Qk263GCVaB4RSBoAFVUOphgTyjagZl9CxKSmmGlSFSpw4hGAJZy0gQFU34FaQGlzFzkb3cVu4aNhz0yTzYLS9FIDIc57KCSaSEUHCygPIWwbAchktIzI+yDIUHKDc6LHepSZQjgoQPY6kgTrO+FJ+Mo4OYU9PLkigjpWivhAmigSCDeBhSjSD+YQ\/H4ijqp8hhhJEibBBnGRajAn65DyM5hPHJfSxKMgxOYwHvIMUGcQ\/nU3s5SilHHMwOxkUOSQK0E0eYAOPBNMjfmI0MKyHZAO6ge2WUNMB0yfz0oCSIobgayWTmybREji8BuytFyspwOauToAqNApVKkEzkAqvqVQcJhGUKsTSfWqIh4wi58ufIK0gOJ5WgMi0qxTm9lFCWSKVsIoFrpqCTpDpOGpRjyQENIsMJTk\/hJEcRA4Rf1Sn3qQbUzjhG5cd9rDc1gHX5vBJaf1vm1HuJ4ThK6yYG8IH4wSkupV\/3rcSv8CAsEWCChFxKYpxUgvswWulVcrKeVA9Bxg\/ohnJ7klh9QD2o62FohoD9cuMBCiE9FOLGEAui4BSoHv7L+N0U5XNwrIdmEAXNepysNoiQFEpSnIRWI4XIP1gdpdRopbQ2LQPLkClkKCJXILhChaQg\/2IZf5AddDFuHxIMOtAhH0sN+B5AGtgPaDnKaVWaJFKCSVIVSq1Cn6rXKFC1Xi7VoIlyXMeqtApCpVJgMgWRNJnAiRw2QZeepCbjsqag6Vpl3DRKLsdxmQzFFFIikSBkSkShV0vkQS0ngS2gCIekQi+4MHcgiAwQlMojkab6tT6CTSC1PgWrJnE0m5ZhalLRq\/SruhJd2A3\/JOQtppCW4ag0Q67xaTmlREfiZBwrpQiphONkuELBIjqlgk0IYgoCJWRylpBIWFyKcFopwRFatRbDFZWyx2c+2dr34ivfvVaW+suVd1bKCnQLDt3cuuHW4YXZ1saUCtlXkxcf6l63rvPgwozm5qxFsjdKlrX2\/umVK6+Xp61ZOflRWQk5u+3Gns3Ot8smVNv1j8o0hctae1965err5WnPrMz5iexdbm7bjZbNQ8fKJpjq40tk2qTyQ52bN\/S1LsyyN6UVyaomLDrUtX5915sLM5uaMwtks6TzWm\/u3DR4tDy7tiEhV\/bs1KWHel5+5fob5emrV05Kl7HK+a03t2\/sP1KebWlI0si89yw51P3ndTcOLExftXICIkMyFh7q2ri+59DCzIamdB9xUl92qHPLhlttC7NsjSl9xDN3wvHcPLgwY2Vzdg\/xHjUH9v+tsglGu\/4aMQOd23qjZRPor7k+\/grxp6F6zbQ3Trdsdp9cWmy6RHgefLJtxF7fEqlOu2baAZi\/pNj0JXFg2K6ddqC9ZbP7xGKD6Qti\/rQnW\/v+9Mr3r5Wnrll5xxniEjGv9eaOTQNwfIntRJHbrp12sL1lswuUP0EcTC4\/1LlpfW\/rwqz6prTjxEcem3bam+27t7hOLDIYPyH+WRSx75qVOf8ipJOA\/dZ1vbkwo6k580Pipz6bbtqbHbu3DJ9YWGQ8RmxUL2i9uW3j7cPAXsltxJ15S1t7Xn7lGm\/PjcSe+5Yc6gH2K09f1TzxFWJf5sJDXRvWdx9amNnYlPES8XQ8tF8ftF\/qS8Qp\/+zDN\/ZtGX57wYQqm+5FopSZ03Zj92YHtF\/cr4lPsLmtN3ZtGjxWnm2uT2gmHCebseJdn+5cx542P1hjIzLvWnyo+5Ww\/U2EST6\/9eaOjQNHyrPrGhIXEg+nlh\/qguPPrG9K\/znBTYf4\/B7a+47HiBztgtZOOJ4sa2Pyo8SBnEWHutev63wT4vNBoqpYiM9c4rHg7LYbe7cAfFbZ9PcS5\/KXtva+vO7aGxCfU4gP2Uj\/Tfa4dMKXUAbx2bYwy96YmkI8mM3b401ojxTiF3gEnzX1CXHEw\/dD+10\/UJ6+unkiK\/UoIvisa0hyST++e8mhnlcgPlc2T+iSXksT4vOy9B+6BYc6t268BceTckZ6HPIJ9rfapu+Q\/uwOIf+OS3+MjPDJHv+JtHWmEI\/\/lGpLlwG8XXkd4G3y29JveD5BvNUntkrX8\/wD+LI3ph2Urilc2tr70roQX\/8ivTJh0aHuDeshnpoyt0n\/qBLybbO0fyrAzzrIx+ZJG6V3O+ya0jfC+P+z9B9Ou6aU58Nig2mt9GGXXVt6sL1liwukfyM947aB9O4trpOLDMY10oVem7b0zXB6ldR0rwCPExqlV3ylhzp2bwV4q7TpGqTPCvmdYZc+Qs+B9uLxZ5P+PC7E93LA9wrpZ+jctpu7gL2yzfXx86UDUfz\/hXSFbISP9Yk\/kmaeaZIUtxzfuZ45bZxZM116VwrA4waIx8a0Auk\/w3wuA3zOk9a3N2PFLZ\/uXM+eNj1Yc680URPBp6Uh+V7pc5MAPsP2vFM6ybBMYO8c6SenhM8nShcHhPxKkCacbEYNuz7duZ5rNz9YEyfdnBfB7+rmSRrp8Sj+SaRVn4Pyn+xcB8tz+PUwf4E9Uhn8Ccnc1pu7NgP8musTaDwtS8h3P67OjeB5VfNEF\/7O8WbEsPOTneuQ9pof1XTig5Cvm3i+Jl3EN961GOD75kGA7+yT+MeQvxt6oL3SP8LfEPC1Ifnv+FdhPoLx6d\/CsydD+0D+NmW14o9zc9pu7t7sOFaWbbLHt+JVM55s6\/vTuhD\/X8Xbi6H9QvjeiX8dxceN+OrEskNdkL+Z9sbUDfj0ghG+N+esxd\/LXgjw3f3mwozGpsxf4b9Szm\/t3L6p\/0h5Vl1D0hr87fuXtI7weRleeM9inr\/lgL8L8XnU7DDeqm36cvzJdIDHDSE+z8dL9Qtaw3xuSPkZfhqZ23YT8hWM51H8hIPnxxbIl5\/gC4ZtGsgPyJcZ+BWXkB+l+EeQ\/+sh\/5uyinGTh+fLVphfgD9FCPn9AD7gtepg\/vDJhUXGHFwD56t1IX2YiGuSy4F9AJ7tjWkZ+EqoF+uuvF6WuqZ5sh5XqHl78PjV4eUThXqgxm+G9IK3JyOx+GcdvrGP56dVR0k2n2vCi3cf37mBPl09syYgWfyAEK9eyXl6TtvNPVscb5VlG21xQ5JbgG+7w3zrlnTElbV2btkI+duQ0i2Zg43w1x5\/Q+LPENg744bkRx1hvrYbZ9ZclVy8d0lryF+rmid8Jdl8ugkzQH61mx6sOS3plc1r7YT4zaqtTzwu6T7ZhEF+wfx\/SVZMWXyoZ936mwfL01c2Zb8rKT3RJODfEcnrUXrwumRnFN\/\/JnnpsybIP778fkk34PPerSE+75R4hHqQtVWSNX0ZxDe0\/x1bJXNZgP+wfTZJLn\/K82895N9Lkpcj+pH6TPPk30i+lkD7DB4ty66xJzwj4aL0YbVkRUJZa9eWjX1t5Zm2hlSrZEsWwP+G7kMA\/xkVkmbFfGAPiP\/6pOWSZ3MB\/teH+P9fEsndAP9hezwkWUHOPnxz71bn2wuyq236mZIfpQn5Pk1yjw7in+d7Sr7kC8jnLSH8T5BsDfO9HOA5TfKkdF5r506gR1k19gSN5ISA7813EBJvYllr12bYf3tjKi4pKFnW2vdSGK8cdmnIpimBfDq1pNhEY9VOmN69xX1qscEYwIaGbZqSg+27t4bSnIDv9Uk+LG+CwB6ZXux1Xi9C+HZjq91WbQnk26lFBuMQRnsi6YUGYx\/2u9D89M6C7Eqr7jo2aeqS1l5ov7RVzROvYVcoiHfnW2XAXmewMjAftYTt0YF9oF\/Q2gX1ItPakHISu5i+8FA3wHd5RkNj+mfY6\/csbu15Zf0NaP8Jb2NdBLTXwFGI39exojshXjsPlqc3N2W9ir1ztlFiAPxj2qtn1mzD\/gz5vrG3FdgvbQumPBPJN86sWYet4\/kO8Vuf\/DIW9M8+fBPyGYznBayiHfDn+M4NLCj\/PHYG6sGGrjfLgR48j10uhfjl\/XHH89gyRsjv1dg\/TvH82wD51YQ9URTWj9RnmnNqsItCfieYMOXJCN9MD9Ysxz57YElr78vrQ3h+CpsVD\/F8i8dzOfb050J+LsCaMqH9uqH9Mn6O2eSA75tDfH8c++l9S6A9Ab6bJjyCvXW8CSkCzyMd5gdr8jHJp01I0c5Pdq1HOmp+VHMv1hSM4L3Kqr8H+3rK4taQPjQ3ZU\/GclLLW7s2QfvWN6ZNwjLgfBe2ZwZ2OYrPBGaB8SqwX3pTUxaGPYnPa+uE\/AX496O\/j9KDIdRlWArwfvX1MsD3IfR6FJ+vo4Eo\/l5F62B8G7bXFVSdHbFHY2PGaXRAwF+r\/iTanzuC16aJH6Jf3LapS14\/1bLFe2ppielD9Ce37KpQehlILx+wqUveON2yxXNqabHpQ\/Ror11V8tqpls0w\/310YNCmhnz0AD6+j2b22JUlr51s2ew79WSJ6T10GZgPQ3pgi38PfcbB8xXws9j4Hvpil11Z8reTuzb7Tj9Van4XRYatAv6+iw7frFcU\/\/Xkrs1+Pr9VB\/izKcSft9A\/uaxaWB7y9Ri65AZffpP\/9NOl5mPoB2nlrd2bNva0lmfUN6YfRlfdDfkV0rfDqNYj5Hsb+t\/X6uXFfzmxa1Pg9NPTzAfRzV6rtuRQ+55trlMLi6p3o53SeW1Qv4D\/ElvQgcsNsuJXP9+1KXh6xTTzOlRxx6LWnvUbOt+E\/HwZPXyxgSje9\/mujeTpiunm36HqC5F05fSa59CVSSN615C6GtX6Zx2+uX9bSF9WoX9QCfWrFq2B8+lW51tAj+Nq0V0ly9pC+vhM82QTepDXty7o\/8wVaM4Xjbhhz2c7N9LtVTNqnkR1hRAvgI+rm3PK0GfPgXwwf7dXz6iZj15B57Z1Qr3KMtnif4aunyrEy+PoN2eEevMoOiNO6I8Z6PyM8tbujRtDejYNndoBy+\/i9acQrZOF9Kwsq9aemIcm3xvWu7SVTRPuRV843Qj0ZxevP+nodydB+tNdvJ6koC8EgF5tC+lVPHrwzkWtPes28HqYrUIVKeWt3VD\/MuwNaUpUr5nf2gX1LtNSn6xA8080okW7Pt21geswPVhDoN1Qv7aG+BpA1n7WFM43P2geQh6ZtKi1ZwPP38bMQWSFZG5b564tg8fKsv5Hv3qQy59CPdm1AeiJuRN5aBrg8\/qQPn6L\/L0I8Hn91TfKUp9pyrmAnIF6tinE5y+R+z\/h9WcD0B\/zacQtnx\/RL3viSaQgD9r\/2oHytNVNkz5H+jMXto7o3XsIG6VXbyPv3bektRfqXdqqpgl\/Q6q5OW2dUI+yjLa4vche7YLWrm1he+xE9qdCfgB71TekbUcevwvo3YaQ3q1FuqBebQnp1Srkek4E302NWSuRlf02dTHUh9NLi00rEWQQpndv9ZxeUmxcifTcsqmKoZ6cXlpiakb+PGTl+Q\/zm5FFfZH8ZSWmRiTRCfgP9OH0YoOxEfmsx64sfu1Uyxbf6SdLTPXI7og+nP6f+dmOFHbblcV\/A3pz+slSkxWZGJq\/t0E+W5D9nXYFzAf6YapBXvNYQD7ks6HajChu2gX6YkKWJwr9U41M8ZUc6tizzfXOguwKi64SaboO9OUEKP90qXk58qxyfmvXjs39R8oz6+xJTyGXrvL6sTlwevk08yLkx1cierJ8mrkcWUPNPtwJ\/ZVVbdWXIUuLl7b1vbT+CsTH5LnIa9\/Vy4pfPRHSkznIC9kRfzc2ZvwUkV+ulxXv5\/Vmuvkx5JuI\/qeubpr0EFL3bT1hAPlke8V084+QLkTI52Kk\/UIDYdgXzi9Eltwv5PcDSK5+hM\/1KXcghvQR\/WxIz0ZqCaB\/WwZ4\/UtHeu5e3NoL8QL4m4z0ftGAG\/Z+tgvqjVmBPHy+AehPKC1FdgN9C\/HXoqO5fWcbJIY9x3dtBHpiprjnBPrZmOXhXEllgM99bZDPHk6mBnzm7V2f5Obc9JzDnYC\/wJ5xQ1wwpDd8fde4+e2NWBFIsx3GmeZvOSMmtMe33ISJCyG\/D5WnNzZmnuNaTzViRS3h8uc4Duor5PMzTZM\/5X5ZuLS172XI59VNOf\/NfRgntNdHHHayES1qCemL+e+cUzavrQvqXWatPfEdjorS06PcmQzevoDPDemHuSWfC\/TJ\/BoXF5x9uBPaK6vKotvDrb13cWvvKxtCermbO3q8UaA\/G7h6dk4btEdZltEa9ycO\/1SY\/yK3Gerh5hD\/f8+tjdLL33IZU6Ce3oT2z17JLfm4kYP6hAJ9auR6BfpnS1jOfRClj3O5JxLKWrshfzJs9ak\/555RhPgBxp\/0MPcSCfC\/DeC\/yqr\/MZdlWNoWsuczTTnTuOosgPeN3YfK0xsaMvK5vXlLWvug\/gF8P8C9OmhVF0f04wEuecAq0Jup3N0Oqwbmu08vBumXb9tUxW+catnqBfp0P3fUadUUH2zfvQ3qSy5HR+nPfVyRy6IpfhPmLzIY7+MGoX5uHYL2jL+Xs\/XaVCE9WlZiupt7z23RFkM9Ob3QUH0Xd6XbJtCrO7lpXou2+BCfX1R9B\/eLLl6vtkC9msi9d5PXpy1Qn7K5Kbm8f3l8ZHA5N6A+tUC9MaVzE0D8sy3svyTulWt2OdSjQPvTpWYd57lilxug\/rQvn2bWchlpAn1PV3J10nltXVDPM2tsCTJu2ff1MsOrJ3ZtDoLyOPf+XRE+Nzdmc+zHlyP5K6aZ\/eydlyL6smK62cO+45t1uHP\/9uF3FmRVWHSD7J8uCvWnhy2fLJwvOtnb39RLof5QIP8Gi9E8HiB\/9ddYRCXU02vslUQhnr5lf\/E1eP4z8HzldHMHe+jLBinQm010e+UM82esFcYvW0P8\/oRlshe29vB4amzI+G9WEdKnTVCPPmT\/O0p\/32FN5xrwIqhXHVUzzEfZhwqWAL5fOwD4PqmVPayH82eI76+zn5xpkBRB\/eqonmn+C+sgAN+BPmbW2BL3sSegvm4I8b2FPRalp9vZ7I4GCdQn+PxW9q7ArDDfKy26F9lF90B\/QL43TljLrjndINCz59jnGKH+rWa\/PNkg0K9m9iWBXtqTbWx9MtDTTb1AT+vTLOzwHUL\/VLP3nWiA+rUR6s9ydgCL2NNsS5jP\/uEzXp\/4\/J+z26F+boT62ZD5EHv500akEOQjZ8wPmmewM+IXtHZDfcyw1qeUsCvlEfvU2hOL2erSpW23oP1TnmmaXMDu5fUO6oNFn8veLAT6sCGkt7ls3ifC+u9gH8ssb+2B9gR6kcWaHwB6AeyduqpxYhobYCP2MVrjFKxPoJ+NEwg2UTuih\/ZkksFAPLR5E9TD+jQ\/Y8fnQr4cK8s02xKczLopiyA\/3oT8GGB+NMTr0TYP0Jt+5rjDoik+eDqkL\/3MPwaEetXP\/GwY6sseqC\/Vt5nc\/pB+AX0y3mI6XEI96WP+cssK9Gn3Vm\/70hJjLzPLw+vJdpjfwySG9Gurt31Ziamb6fAWH+rYs931zvysijrtTWZtj01peO1Uy1YfyL\/G+LpAGuhP+5MlpstMdadNYYD60\/5kqekb5osbkfRTpaYvmDwQ723YCPW9IfM8k0iN6LdFf4558LpdbvjryZYtQH9MHQylmN\/WtWNLSO\/bmdNR88FJ5sBVqE8tQK+mmT5m9Fci6eXTzO8za76zC\/ToXWYVMqetazfAX6bRGn+MuXbJLtCjI8yVrAi\/GxoyDjJ\/NETw8kxTzhvMI6F4aDPUqz2MPB\/iA\/K5cdIe5uCFSH7FdPMO5lXdgtZuqK8ZFnvKJkYZ0auOiunm9YwzSj9fZrbmCvA0cS2zPQ3iB+CxviH9Bcb6Vb20COpVR+V08x+Zh\/yzjkC9BP7R\/ZLp+KIeL4J61FE5w9zM3Hu3UH9tzCsCfbToLUzu+Uj5qhnmFcwfVBF7g\/iTeflsvQTo1yYG5C9hliUJ7b+IOQ31eGPnm+VpTQ1Zs5m+Dl6\/NjEd1TPMsxgPOretC\/I902SNf4h5pJ3Xm00s0KdiZuUEgZ5mTmVePSXUo7uZ3DhoP8h3e8qdzEqZUA\/vYKaUAL5vCPF9MvN2APB9e0jvshgS6s\/xXRu5M8aZ5nTmzYIlwJ8hf+mZezMA38P2VTFlnzeghVCvzphmmpVM0dQlrX2vhPmvYAh2zuGuPdscby3IrLbEBejDxxvC+mF60OyjT0Tpq5tWaea3dm\/ffPtIWUadPbmHHkiG9gvpZQ\/dLIH2gXpgTeimiU8bBHr0PV1956LW3oh9P6bvIuH4Qnz5N62eJNTLD+iAQA9tif+kP4B6GbZfK73cadEYDrbv2e5uX2SoPkRfHgJpoDfti4uNh+gbwxaN4c1w\/pt02aBFbTgQzn+TrnTXaQ1QL9oXGqoP0mf6I\/lLio0H6RueSH55UfUb9CO3rSrDG6d3b\/O2Ly02\/o3+HRexn9Ea9zf6\/T6ryhDRo\/30\/b1WJUwDfTHupvd3WwV600J\/BPV5Uzevz9vpnxeN6HljznZaH9KjrVCPNtG\/uSnUnz\/Tg9dtCqgvMP0S\/VVY38tSVzVOeoHepuX9dbgsw2JP\/iO95JpNoEfP0W6o32F\/PUsfv2KDerMF6s8zdPV9gL8bQ\/5\/hl7sg\/zk9VO3kp76vV0GywM9MjXRz6WWtfbA+SEdzJ\/09stQj1o2BzuWTzNV0Oy3dqIIpMmOFdPMK2huCsQDnC8asp+m91GzD3dB\/cyssugX0VUX7UTR\/s9h+enm2fTzyvlt3ZDPGbW2pF\/QJ7+xS4uAPkH9eYz+KYiHtm4O8fkR+rWcha29EE9pYP6lc7+2S4v2wfKV080P0gwy53DX7m1Dx4D\/4gvp9V\/W40CPNtMg\/07aGdKTzVBPJtNlMF7aFNLTFHruuZDeQD1JpifqI\/y22FOS6dMjelZZp0ukV4H4Z9dWXh8T4+l+w9K2W9DfKasbc+T0sTNCfZLTTflL2kJ6vKpxEkvp08uBfSG\/69NJKomZA+wV4m+Q0nbUh\/TqTPUMc4DC7l\/c2vfKxusHy1JXNk7so6yn67FCqFdnqmeaO6ndd4P5Omz\/G5RUHbFvnS3pInXyZKS8cab5AvUrTKh\/X1MXoX4Ce6f\/z3rmNPXgHUJ+f0bdcQLoz\/GWTVCv3qEeDM4+3AX0bH5mZZ3uHeo3n0F9AvmmmeZWqn9CxL6NDZlvUgGoj1sHjpZl1NgS36Bao\/RzP3WZ15eWTVCvtlHro\/RsHfVKCbQv1NPGyX+i\/ibQx\/r0F6j4woh9VzfmrKFenRqx16rGiSupdZr5bd3bt0C9syXbqGCUvlmpNa66sL4sNFRbqYCzTqA3Fsof0pcdUF8s1CpHnUCv6qiVXkNrx94dgE8rarV1lAvo08HTe7YDfaquoawDQj0yUsUgPlvH87FhgpHqvm0J69GSYmMl9eQtkD4V0qcVlDmlrLVny+beNsjHp6kve3l92gb1aSn1eI9Qn8qo97ugPu2GemOcS71FQn9BPtbp51D3dVqh\/mz1dzxZappNXYzS859Su29E8p8qNf2EUly3yYv+erJlawCkf0Q9q5jX1s37s9aWNI1afZXP3xLoeLrUZKBKEwT6nlpA\/W4S4O+mEH\/zqZvf22RFUG86np5mmkopeP5C\/bXE30XN+i6Sv3yaKYv6xyWbDOgNn06gsqH+fN6yBeiPKY4qyRrR3\/oMBdXlm3Wka\/+O4XfmZ1bU6WTUCxd4\/dlCdlRMN0mpZB2PB6CntmSc+qVUqJ8Y9VbREsjnA5DPFHn7a6hPLVCfTH7yR3kAbxtD+uwg53wF9agF6pFpgLyLBvoXtncv+eV9EI83AH8bJtwg3\/7CDvSpBeqT6Ssy6bwdL9wL0meqZpi\/JBvuEurpFySqEurlCfKP6IjeWeKPk6vP2iXweQY8\/yn5WWJZa89Wns\/21H+SaZMF9s\/6O3mho15SuAeWr55hPkCWByL2qqzTvUYWt9djhXuOt2xmz1TPMLWQQWJeW\/cunr\/WxF1ke\/bC1l7I77SG+oxt5H79grbubVtuwfkp5UVyH9Sz7SH+PkfOKxbq4yrye6iHG0N6aCbr71\/cFta3honV5Noo\/VpOopK5h7tb4HhNlvgyUnXPotY+oHdlqc0N2Y+QJ4Kzj0T0SP8geQzo14ZNITw\/QP7ZU6s1HAIM7SgvqnqA1LprQ3zuWGiofoDcMMzzfYcbpKeSv5ZH8F1jTZxK6iJ60LHIUJ1Lrh+qC\/G7Y7Gh+h5SOVgH+A3TxdVTyBf6LeqiA6d3b\/d0LCk23kHeG7+grQfaJ91qT5lMcrcsqiLId5A\/iUyG8UfYXpPIFROhfaG\/6jMnkKv7QHnA946lJcZ0crDHoiwC\/Pd1LCsxJpKV3VZl0WvhtJb8ptOqgGl\/x5MlRhn5+E2rouhvJ3dDvhsR8p\/XYTrEby7IeWcd6XoV6FfmilptIHjXNauA777g8\/jctu6WbYPHFmSYrQneoEYr1FNX8FeFS9puQX+mrG7IcQS3XYnoxdOlpp6g6oGIf1c1TOwJoiH+b4X8vx6cQQn16fug9bKQ\/xeCr9+7GPgb8Ke5YcIXwYvf2mD8wfP\/s+BDF20CPfgkyPL61A\/0yZr0cfCnMD4J4SH7g+BGZM7h7t3bht5akGG0xL8fPPCNDfJ7C3WmYrrp7eDfExa09mzd0gf9ldoW1H5tlxZG8g8EfQI9q8\/cH2z+0o4X7vu8ZQt9pnK6aW\/Q7h\/Rn1rdpuBFwO99n4XyXwky0rlt3bu2DR4rA\/ZcG9ymm9\/WA+2ZbrEl\/z4445yAz6ZfB49F6UlTcO8ZYb49mGoAerXxKm\/\/8uDvoV5shXphTZofVGL8eI\/B8c4LLj9txyD\/Ib8fCd64a1Fr3\/pNIT49FPzopB3ECzB\/pml68FpIHwC\/anW5wewT9WgByOfOGmeaJgWfkwF92BbiS1JwCjvncDfkf0Z1XVxSMDsugn+LLSUu+JMJ5a29Gzd3t0L98AayvEWtHXt3hvDnDfzVXastOtQe4qsnMMVVq4FpwM8qV+Cgs1ZTxPN1kaHaGbjbUacpOti+Z4cHpAcDBwbr1EUHw3y8HbhjoA7wMZS+FVgb0ZMMkyWhNyDThO2VXmdL7gnsv12nguUBP6tvBNJu1UG+bvd2LC02fh\/Y3GtRAj5u9wE+XgjIo\/h4ITCXBHq0wwntpT8feL7LIuDnqYDrpkXAzxOBX92zqK1vXdj+HweMN3i+boN8fT9wAfARpAEfjX8PsFH69FYg506Ix85DZWlN9VnHAo9dhfzdzfP3SGAvN+dw956wP94IvPu9VVb0V8C\/M0+Xml4LHBDoky1lTyDnO6usEPLzzP\/Mt1sCL4P59NWQf3SbAusvWQmYT4L8FwLBizai8FWYXjHN9JuARArtC\/XCkvDLQOUFkP95KP+ZwDotwPvW27y9GwOfUrOPdEN7ZVTV6a2B9q9tAr49HSj4yiaF\/ILpuYFdX9gEfPtp4LdKId8fDySjAO\/bIb\/r4n8SYM8JyxsC709Z1NYH9KAstak+Oz9QGZo\/t0A+5QUC\/llHuiP8nRT4vEOYnxj4HRHiLxyfPnB3ux0r2PNZyxb2bPUMkypQwsw+3L13h+PtBRlVdXGyQKI+Ml6LLYX1v3yKL78Zlu\/395+wh\/lUPdN01b8Rmwv7z\/P1sl+int\/Ww+Oz1pr0rf8Xn9sF\/Dvjrwny\/X17fkZlre64\/9BxO1LQcrxlMwLy\/ze\/wVOj5RkG+PSh\/8MQn3ZCPv3T\/9Aw5FMkPe9uaJ+bvH3+4f\/YIeTbO\/7\/GqpVj\/DtmF\/+aaQ900zTUf\/HA7WQfzsg3w75f9TP82mHF6QP+hH5iF5YEl\/3f3BLyLfX\/Iej9ONVv6EP8O\/Unu3eM0uLq1v8R3rqIN+2+84sLTFu9++B+rI1pC9b\/Hd2W5SFfP6yEuMr\/t2dFkXha6d2b\/fz6QPeWUe6IZ4zVtRo\/+jX3wT5gG9nniwx\/tr\/h+sWeSHk25knS40r\/Soc4Hn7IK8XTX7v1Uj+U6VGi\/9FDfQPxLM1ucZvumKVF\/4V5j9daqzxXyIj+K6s1T\/l\/wbw668ndm8D\/DPO9z98GfJtN8+3x\/zHvrUCPu3m+TXTfwci5O9M\/28V89p6dgL7pddYEqf7My9aBfy737\/2Gyvkz1YKpO\/xJ\/rheIffmZ9RUaOb4Hd+ZQvnV0w3xfkXf8nzYysN0gr\/i1H8xfy\/oEf4WavH\/B+dD5c\/WzndRPnUuhE+W5NdvinnbJKCvZ+3bGFA\/te+3VF8POtDVbD\/\/Uchnj\/z\/S4A8Bvu3z4fLpt7uGfXtoFjC9LNlsTdvo+Y2Ucgn0D7cbt8qSG+bIF8edm3mefXLcAva8oLvl9+Fsk3zjS94DvhqdEWtnbs3eV6d17G8j\/4ZrtBGuD9THlR1e98Xw\/XaAohH84sNFQ971virNEUvhlO\/8r3\/VCtGqY9ZxYZqlb7nhqsVRdCfJ9ZZKhu9H3fH0kvLq5u9F3i+bB7C+Sf1bf0dq2qEOL\/zJLi6jpfomTu4R6e36a6eJPvYl+dqvCNcP4KnzPI24PHy3Lf\/N46ZeEbp\/bs8AH8L\/f9QT2vrWfHtpAeLPa1d9cBvO+BfKie63u4q04B8Q\/x\/lPfBzfrBPh\/yPfADR7v2yHei31\/u8bjeTvEc5GvEMwXe3dAvNXG5ft+L58H\/XF0AcBbri85Cv+Tffm+WUchn+YBPk3yvfi9RQbxD\/Gd6kuLH+GnNSXZ579sgXjn85U+0yULAdMA70bEtwGfe7gH8i3dVJfAeL\/m8b17G3l2+TQj5a2A88XOEJ+c3h9fsEoLQD51dsU0o8MrA\/PLjm0hPN70tn1tlRbsP7F7K8z\/ypv8FUh\/DtIV003t3jeROYd79uwYemtBenVd\/KdeVBniFxhv0kfevVH82epVEpH+mesStnq\/jOLHb7x\/hnwIt1\/pnYjNOdyzG9ZvrItf6lUGhXgv9\/4+zIcF6bWWpNneG24zwOu+Xa4z5UWVv\/DWunh87nKfKTdUPe51OIV4fdi70hHCqwekH\/L6B2sEeJ3u\/eUAxCdILy6uKvIGQ3jc6QV4nep95pYQn\/d7H2VnH+mBfEuvqo2727sW8nE75GNd4l1eT2+tEuLVB8pP8Db01EbwWVKd7Z3nfeJo9194tpm1Kd7bXTw++Xydt7IT4nMPxGO11quPm9\/WC\/mbZrGmEN7LNyBe9\/D49Ht2SXj7HYP283rWkNB+0P81+h5P6RWLrOCvJ3dvD559qtTY45FoRvhhSTrnybhskRVAvJ19utR43PM+NweO76356dW1cR94cMU8OD4e3x94PoB43gXxbNau9\/RRs4\/0QLylV9boX\/HES4X4XOaZio7gpzZ+oWdKYNaRnld3Db8zL72iRveE5\/fKeYd7dm7v5\/F0l2cJE6mvqiZuoueVKDxleJo8vPdD9vPQLrMG3nGfLS+qTPD8cdgcwsPZckOVzqNyhvFxdqGhSunZOMT7f5fn7CJDFe5JBHg4CPAA0qx7e3+NqgDgwXt2cXGV3518u0ZVcCCUrh52vy7Aa2280705OOtIz\/5dw+\/MB+Nxurf11YLye3Z6zy4pru5zJ\/TWKgsgHkD6untDN0gDf59dWlJ9xY2q5x3uheNPq7UkXXQru2oVBQAP\/rPLSqrPuv9wU5j+t7sd4m9nCH\/vuS97nzjaA\/GUvtysfcctlwvx+LIbAf7ZH\/bPC+5MHPYf2NNYm1Du\/hEy50jPnp0hfy9wP+yfdTTknxVm3aPuPyrmHe6F\/k+rqUvMcdfTQn+nubdBf8P6TLUJg67DgcjzFWZdv+sdFNY\/BOuP73atd5u0Ba0d+1rcZ8uKKm+4Ul1mTUFrO0iXF1V+7\/qL06wpONS+D\/qr8oLrbodZXXAI+muhoeqCC1NF7FVTl3TedXjQrC6I+PO0K3+gRl1wMJz+1PXubaE\/P3QZbgn8WfWB6xqPNwePt3+4KA+wZwu0p0n7juvd3holLA\/91+bSyuYe7oXjTTPXJR5yFfTUQP\/u9J1dWlz9F9fRLt6\/O\/3Avztd93YK\/feiK40cwYtZ91vX3SH+vjUf4Gmeay434t+auHmuSl+kPyvM2odcL8nnHu7dtWOAb3+iay307y7oD7M+0bUfn3O4F\/o3zVibMDDcDv3ZEvJn7\/AnyJwjvdDfadU18f8c9kB\/7gr58+\/D2R6TtqCtYx9s8em3hg+5TJqQv8qLKluHDcMmgb8ODqcQEXuYahNeH\/437699LcBflfuHHxuK+GehoWrncMeA0F8bh+f2m6F\/dnlB+k\/DX92KpBcXV70wbAgK8fTb4fI+3n+7gD+qnh0uxuB4hvjxPDP8TU\/YX+eWFFfZhhd0Q\/+A9NLi6qrhLzproD92+s8tLameN\/xHr9Dfc4ar2dlHevftcrw9P62qJm768Abe39DetYnZw6+Ss4727t\/lhP3Rxw23SuYc6d0N2zfWxA84bwv8ZdJ2O7\/mZh\/p3Ruu7x9ONT37SC\/0V1qlWf+ac5IUPj8Inq9NWOucH5h1tBf6K22FSfd750y3SZPf1rFvt\/tcWVHls86TwyZNfitMlxdV\/sr5GDrnSO\/e8PhXOxc4TZp8YH\/3uXJDZb3zypBJDdOecwsNlTVO46BJnf9mOL3C2d9vVuW\/2b63xXtukaFqsbPhtlmVD+1\/brGhaoHzgOeJo71\/3e16d17a0ybtHKe7byS\/uOpR56peszIf+gPYe4bT112jzH8jnM53ruqqUcC0\/9zS4qpBh8r3xNHev8D6lpu0Lzoa\/TA9\/C4c70JHh9sIxrt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auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-n3nki66f.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n3nki66f.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n3nki66f.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n3nki66f.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n3nki66f.nxe :where(ul,ol){padding-left:20px}#nxe-n3nki66f.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n3nki66f.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n3nki66f.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n3nki66f.nxe svg{display:none}#nxe-n3nki66f.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n3nki66f.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n3nki66f.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"console\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>A-P11 \u00b7 TOPIC \u00b7 credibility-and-sparse-data<\/strong>GRA dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GRA<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Actuarial and Quantitative Risk<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 Actuarial and Quantitative Risk<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Credibility and sparse data<\/h1><p class=\"nx-tagline\">Many risks have little data: new products, small portfolios, rare perils and emerging markets. Credibility theory blends a portfolio's own experience with wider data according to how much the own data can be trusted. Bayesian methods, pooling and expert judgement extend this. This page sets out how actuaries estimate risk with sparse data, where estimates go wrong, the sources used and who decides.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Dossier<\/strong><\/span><span class=\"nx-eyebrow\">A-P11 \u00b7 topic<\/span><\/div><ol class=\"nx-register-rows\"><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-register-n\">01<\/span><span class=\"nx-register-k\">In brief<\/span><span class=\"nx-register-v\">Definition and scope<\/span><\/a><\/li><li><a href=\"#nx-sec-question\"><span class=\"nx-register-n\">02<\/span><span class=\"nx-register-k\">The question<\/span><span class=\"nx-register-v\">4 mechanisms<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-register-n\">03<\/span><span class=\"nx-register-k\">Key terms<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-field\"><span class=\"nx-register-n\">04<\/span><span class=\"nx-register-k\">Field<\/span><span class=\"nx-register-v\">H3 field plate<\/span><\/a><\/li><li><a href=\"#nx-sec-indicators\"><span class=\"nx-register-n\">05<\/span><span class=\"nx-register-k\">Indicators<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">6<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-pipeline\"><span class=\"nx-register-n\">06<\/span><span class=\"nx-register-k\">Pipeline<\/span><span class=\"nx-register-v\">Run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-sources\"><span class=\"nx-register-n\">07<\/span><span class=\"nx-register-k\">Sources<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">5<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-decisions\"><span class=\"nx-register-n\">08<\/span><span class=\"nx-register-k\">Decision holders<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-methods\"><span class=\"nx-register-n\">09<\/span><span class=\"nx-register-k\">Methods<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GRA \u00b7 In development \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>A drought index against its\u2026<\/a><a href=\"#nx-sec-question\"><span>03<\/span>Credibility