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OMfxviqU56msJzCLbfArWmrC28OyC2w4c1v62kg8QSQeA6QZAJIYgzkmq9qmLSfr92LtywvCnplr4qen6+dAaTrJY0vVnZPqzbLYZHyAFibY4WuvtsPdLVk75TwLqCEhQSb\/oA49KQzfeyQN4YwC\/1wFhpOMaZ6\/dBV6h34Dheic9Bua5C8SEbtpI3HrhHqdXs8SKu5LV2bA5m54kKK\/7rjmAs\/M7O5DI30pGdevjjK3aHr7Up7a6X5ob8n7a+8pCV9kWR600muSgjISfKmdXqeF6tBIL+Fti2EBxuIs+RQahKITqW2vsKnb5RjhXiG\/GUIW8WZAj7R8Tje7vIukatG141usBYkL\/1gKxw6bnSpOg2i5r0\/WU+fB7qZTeOQTuIgDG\/PB07caBglzVb7hCngiM4PoG3rT7hDAWJYrA9Sb9phAu4jnVNs40jHUBnpOKWJI39AxDhpD59phRjdvnZBelzUnuga9ZjrZArBFbg9VmkW19BVraE8pYEJ3hOXtiZ8nYGD7sfLydCi7pvUHYOb3ACX28LQP3xJy4f30SxuOuLs5LODKnw2Tx79qutoHayYeD+dM+iNiw40cOyhe2w\/q\/tQDiBm3ie9NWpWrI8nMfmI5GxmBJq3bYgrysyNM0WHyKXh9rMYB3F1wQxbw9lMvFB4xjeclYptNiJAq9RK49tcj1U8xtdpIxyT\/IW1r9kzL8\/xUEYC0n1GP7PNZRxr3pkVPpDL7RHmTSuG+Tz9J9y+If9PpbYyi\/8xy1tFYJPcU9Qf7GgmqayGd4zmyqcYEIOxpwL8+VwArvl4rBuO2ZCSEcMZM\/TMhnFlesIkUN8MqDMHaJ84xUSig2eR+ZqPxB7zW6NjfgpWn6xB74k+BQHF2WrnjFbTDiZ1OSYY6HSFClMlzdvU9lhI22OhFa+Vl3XflhksL8amzwtoWlgsgXSZMFRCylApNVoqIMYTEOON0vhosIQKH7fRSXKjBSPzrJSt2V9e4Ac+sJoV7vUKQXHL6up8G42NB0p2wb8i0P\/Pp\/Dyvv7RVuOLzoHLdfaDA3+5Fg282b\/dqPdwlrtPDmXcdH4E0lc+h4wF1At+oGAoHSwOhbQGb2R+nTEsBc2pz8LgoOlI2m0eCjphb\/J3Ldf\/YWAC+3y1ChThDErkgkCUpm6BVk5oix60iKucm5U7rjHKzLdfIaMUzqQ4tld1jmNOu7hNwfYyxiKGKOpuyvYyZhqjXLS96bgOHHVIyDCpiubLk\/v07TbhYF3DCwTtJ3fDeFprnHxnAjuQFywspyrwpqQATnoiiR01eXq4w8RURUSvqj7NNoymP02fp234MJGtSKQmeDlHkT7t2qysZ8Kn97Sf3nvkp\/fNgPHyXpfyKdImkhH64MQZqs\/n2Egrmvp+F2OlX7oJFRlC2HjyXaj40GOo+Mx9hTdUEGqzCJqhMUlv+hEk31Jl3hT2qytxEfEDrduPWri7FPBdoktx2YAn0wY\/UrdalhPYUJEi8\/xlehQ13K9Ro+j5PUqLomE+SomiYM7Rodz5ShQ1koSBCVqUe5inFVHgx9OSScRsrBVNgBV0rVCH0xmUdXWBkv38b92Th27Y\/wLbf6pBXjY\/ZccOk1m0aX3JFpoz3pJdK+MTwaxJmu5cLRlxcE1370y4P9cKwo61YA8H9qxPLm7jYCmXYwHLNvIyuH8DL6BugWSD\/9fi2qz+C3k8K4X\/P8DZPMzqlx3P7Ii\/5oTmZvmoQ5oD6888MtjuP2OT4dh0mYiJ2x9Epf3VEIG6+j+xP+5GN4ahoX8utZMFQqUPUEqRvkABzrFDrukgwIEfDWzbvDw2+9VT8uo4T5unprG7\/\/OeHlKQwejTSLed\/C6xW+f1wYhP+sLWtD\/vInOcJBvzEWhJOYY\/Y6m8x+Gi3d\/MwL7h+TQLqt6RIDiYSbfhnGHNpWZHIswF88NdphBZditCQ5ghB6E14l1YzAcyv36J5nwK1pdgY9wvq4q8Mn+9hse8Qai+3OQLY4bxHCxjE3dB0hFRQn75JvM7\/zvx39mvfNpPxg1tOqcFzrRlPywv0l3ZCeblnHVl57yYNN4+dIlmLPgKy7aZdJxi1vOvxKfym877UZJgaRI0\/k599psTLC6z81x3WXLSqm1hOpXm2uszYIuJnhcWWKDcwIZFai4f3wS3ymEtmjZPXj66FE0jIXqM656kTpAqjINJvfAop9GpqX1l7cJzvBPF6Ly+ZUB3+p6E8bj7KzJYWl127sHDRb2F6KJKednAdmWvLAdnPBV89ni8