and sparse data: how the\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>05<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>06<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>07<\/span>Where the evidence comes from<\/a><a href=\"#nx-sec-decisions\"><span>08<\/span>Who decides, and what the evidence\u2026<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Credibility theory is the actuarial method of blending a portfolio's own experience with wider data according to how much the own data can be trusted. With Bayesian updating and structured expert judgement it supports estimates where data are sparse. Actuaries, product committees, supervisors and reinsurers hold the decisions.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Spatial finance<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">A drought index against its attachment point.<\/h2><p class=\"nx-lede\">How a dry season is read against the point where drought cover starts to pay. Synthetic season and design terms.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><figure class=\"nx-fk nx-sf\" data-fk=\"parametric-drought\" data-gl=\"sf:parametric-drought\" data-sf=\"parametric-drought\" data-sx-credit=\"Natural Earth (public domain) | synthetic season and design terms\" data-sx-src=\"Natural Earth ~ public domain ~ https:\/\/www.naturalearthdata.com\/ | Design scenario values by the Global Risks Alliance ~ illustrative, not data\" data-sf-learn-views=\"sched\" aria-label=\"Interactive map: a design rainfall-season index across the agricultural zone of Senegal, Mali, Burkina Faso and Niger. The panel chooses a country and steps the season.\" 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\"><figcaption class=\"nx-fk-cap\"><span>A rainfall season across the Sahel, read against the points where drought cover would start and stop paying.<\/span><span data-honesty=\"true\">Synthetic season and design terms; no cover is implied.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/credibility-and-sparse-data\/#map\" data-nxf-title=\"Actuarial and Quantitative Risk live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">The question<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Credibility and sparse data: how the risk develops.<\/h2><p class=\"nx-lede\">Working where loss history is short or missing. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Blending own and wider data<\/h3><p class=\"nx-p nx-mt-1\">Credibility weights give more weight to own experience as it grows. Too little weight ignores real differences; too much prices on noise.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M14 2v4a2 2 0 0 0 2 2h4\"\/><path d=\"M4.268 21a2 2 0 0 0 1.727 1H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v3\"\/><path d=\"m9 18-1.5-1.5\"\/><circle cx=\"5\" cy=\"14\" r=\"3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Benchmarks may not fit<\/h3><p class=\"nx-p nx-mt-1\">Industry or reinsurer benchmarks reflect other portfolios. If the portfolio differs in mix or practice, the blend carries bias.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\"\/><circle cx=\"9\" cy=\"7\" r=\"4\"\/><path d=\"M22 21v-2a4 4 0 0 0-3-3.87\"\/><path d=\"M16 3.13a4 4 0 0 1 0 7.75\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Expert judgement fills gaps<\/h3><p class=\"nx-p nx-mt-1\">Where data are absent, structured expert elicitation provides estimates. Without structure and documentation, judgement adds hidden bias.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"12\" r=\"3\"\/><circle cx=\"19\" cy=\"5\" r=\"2\"\/><circle cx=\"5\" cy=\"19\" r=\"2\"\/><path d=\"M10.4 21.9a10 10 0 0 0 9.941-15.416\"\/><path d=\"M13.5 2.1a10 10 0 0 0-9.841 15.416\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Updating as data arrive<\/h3><p class=\"nx-p nx-mt-1\">Bayesian updating revises estimates as experience accumulates, showing how far data have moved the prior assumption.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Credibility weight<\/td><td data-label=\"Definition\">The proportion of weight given to own experience when blending it with broader data.<\/td><\/tr><tr><td data-label=\"Term\">Prior distribution<\/td><td data-label=\"Definition\">A probability distribution expressing beliefs about a parameter before new data are considered.<\/td><\/tr><tr><td data-label=\"Term\">Expert elicitation<\/td><td data-label=\"Definition\">A structured process for obtaining and combining quantitative judgements from experts.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Indicators<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Credibility weight assigned to own experience<\/td><td data-label=\"Unit\">proportion, zero to one<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Pricing analysis<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Exposure underlying own experience<\/td><td data-label=\"Unit\">exposure units<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Portfolio data<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Difference between own and benchmark estimates<\/td><td data-label=\"Unit\">percent<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Pricing analysis<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Expert elicitation sessions documented<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">per model<\/td><td data-label=\"Source type\">Model documentation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Posterior uncertainty of key parameters<\/td><td data-label=\"Unit\">interval width<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Bayesian models<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Actual-to-expected ratio after launch<\/td><td data-label=\"Unit\">ratio<\/td><td data-label=\"Cadence\">periodic<\/td><td data-label=\"Source type\">Experience monitoring<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/credibility-and-sparse-data\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Credibility and sparse data as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-a-p11-topics-credibility-and-sparse-data--t nx-dag-a-p11-topics-credibility-and-sparse-data--d\" data-nx-z=\"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\"><title id=\"nx-dag-a-p11-topics-credibility-and-sparse-data--t\">Credibility and sparse data: claims reserving<\/title><desc id=\"nx-dag-a-p11-topics-credibility-and-sparse-data--d\">Claims data are built into development triangles; chain ladder and Bornhuetter-Ferguson give IBNR; a selection is made with a risk margin, tested by actual versus expected, and signed off. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Sources<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">Actuarial Standards Board ASOP on credibility procedures<\/td><td data-label=\"What it gives\">Professional standards for credibility<\/td><td data-label=\"Licence class\">Free, ASB terms<\/td><\/tr><tr><td data-label=\"Source\">Casualty Actuarial Society credibility monographs<\/td><td data-label=\"What it gives\">B\u00fchlmann and limited fluctuation methods<\/td><td data-label=\"Licence class\">Free to read, CAS terms<\/td><\/tr><tr><td data-label=\"Source\">Industry statistical agent data<\/td><td data-label=\"What it gives\">Pooled experience for benchmarks<\/td><td data-label=\"Licence class\">Licensed, terms per agent<\/td><\/tr><tr><td data-label=\"Source\">Structured expert judgement literature (Cooke method)<\/td><td data-label=\"What it gives\">Methods for eliciting and combining expert estimates<\/td><td data-label=\"Licence class\">Free to read, terms per publisher<\/td><\/tr><tr><td data-label=\"Source\">Stan and PyMC documentation<\/td><td data-label=\"What it gives\">Open-source Bayesian modelling tools<\/td><td data-label=\"Licence class\">Open source, BSD and Apache licences<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/credibility-and-sparse-data\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Decisions<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Actuarial and Quantitative Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Pricing and reserving actuaries<\/td><td data-label=\"The question\">How much credibility to give own data and which benchmark to use<\/td><td data-label=\"Evidence this page serves\">Exposure, benchmark relevance and professional standards<\/td><\/tr><tr><td data-label=\"Who decides\">Product committees<\/td><td data-label=\"The question\">Whether to launch products with sparse data and what monitoring applies<\/td><td data-label=\"Evidence this page serves\">Uncertainty estimates and monitoring plans<\/td><\/tr><tr><td data-label=\"Who decides\">Supervisors<\/td><td data-label=\"The question\">Whether assumptions for new or small portfolios are reasonable<\/td><td data-label=\"Evidence this page serves\">Actuarial reports<\/td><\/tr><tr><td data-label=\"Who decides\">Reinsurers<\/td><td data-label=\"The question\">How to price cover for portfolios with little history<\/td><td data-label=\"Evidence this page serves\">Benchmarks and exposure data<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Guide to credibility methods<\/li><li>Method note on Bayesian updating of pricing assumptions<\/li><li>Brief on structured expert elicitation<\/li><li>Questions for reviewers on benchmark relevance<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Core questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">The questions experts ask.