DNGaUYynH4mvxuOpHZfIp3jLU\/iNsVYrLpj4fipnoW84P1LPNBR+lCk4ottERInKwAl8P8hcmy6OpThQ152grhHN+StfjV9ZpZAU3ZkKszDonyfIEVDo3++4B8eG0COTOOExTkIci48N5cb47PYtCJME7UbMO+jWv1XothY3G6CEro05\/MydITpAl6pu5ywo7U4sTFA\/FLnqlgd9iIOpo0E6sDA9QnINr\/o47BgjzpGElL3VbuJTUbtnp8bqnKPr\/Av+9Yy\/0PygERHU8Nnnxc1MAEqrEi4JpOnBna3XHC5adsmiuysN+bSFFYN68SqS7g+aOgts8QdhwRxBCzdz5gCTvMkFfghnBEz2cGMrBumxn4Te05XmQnH1rgp2\/Jk8uQXHnx+zxw2IyWQxFQ7Ep5oDKyvJWbAZnUqHk85Am5ls8uBB39+Y3QZzoUuW577jQjrE\/UtyxK7fwpKwx4pn8rW6Xa1e3xk0AMeNrZBbpo50+siLDA5izAslUojk4KVc8\/RwS\/SBjMnMlwqomuUnrpFLdzbuLTsKFvDjEkMq4e2u81Qz3bx8sapyIg5XRfuDCnqukB79rQe6L6v2U58l4dt6oD0RNwbf0eXynpHQVtncB+CNcnSdXrqhl3J+rDMncjVdSV0546o9zRiLbIOfAFy+fBOvvnEYEF\/hlRfegUMfbQovX56cp2\/jCFYKsU3DPjdz4bgyODgn7VxIngyS6y+A5Msg4ZWbCymQQfKjBZBCGSRMUXMhbWWQtktWPJJBipas+E4GabdkxR1bSlD2kjV3nAniXAJLTujeknV3pKTu+EtW3pESuxOYrj0vrc10k3e2uNWb7yd\/75FHzBGLThPSlZ0kalo5xOufROr0M87LLC\/zFgoJq2qYXhOYdvgisz\/6W+aE\/CcJ2\/9TH+Avf0ZyvtPpvrFgSSONQz4mljGtziAvmVyB6WSuwNQsVyAN+Rvl\/iwWltAnNZLDMi34gM3ZJIU98kqgEip53M2TOugqpYOuUj4JlkKQnEKfz9SvxN7wOko+FnElN7lzEvvTabvuWCyjdeHD4rq\/92V7IjVzPnlr\/iIjnaiRKEInsdv0jZGrqmqZn8ERzieK0GYrvk6L0QJuxoHX5nOe1ARpnCdzFbxCqymq1iiXy\/B2z5ozlKqkRSlIIUhRhnZ5BpKxEPSsGGxNlkWuoo+tSMigQVvCZv4VHX\/I0rr6n\/9j9X\/A92uz+heESv3xdJhKGRN1panuz+ihrEj5qAtO7bKschK2z81XLIWmSuqA9FlSq82szi\/Vg9rRzOWwgUHn+xVeYfoQ2xAHCtnGKPPMdmqHHmuA0nR8eQJtAZreXZA2IaFknejnILnIQrnj0c+B+\/z75wign4PsQ3e5QoNg0\/+LEtWtDwoDsKyLE\/Zdhr22f7a36OfAW5r3z3aCfvADYB2viND3bjTtA8Svy2TaUW6tHPyPfK28FP2Ia4VxthVrZW\/wj9kq9S9L1ycD6EeyPpmPfu5KbqpcMirB6f+\/Vsx4Y3GZc4zTCfCAuIA\/yjHKcSObK5ztm1O46FE3v8zirLEoz1fmAHbZtEhOlJzoo8o3KNX92fan3Mdnodi7KDK50fi0TTaLH84VIfpXcm6Ui\/0opXiyDrsMttxBj\/Pc6lbPCeQ+s9HM5cOupgI2qrULxHxtktWbt1fYjFhOZELOGhdh+Tj+TTMVhc++FOJFyzTced9DkvLLVwUEyXkPnXOLcWWcVaNZPwKTY0vBjGbB7pXI6k3c1YYzWrhJZVmnbbbO4yx4yCmZ1i28kGX+dDaw3sM14GbpmfF2ZQDlxGTRF2xYzo4QUtQXUFEESvzLKFJMXTukPW\/F2fJKjnE2SOTGPSchm+aUG2PcQ5wydGXPBjHofnRVUPX9qxYisfrQ6aKjw5BXgqZM8hf+hpZno9+7LImWa8\/9kmhos8QCsqrzNl91vmlB3d6JwNc0f2XKIVMnlMq2pQbfs0l9XtX5yI5WKHd9451g\/QcNUpXFR7cgwhDRzCEMvZTxR0YO\/fT2HpdO9EO2l+OxAjeRN0gDaJeP8BVnHBUyix7mlmo0HU\/ub23gUZ2aOSLoRdCR1QUT6Tz0MJjUYEWrLxm5ZBCW8vYn0Hz6SovSyBt59buQe2S9pDQsJmskeQ\/I9zaI9b3cjEb3Lr7FzJuzgN+G+IDZG04FViiNgdWDpeLQZIYY+0Bl3LEXw\/xKJHJae\/FlCXuVcu0Co6EKHkqoRD4Ok6qU2i349u3ulLA6HL7+6cYW3Sb\/EcvkCug8Jn26Hr\/V76sBx65wPGXp70vJ\/4yxe93wLjlghHTha9HzxZdnwZVUIXPoSjj4D8HxL5g\/hizdo3iXdO8hJOao9martxjGqj2NFI2+Fkn275CC8hqdwpfbx1A0LUPsy4Pa9OinFPrOgD4+9A3KnRnAZwTmGdmY\/Zn6el29NchmMU4HyxVjdfPZ0EaxSRQA55lnJBm459ey44NRu8ve2Qc4nbGbT7PtLpnZ9B3wXQYYJtW2aJ6YB4vKM88TiN6sOHuoifT0801oQ97V2z3MW8jY2FkXtu7dccw\/VWIwKXFv2H+68P0SsyMFeDqno21sP0bpLtTLTEe1zxYmzUrey3Wn7rEVH0VK6J+pHDsWYEQVqL8JIqEJNc5hpqqkmztbns72J+qMOi3Ry+r1+Ncuq9fLlUB5DHuAba8IDTK6wqder10xcNzFcMYFpBSj9eZ+cHwo9ZrF2Fn9eWU5B62jh+GK43F\/l+uLd09MN4DZVGmaAG2eHOYZMUTyZLImC0aNn76UA+\/\/NWs6HjUs5ZOFQ26BeKGRsW\/lst5kP8063qXddZT5rO\/7MgaWcoHY8BpED7dxS4maFryjuxeay0luONXtfDX+0cnP54z3iIzbZi4dlMgkT9Fs7M7AeS\/8UJUJjHa7uWf7tIHIZq1D9o9lBnFjX2dz0UPmvM7iHvrL1pwTcQwd28nVYEAunInOSf4iyOCb6d58pPF0dyLGq3lKwJt8Yb1+nNn7MZ9OCvsr7VROsxnJ6pJFtAJ3vIQd19Nz+2xJ+CZhv8LXTD\/Fu2jOPAxjCvp7ZOw0hee2Hae2Y28DJlTaB9jMtBPElxQa13PTey20v9gLuuXEInPGf8w93sNx7QlC9hGlht+BYXjLjCssJ1tiYfgqLa2wlMGis\/EOnKh73sfjRX0V0Si97JqVdjEZIoe6T4pTsvY7G\/\/R3QXVfXl5odoeVT6IpiAitODZK4NjhFMC+Udd3unwGGxdQKaz9WYa6nKgQ1rZM2heOiOv7uyjLTEb4op9zt8\/5eWqqY+x\/L2VF3BRP3wY0IHCYPW\/BoWFHjUK8efxXjU\/fQipcMUFsQzHny0pnsmnq1y6Gfozc4J8Hc2e4IBT1lflfqBYTn8DY\/+GbgA2+BeS0nN8h4zfT9Jino3fCvHAFVmgDfX7PEs\/yq56mxZWcI7EjmEtKEb90CgrUfC7y1UaT72\/5eFIUbB560TMx6ppUv1k849wAG\/+8YQT97Ha392S71IVqLNW0mIDTfFosr9Mk1m+CDxD6WYlY+d3zU5ufSFFyH6CsyaVRLascBrZoTDee\/exmqSuiiIGNe2WRq7ubewO\/hPw2u97SWsY\/Y5vxzqMvp3yFmLWDYdJo2W477qeobwCDISHEkjQlLi+Yx4Tw1F5nQfYeYnPEMz1p6qkoaA+q62OR1ov7jriDMOcU9p9Obn1eRkErEzTXwsdV\/G1bavyBi4XCGpQJp0vRrddmaTPAZ96XOLspjq9HyM9KIUHThxwD3367i6Jg\/mKrH7vl2amz5wmN7dkKOLGfKlh08B0dGN+7EX21OAzkkPjN8Xc0Ja6iujX+brMRN+vwl5USK32KLUO6czdOy0YIgKCjIyjERaIgqYjbh49A5Nz+sfP8S+YdQf6FZ8P\/c3kL7pd5gblLsV+6PMUdAd3hRKjc6zoMGw1fUJzDhS7Z9XpMbtNuyD7aJe\/ChYwaWH67adlOSXj1NdSPYTcavqY8ahc7NNGHykCK9fV2Xr4dDCdEFKDBH7TLiRlCOSnGupyaaEh8vI4JNSiUnSR0dc3fhRb6u0snwESUb7N+0zibKjEH6g4Czrg+YyRoDkhJjNm\/BpTfbFVzEFzmqxibn\/23OlC5pqU0GS+JMbzrn3MgdqfkKiHHqCAz82srgILwDGjyxIB97Crcrz36AAuyuUtgpMlFTaQVwZSJknbvokLgUqZAibH0aIQxqnyIcZBT0QBZN8ENQSo6hoxM5Hz9mn1hF7CgJ8Y5xUsQyDlnPzlPib0U3L8E\/0co4ow3K9wg+g09bdr0+bZBzoBsm74+xV2nThCoUVadHjBMh11lUegw7kiEVzQr4+e64TO9lg7ooS4zWL4pJQ80DAmmY6Wn0aqde6\/JRRJQkKHPxbfzTEXCCDNQTFa9pO8Tgq4At09wOVdf4Ug2Ps\/owL3OOXhn\/GOJttachnGDw6LIr80cLW1f1tF9m8Iiyj4beXav226O+l\/RpIJM9rW\/o293noYUEogCAKFgkayxasDDseXL6BE0RpLwxPll0wsdPmVk4lVpC8bd5uVDU\/MN6FLkKcZnsiibP2qfgcMN2fYJGfFsH2DsGSPiRg0ENciGyKGRgMOiYqKukuux+WsY3A3iaBRO8DrRkFRTTeU3KWvrzZlsLJNIH0t8tXvqyK\/3R0EIoX9FWwMX0zzVzPBAydTlUgf5nTgdHSgu9Ti69J1Sy1EHApV\/YIfMyatO5B2jD+240jjBSbBj37yw9NyMkRhYmmNvzWTCpj3MhmXmeQitjLQtBuchX3NZWw2HpCoEBvj11lr3xSvwkB1QVMmeo8Wg8faG6fWh31YLlwuDsrCeF4eykOs4jiDz9w0G3y11J9vu3fn5Rma+Co83gxueXmCdd7KeJRQaJRHRR+bpcPx9WbEUSkN6mAgPOmW4PVm6BFLf0w68ZeWSq9nQzrt4t9QD1jnyX7VghgVMEcNjZCL6761XoHbXQCyqmpvErGBBMDzYpKpXPArrrlUIaCPNdUjyZo8LsBHdW2F1OZciqLZwQ+aAfT5n+y7hvvylFZNk7P16fHArIrziHCYBWMTjYkZfCcJB\/KiO1ddiorgW+H\/hHgU3ffnlkKa\/evBNC8LW\/pKE2H5uzPvAkoWmERD9BDE+eE4\/CkwOKQQNxVQfrydYK00KflbxX3kImmdSl8n9cSRaLt8febBnuTrjyZe5ZQ5rUzAOMP2VKUNr3T12aumehXoCqQ2U0AXJsdQD43ygyy1UDM6Iqnxwlqh7UcaBjqELVQWwQxllG9gCd6Ozyi4JCfGIu1WM5XyRlChreIMLx6ycJqKAM5MiZ4W4pTilmYmOLZdQvtIMDMQSocFQYlNDDLiKPME8wYET56SGeVlPdAqNtGrsQptpkH3WNdVAW+mMrI3w8LRD3Cs5J5iMkLu8839Au8pXylXzuCdwzS7HDzTQEdb8oF6jn9Fe\/+\/f7KQzZz5rF0XVNtJ+LCbAYj0sRZfktYGF7ikhxyhupIKWpvFhIZYH2WEoHfNxqCnaIBQ7jv1\/awGp2N1H0bH6tH4ENq6F6uOQinnA07nn4cepp1lOFFkJ+wC7\/JutE53289caeZozdCgyVPIXUvIWJlwVYFlO3RJq7ggZEZKQFmVM3knkwocualGc6saiV64\/4utYROmd5lLttmBuOnmuJiT4Q+jtDezb7KEapSAa5Zdg8pM6imUGEpoNhGTTSRZXgnVKgsjEzK5iWT3nHxHEcGTV5yodPg2ue3zFUpYkZ\/ztpElV6DWdhsmJzbzvI3\/67qKyl4I7qxkCvaQSME+9OpvnylsXgYFaAHRCr9RVqBeK+fBKJfySmp0fu75qQiosYitSae26KpkTF++dWn4FolAOqCGJ+T4\/oLjcXltzwVyBS0qbpatKCn+uYBz6TKS9rVlQfIy+thRSVDtHw8CufodweVzh00RMcoZpc0NixQ5rIfwiVm1+jptt7L7w5Owv0sN2\/ZjAq\/LTVtiJ5Lrm+GUEi7HKkTFm4yx6rwjNtOLOVGFUpVwNrTtyfF+l6RUtj\/vZCn\/mXpQ+kyinNF4YkpMoqOJd07ejVrAGBQoJmmqKiE+B9trwwmm\/LGokhtJ3Rj27kebpTt7wTlS1roSOtnLZGLOfgKXukrn3QE7i8bBtzVGJrguM1aoyrSmsI+iObE3FviqJGTZYPbCWOmD0J6W4BDELioDW843Bq8NmcYYJLwfSiym7gpE8L4UvP9jMWbuj8m+JKj2xWpej6qin3SCkWk82uIuzykMATlLSYRGI+Laov0letkofZeYKn\/YsjtsXA2apjJol7drDBkbpdGJPF4MH3DXWjyM0kP1ku+YW7sXwunqRJIyV7jm6ztiEHiNBu41gfMFXJB7z5SCbMBN8Kw6iQFVLYF1iwr2TuU8yl6sEh7lal73zHSsN9AmJ81Yb6AuJwfDDxWjtb+CG6N6tQuMCJR+FygsHANZ8oTiSVDNyxd3PcPyi7BnU8lPQ2WuEUTOOwpOQbBTcl7CdCZ4SijcGSmJifoErDY+3esMLuaBq0gqeQCrXn1GSObJy0b5RhEXVjOLlnbkBoYKxtETbD\/UEqLlRlKE+1BTSuZNib9SCm2bH5cwPf3Ip6pdspz8hRY3upLF4bEbCG5cpwYCHcKgTPleqK2PuKOakwo0QoCmtJIePcDfKyT\/4wGwrCCAJRxGCMSUxPf5k4W9qbHI8otrh9eTjlN0XBEX\/OIDEKnzV5TwQzHrN9CKk3Y\/9zjhpFEJEl7w0US3\/q1CohZuNkDCauej4UyioS6gyBBj3sKbuHFdCV11woRpIC41wOS3ftAYR3A9CrNgQHFsSVxI9QDoZgUKUyAfRd0zvja32XzxCEvpN9GwCGzpeL0JM+7uMvg5+0bgrFN+k5Ajkru4TWEADqWbuZnsZleym4OVBmN8\/hoySTk3026XE\/i7nAoEtuVFprtYO6JrOKQTGg5pf44k6+vpuGVWVFVqm