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>How much weight should a portfolio's own experience receive relative to wider data?<\/li><li>Which external data are relevant, and how different is the target population?<\/li><li>How should expert judgement be elicited and combined with data?<\/li><li>How should the extra uncertainty from sparse data be reflected in results?<\/li><li>How can models for data-poor contexts avoid simply importing assumptions from data-rich ones?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Mechanisms<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Credibility and borrowing strength.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Credibility theory, developed by actuaries over many decades, blends a risk's own experience with a broader collective estimate, giving more weight to own experience as its volume and stability grow. The B\u00fchlmann and B\u00fchlmann-Straub models formalize this as a linear estimate that minimizes expected error, and they are closely related to Bayesian methods and to hierarchical models in statistics.<\/p><p class=\"nx-p\">Hierarchical Bayesian models extend the idea, letting groups such as regions, industries or policy types share information while retaining their own parameters. They make uncertainty explicit and can incorporate prior knowledge from expert judgement or related data. Structured expert elicitation methods help reduce bias when experts supply estimates.<\/p><p class=\"nx-p\">In emerging risks such as cyber, the problem is not only volume but change: the threat environment evolves faster than data accumulate, so old data may be less relevant. In lower-income markets, using models built for other countries can embed assumptions about building quality, health or behaviour that do not hold.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and data<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Methods and their limits.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"3\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Credibility<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Credibility models<\/h3><p class=\"nx-p nx-mt-1\">Blending own and collective experience; requires a sensible definition of the collective.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><rect x=\"16\" y=\"16\" width=\"6\" height=\"6\" rx=\"1\"\/><rect x=\"2\" y=\"16\" width=\"6\" height=\"6\" rx=\"1\"\/><rect x=\"9\" y=\"2\" width=\"6\" height=\"6\" rx=\"1\"\/><path d=\"M5 16v-3a1 1 0 0 1 1-1h12a1 1 0 0 1 1 1v3\"\/><path d=\"M12 12V8\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Bayesian<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Hierarchical models<\/h3><p class=\"nx-p nx-mt-1\">Sharing information across groups with explicit uncertainty; results depend on priors.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\"\/><circle cx=\"9\" cy=\"7\" r=\"4\"\/><path d=\"M22 21v-2a4 4 0 0 0-3-3.87\"\/><path d=\"M16 3.13a4 4 0 0 1 0 7.75\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Experts<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Structured elicitation<\/h3><p class=\"nx-p nx-mt-1\">Formal methods to obtain and calibrate expert judgement.<\/p><\/div><\/div><p class=\"nx-note nx-mt-3\">Limits: no method creates information that is not there. Sparse-data estimates carry wide uncertainty, and results can be dominated by assumptions about the collective or prior.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/actuarial-math\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Actuarial mathematics<\/span><span class=\"nx-mledger-role\">Reserving with development triangles \u00b7 Premium build-up \u00b7 Annuity valuation<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/actuarial-math\/\" aria-label=\"Actuarial mathematics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Generalised linear models in pricing \u00b7 Bayesian credibility \u00b7 Goodness-of-fit testing<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Reserve ranges \u00b7 Parameter and model uncertainty \u00b7 Sensitivity ranking<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\" aria-label=\"Uncertainty quantification and model validation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/computing\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">High-performance, cloud and edge computing<\/span><span class=\"nx-mledger-role\">Monte Carlo simulation \u00b7 Version control and reproducibility \u00b7 End-user computing controls<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/computing\/\" aria-label=\"High-performance, cloud and edge computing: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Credibility methods assume the benchmark is relevant, which may not hold. Expert judgement is hard to validate. Results for sparse data carry wide uncertainty that is often not reported. Actuarial and Quantitative Risk explains methods; it does not set assumptions for any portfolio.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-14-sec\" class=\"nx-section\" data-hue=\"finance\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What is credibility in actuarial work?<\/summary><div class=\"nx-answer\">The weight given to a portfolio's own experience when combining it with broader data, increasing as the volume and stability of own data grow.<\/div><\/details><details><summary>What is B\u00fchlmann credibility?<\/summary><div class=\"nx-answer\">A method that sets credibility weights from the variance within and between risk groups, balancing own experience against the collective.<\/div><\/details><details><summary>When is expert judgement appropriate?<\/summary><div class=\"nx-answer\">When data are absent or not relevant, provided it is elicited in a structured, documented way and tested as data arrive.<\/div><\/details><details><summary>Can industry share data to reduce sparsity?<\/summary><div class=\"nx-answer\">Only aggregated, under GRA's written competition policy, once membership opens. Competitively sensitive data are not exchanged.<\/div><\/details><details><summary>Is expert judgement scientific?<\/summary><div class=\"nx-answer\">It can be, when elicited with structured methods, calibrated and documented, and when its uncertainty is reported.<\/div><\/details><details><summary>Why is cyber risk hard to model?<\/summary><div class=\"nx-answer\">Because data are short, threats change quickly and losses can be highly correlated across firms.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-15\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-15\" class=\"nx-h3 nx-mt-2\">Around Actuarial and Quantitative Risk.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/loss-distributions\/\">Loss distributions <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/extreme-value-methods\/\">Extreme value methods <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/climate-adjusted-pricing\/\">Climate-adjusted pricing questions <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/dependence-and-correlation\/\">Dependence and correlation <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/capital-and-solvency-models\/\">Capital and solvency models <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/model-validation-quant\/\">Quantitative model validation <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/\">Actuarial and Quantitative Risk home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/\">The Global Risks Alliance home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/trust\/\">GRA governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Actuarial and Quantitative Risk<\/span><h2 id=\"nx-close-16\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"mailto:contact@globalriskalliance.com?subject=Actuarial%20and%20Quantitative%20Risk%3A%20Credibility%20and%20sparse%20data\">Write to the team <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/credibility-and-sparse-data\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/credibility-and-sparse-data\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is credibility in actuarial work?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The weight given to a portfolio's own experience when combining it with broader data, increasing as the volume and stability of own data grow.\"}},{\"@type\":\"Question\",\"name\":\"What is B\u00fchlmann credibility?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A method that sets credibility weights from the variance within and between risk groups, balancing own experience against the collective.\"}},{\"@type\":\"Question\",\"name\":\"When is expert judgement appropriate?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"When data are absent or not relevant, provided it is elicited in a structured, documented way and tested as data arrive.\"}},{\"@type\":\"Question\",\"name\":\"Can industry share data to reduce sparsity?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Only aggregated, under GRA's written competition policy, once membership opens. 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