84\/NADmGAJzXQNgxsukB\/amZMYT+8ml2e5WPwbFFeXjyE456ZcLdF8O5T6YwR\/d2cyiquO8fZD44Or2efMG2hM\/HZAkZjsCvzh328P3m+lZ5fISYeOstdA7x9gvT87TN0E4dGf0dyX9vRn9PbH\/aU5\/X9I\/WOuos25Pq4vko8puhDmF0lcrlPOVq1HX1aoQG3MdQ1UKa0IlGr0GNXkrIL7ksuIjn3ghPlqwgK+AYlf6DOOGxxM7tpDzgb87kfg9utMViIfIuW\/9WFRKk7xElUv6oak637rcEjlJBNP1Y5OP1PAVgmLlSRzNkJZ3blDG1dOsRCw8ep+94M57TYOZyO8hH5HEiS97dSny9unbTysdFTCxoFu+Ns\/S2hvyWehLo01tQXpSHRN8\/fJELpaZ4p0a\/3CTeCbTsfgqlvOCsocCDPZvd+OB3li1tTpYdoaH0uyRia+CJvx10WBfiTNH024W98bh7WxF5zlUckiqa4mDNiB29yvfi9f7poJcp3qgeZnU8IzvMBBgs0sfVMqDu++Rhd25JlevMu+9ZS435rOXXCP979bTqluTT2ghNilM8jMorAIC7Bv2d1hX64Myty9TEkDIpPmT44I7bYUNI0O5C+86iu7jASorfPZy68Tm\/bMTu4kfHDoxeP\/sev52C7q\/vf2zm4Ug9rq\/\/f1zkO4SGHZ\/B\/tnECVpFpG\/T87+2YvdxEm7v939c5j4nut0f3vo\/QC4HfyTv3+G9nbr9e8H++csi90tJH8nCN4u2sIOv8TdP6e7YOd24yfe\/tnZ7eDW7v7298\/JLkL8l\/wd7J\/TIHCibvwk3D\/v7CiAO\/J3bNv7Zzd2Yxf2DWhFMh8Gbt+AlgREIEq6BsfePwdbz\/azvsHZP\/uxH2+jvsHdP3tgm4EeqGvvn3eJP8Jwnf1z5AURSPsGd\/+8TaMk6xBvINg\/21t7Z3ddkIll\/+xEDnDg2OLunx3bcZ1+eZBVev8cxKEX9mOTW61nL\/QS3ydtxxq0+2fHdUInPoxa\/P7ZB1EYpz34F4hecj2vJ4YTSOG+Psbgk73BP5+37rp7G28+\/MwNgk3\/L4qR6N44gaLaP9uxndgdali328vqy\/Y68n6CKQ869H5SFh5Vm710qw73evtpTjje\/e21An2v9g5v5tLUfWTiVTMCJEzttR8OWf3Ghwm4os3cPSvhkXpU522egGJYjawaH6K\/ugf9mcIyva51w7Y2oGx6JJGIsE+ucZ5YMfx7DutP9mcXf3YHnRG9MEqUxT\/3nuWIgx4k7gzyG00FV8ao6OOG9NnKcParL0+YQPuc6hT3S2O4y8KR+4EwiWE0cj\/E7VJ\/5H4uCNM4GrmfnXpZFI7cL0vhrueWiPtlcZyF6cj9Mi+K\/WjkfukucNxs5H4gdH03HrmfC7ZRGo7cD8Zh5PkU94tB1nNrxP1gChya+8FoGwN\/5H6hG9g9PMz9YIR+KO4HEuhDn+J+AUiiJKK4X5rFIwzE\/UAA0tSjuF8Q7qJ4R3G\/GIY+YLhfEroAUNzPi\/3dzqe4H4SZmwU098uSLMlihvtlbub2PLPjflG8C4DDcr8kTJIeQcL9oANDyHK\/MANhPy3C\/dIABpkrcD832njRJnA39udAx\/+2AcP\/yBQEuYDkSW0SRKvIKft4aif1x46gkZzzBz3\/Qc\/\/EPQ8KWauujj9L08NsrpaVn9AKm22TD9X6Oca9fOEfp5RP1\/o5xv1C4R+gUm\/k7AuJ6N1OQnrcjJal5OwLiejdTkJ63IyWpeTsC4no3VJhHVJjNYlEdYlMVqXRFiXxGhdEmFdEqN1SYR1SczWJRT6hSb9YtvmO8a2bdbTEXs6Zj1dsafR14gdEVvHDFtHxNYxw9YRsXXMsHVFbF0zbF0RW9cMW1fE1jXC9lI1fM9L1Zj0RMoJ31XlAUr3LeGR76pw46J7ImWG74oVHIO+IEn4ruhu0mjUF3HQF8OewqdRegTSnZG6yXdWekXTnZEQwXfGgoXZx22RWYn\/ugr3G9R7TqkcxPvFfKzK+BIqPFOTt3XwbidJgvmoUlLw0CgQRbFaGP+8PDJpd7sqTs7KWfko48t0b3LrMVprJfrvqLEz2i\/OJKPL8+HKo8MC3mrZO2jRsVZ2cprGu7nGA9Y+DhmwTdKWTjhyMwGxynFJVvpGGR8xVowKL9Kg9mnwcTsjNF4gPz5kHpFEP3tcWJuiEc9l\/c2Zwk\/GDufBRR2CMG1kkWdy7GKsuAoMxvdInZFIls5jeDQZJt+\/ofw6RjdLUtpWg31E1gflCKQIFXy\/gDKlq1DJgyjwZpw9i6\/IL753IPDpcuf+4CfOuAnRScpF789HONuiLQ7Lq6yS1FA2KiQBOQkokk+4WOvvaPesx\/LN1LZx7bF0LXO75WN2w0VePXo\/qSfZFzRTRCYM6EaSWm9ijgx1+TeD\/exJikAa1mxjJ6XMS8G9ujwHck\/5JqmP+0GbMygKaXXZ5RFE2Yv1csmt19uM4zg0OI4d6XHsTp3Gi1OQdfhfb3fGz3ZwHhhAe\/ec0s7ZpKpGJsoPpdRdRCgZKBoZGKUyQoNJ8\/rL0\/WCouaEi+z\/cIN\/cv\/Dtv83+4leJJRA5xXW+L6m35yO6gjhh0urt3JywH9+wICXnN5WxmmC9BQ3NyCuw+Vr3h9tIXWyhRPRmjIe3lOgosIqPeCQsKGFX57OEHld5RvzHtUL6iAPoFQTqAAKJWSZOTrRy6fGV2voEgSqubPHuv3U8OqNJcAi2v4UMLXeL\/kuWSYAo+qzLI7axUICX3+F8r6JSGof21jp1A00W3Mewr5N9OaoT93A5+BRCroEseVKrSNPXinTYScOTR1utOLqShTXZaekJCCVUmmFoFSqBHEnJmkvvpVjE5HrZig2DR3u0Xd8kfEqKaN5PXaGpfMXUtbqm+jbT5J+mHlC8zDBe95MwXSXAkUu7KJr\/fIsdfdIPwxi2AVZjRktjRlCrSfAUr4Z\/TYxhvmqhzknk+\/jVjAv4YCVJHgm2TB8ZTJUjtOD5UrzLJw6KQ80p0HClQbZGsdrqoZ5+\/LUnvJyHGOMqDEFh6rXcd9XWDyTQDAebkxs18ZwQ3mO22iOiUmCAPnzVLX919jM7z2W6mQiE0xX4nXmUtiRIeDvVzAL7nbGys1gUcbM5AzqF+zpivNmTW5YdKGzXrw9ztMcaynDoqWZC8jL1uqkTIKjYUQ2OnPnWt+YmaVIjp\/62vSi6TAxJdyLYrzGX88IRdfmr+73trl5S8CU36ubuV0pJnEziYA3XcYEFEV1bVUkydhe\/pForqxaaMoF7sDTM+YhKIIAX2vODzY25n61huxNidlUTK2BTE7VaJEsiVVlKaNa+UJI44dNqTnNwdFcUp8VMi\/MChaF4lM4j\/8Ur+CxJ4e64FfH4yz4Csu2MVD8OET7uMaXwR36NietDw\/z1K33Mhbc9RaKm3Nfc7MAWnKtp8A9lFEzg1KnwaJBTdlNXb0Zr0pS3fF98pE+KIuZ6Vasq6aRS24T07+LyfTDkaLTagn3sTIPyvrwMKln2ZdC8WCGui1hupJ8CgEt+IezBu73XKvQX72VRw\/gz1jc42IK7nortif5eJsF4GQMqyMFIw5jrJ4QoMJdg2mikcYXP7bp5y2N7UN3C3NT5pWlJ16an2UquGuqKacoLGOR0CgVYIwPFPheietNPPgOj7V1kqE6Yn4bDRZcigkSuGY1VdaujdK8mCve1zIR5yrNMGObZZi5T3jE5+r05u6NpZvZ0KScp9ZaHSiFzmQG+GJ2+Ips3g0Md+VJvK9UCTfQzSHJfSHUV+PdDidu\/2lflM4X7y8v8COrwRk2A2K3rK7OdIaRtqKqC02HllgFPMIyXZqtHPkxYk8paaVBwbmIM4fL77l1uFpsohp32nGiu4frnKKU9vcX+LHg5p72VnSZcn827aU04ZJEex+Nf5nd8hO\/IQp53gFR4hpFuU7Ks\/vwQDvPQcM7Q6H33RdmjhGWxE3wUsOmgQrXlMn7SDN4X3PGDK2kpK+5QEusX5tLOxpKil95Up86SgQlkzFzzEBTIpLPCVxgf5XxNb8NlCsi4Gj2CgMQnRQMQDY7CPre6BWYTikTwgbhsLFn4ELcLnq\/l4ha6Ejq92IOus7LIw0b+yIOX9HWe9Ww\/pXI6sjsA35ZzDGLq3fBP2PmYLMIAGcZuc02zvwE9zaSsYj2GaBcSXyf+ihJVRTg0kBcjwz\/JvFhuBuVVXu60QlW8uTlo0\/gPzjvOrwTO\/YcsQ0S4+uDLoQjVp1SSS5mYJfXWd5m\/eS7pGjXs\/4+oE8AJXS50ajgs1s\/ruBHbHSEP+jENlasOWznHa8TMH5G7UWlpMQjIKkjMbhyCQdCV9WCYkeiH1kvr2ne6isbd28wfvm0thoYUC0+8Zuqbr88gSaBZYpZPTdDelwhJ5QOEWceFimcRGMGSKr6Vu8837Maw2JCckdU3in2nKdpAX8s2OqzzpAJjWFtOnAMRt2uNyFFzImxNaH89Gf5zy9NRTfECS27i2GHJUtFhIwZNkMFEF0chKwrfSu8KCXeBFyTi2pZP+oyRI4PbeJZihKXDXExhnxWRVyH5wRS5DuZlw1sey\/OlVk4r3S8CeNgGJqCuf\/ksox266wlPTEL14e\/klMrVI1Wg\/JlQumj+AvyaD48SG5W2LPeNOqnoLiwhpJIKhGYKaT80WBpFIkzOtTNisCN2Ro\/eyjlalcUzuOLwglivXb8r3+tqwJ29sYRmU6kabT9V3T\/r38aT91td+BuZx26TImieWN33HPYmUwuRdtYGWAwFSItdeHdCswefvYfBGXhLiVOgXra53yYFzqlhkjyOcyLojL58N2ZUBXfdLYo3kLCsoLQmBUoEE6LuRscbW\/s9M8FRJjt6rT4muavc\/ZxWqzSlt22OPXr9i5hWcen0ajpTWI5384v+PkPto\/a\/KKMX456o0hfX5A+JANyJA2xIG7ElFsU4oB\/Qeg\/vXzcxY0XzLxgbfO+zK6mdrdvYr7RKED5ZXXH9iOFVYU7Ht14c7cf7tVOWOwPs69ldAMZm4fnszo8QKqO45obBfxztufqMmAZdjFdKvusYWRMLdv1VL3SYdtQ5IRERCq6i+TCOSRFfrFQpY1OZ\/kU2L\/NDIFNwEVzrzneYbqyO0xrrHoq2opNE7Uo8fuKKu0yOsfMK1EEo1eZS9i0H99uD9wvInRp+doZQcbaSNH7kn1nL6s\/TSp5fPmJg0Gi7xver29nq0ogKPfPNkA\/h76VpKD0QvQzNuYtygKNfoY2kijy2U7Rz9CKC+rtn70ssnH6UNKK+HOXUDseGrvMz+zwKLvzcwbQz9CW9JkrvTTK0pBqr2FyrVEaztjHqbv7dlyTCqUM97MdDaes0KffP4fxNozc4cF3y94\/J3608wDV5uyfYZgkUUa1ocypHkipqX23vP3zNkudZEu1oQyqGYA7ui\/6HsLQNchLNLqT+iS\/KtWM0qIm2yzZsc3u\/tnPYg+nA6WaUQr13S7OMrbZR7lYYUQ1N2X1loGiQMN6sedvbfGRs38OgyCIJY\/c\/fPOj7w0Fh95++fEi8PMFR+hlK0Z3NFoVDlGIQIBcG222UGfY8dQG2pGydAzAKl1ws0eolfgxS7b7O+fEbnAccgTBC0a0o8d17fZZgelYne3vsM2o8TycQBwHniqGWe4Bb4fss0+TowbAnYB8EL7QbBN2Ga0yPHOS3y2GdNY4sOQbfb2z2kIo2zLNqMh8T9jc1vVEI2ZRsALANeOctKCEHgu147T2Xs2telJO870v023Ptfu759DPw2TcSc0DV7cMIs9CJhWRMhpQNM3akUjhm7ieUyrh\/DzQxq\/hiysG\/nBuILHAjQNyUTr2BvyP\/tzFK0ZBlW9sYnp\/YB6\/vIhsJ8T+DvKbp+gn5FvdAl3UbZ\/ilGiY6HB3xa4gMKse+Dsn7eeF24z\/oGLiDtww4B\/gIgq2AE\/4h+Qb+yA8cErPGJCBj5wE6YVEVUWZX7AtOKdE2chYFpR3ucgtrcO0+rvn3cZtKnRjpcLnmfKjIZanf1zgEZjW1F1BzeOKJpBrR7mWxmwmVY0Wgy9dGwFsO7mZntsa0+7gGlFW9RlZoxa0WLaIApZCHgl0x2FQ3olZOsDL\/O4ZjSeHfq+zzajD2jv7JB720M1K2I74prRkFGaxNRigI5svCCwmVa8nAFNZajVRYws8hMWgrd\/jncAUmwCtSI+G6Ywozknkp7JgrpuxLXjJd369HbD7S5mH76XcO1oWf04C3j4eGGhRy1sUlTXFI3rZh4Nh7Sj2hhJ4AWQa0eE6m3jKOLa0enmgh21iqQdpRYPUwCpcU8F4fEuoMgdtaIxQQgowkatLj6qdxTPRq3oUHfTkCJh1Er4O6BIDSmdZHERxbPN6GvCHaBIGzfj7OupnwK2Ga1smHoRZJtJJRSbWvAsJ+w9y9IwDNlmlJk9AzZFsbgZZRDPQuClbDOmWC\/yONj4W+7iXUxLVFUNERP0s9gOM+pBc6qvMaquwpzLidWAV1CWYP+cpsmOEjESlJi8afbPsZ9GO58W8mBLhM\/QjWliT6ykri6IN8YRdVgk1rWOkQCbZdvMpaW8c9WAPNk\/Q28HEuqzWnkC989JlkaQbo0But7eP0cwiugjukX+cvjbZmHMnH\/4AZJRdrEfRPwDF3Me2xNAYZYUSh7gBfdiH9ILfoWFlVVNkxdEGNoC\/ml5TQoIaslE8ePTR1pXMuEYPcS7VrKm+OFbjnWADA3LP4vzCn9tjzrxukdHWEm+BhmurWpwhFhEhfRmJY+r9oTk+y3YstMEWG8I0iD0R2K8wOpSwI5nA4pndw8cIlP6wgPE0+yI3krdA3JW2FvhATku7Ng3qyLCq1ZplEZpwqlWnHKDVStegiOqFXCAAwCvWvlxlCQhp1rFqNAQr1pxULFq5URO5MSiauXGYRLb5qpVGjlxaEtUqwjsQuCxqlWUBI7NqVZpFHuUCERUKxB7ccKqVgGISUUFSrWynTCkTo5eteKG7lWrHUwAdATVyge7KPUE1cp1wpAar1et\/NgDOyioVlvgZMCVqlbpLokyuWoV+7sYylWrKA2zBEpVqxD4MXXE0KqVH3h2JKpWcbQNKM2lV63SbeyHomq1S5JgB0TVKttBvtlHCmuQRomgWmUp9MBWUK2yLdhR+65XrSAS9DNBtYojF3iJoFoFmRNSR2+vWiW7NIGZoFrtvDiBrqBaBfEuSDJBtXKzcMtpu3iWsceoLb1qxVH0oFpxVD2oVjCBYZqIqhVH3YNqxVF4p1pxtNmpVjs3CVLIq1YQZk7m86pV5oMgElSr2HUiyjBDqVZuYG\/6f\/XaleNy2hVMYUrtAaJdJbtkR32BTrvaOltnK9Ou3NhJfCDRrgIk4UGJdgUi3wsyiXYFk4imK0q7iqEXAV67ylwIk5jXrpIwTSghrdeukjgOd7x25fkR9DNeu7LTgLY0ddoVBJlL83WiXYEwdSPAa1dBBJIQ8NqV60V2GPPaleP6kDqrO+0qizMvDXjtKnOTlBK9e+0q20X+jteu4jC0XcBrV9vI85xQ0K6yEO5SR9CuYJgEu62oXaW7KEgF7QrYYejtBO0q9L3EyXjtCmYwzQJeu0r8OKX2Yadd7ZwoSmxeuwqywN4lvHbl+a4TZKJ2lWVZmCSidpWlaRZFonaVRbFHLeqgXUFvC92dqF3Fth\/SB3evXWV+FlA2oUG7QpIHdeoN2hWSE5JA1K6ieOfTOlCvXYV2uNtuBe3Kt53A57UrLw7cKBa0K2fnUCdFp10FMNmFrkS72rqiduUmHqRA9NqVl22zOBC0qyhLHMpoOmhXdhJSM+m1q8QPPC8Wtasw8anDpteuYBbRNpBeu0p3Ab0Zeu0K2IwhqdeugoQxOo3alROHsQdE7QqEEQh9mXbFqSCDdrVLEocRpQftysuibJcJ2hUMd5lti9pVGgSQae+1qyTZ7qiTvtOu4A7CzJNoV0mUuLEv0a52WZwkMu0qc1N3C+ZoV9xXGLUr4LmuM6ldhdAHnj+pXQkTpbUr2966sTuhXQlrSmlXtr2DW0+qXUVJmHqOVLsSvgarXW3jMIuntKsd\/kfQrqCfJkkq0a6yNLMpDjJqV1mY7HaxRLuC0Y4+eEftKgkC4MUS7Qrpiq73uLp3f2hgf2hgf2hgf2hgf2hgf2hgf2hgf2hgf2hgf2hgf2hgf2hgf2hgGg1swmPy7Xyz0Psopdse54PBf572SQHOl08Bykew2Yavp80utFG5LklJPoPgn7fzPrsWRZPUEJa3bgjHtl9Pgxut9dFVTZgKnkxA+Qqa9Y+\/ID9OkJewXiHwn6jhd6hu2JSjJTNV5DaOprvB9chIQTJr5WC\/\/fXqzysXlSfzL+\/r9fTKobxsRygrUti7lBL00WTXoruwdJ69ZrtmYsYdMcCwe3wCeY1cZrXpFmRd2uqanLpii33WgC569liDmC3QdcjRliOvkl+hZmX4EmFCTKeNf5CzPhc3qQHc5bQ\/k3jasUCUUeSvACStwVGAgeYf5+VxGgghR5nzOJ4c8smWFQbBf7BBFNNjlFWT1PmllY1CwlAGCkNUvSYhKshrWR6F5PO++uW7hd30G5hUZQrqj+mlH9arhiD9GAJ3eUxN6sEi\/oTKtGwmH9cgVzwlSbWa6RcueTn9sM0v0w9PednepnYm1trWB+sNxi95a6HX0rq6WFleIAtPXFzrTw7mkvyjFf1swqFc3KEWibkRgoKIp\/q4m2wcD4GKDjHoksdr+Re\/1PlZ+b07AvfSn0riA1WdqxR+eXLTkaT68TcL+uL8dY0hMXJwPAEHUwyORRVDvvMN87+8wNHmhPUvWQ0vtaqy+FiIStfbdHd2y6f68FRqIOFkmxFcNgw2q6B032uaL9oHOn5YVaiBDYoRqlDJilANxl5pdA8JfBr3po1yozE9ke14vRHOPuaJI3uiWY65a0gqmamCHoPDGE9EFrbL\/UaEDCQS9izAs9HpwoxMgpfSvIadiEEC92bwLv1OwXP48lRdYDmSO5kYE0ilWQXmj6602f1FQERicnx4JgSCI3myqj7vV9fLBdYJaODiw3m6VpxRSWt5ZTiqrJ3bhxcPBePmi5s65KX15JhC48z6hrZ90E+1n4vimMOZ4fgINBx1einQsrAAmSrFVPSYa7PpKiXRZL40NrN8789iKz6Kh3X\/TKwBIluQxRGWw1dIiqqBsxiIhbKj3AQ1DjU0SV0VhRXDE3jN0bQ61Y0p6DxGR5PXY1D3K4xqVk2PjtbxwuaLshWJwPoeq99ZADMVLQ10joz7PeNzAZkTFVyk+18ZlIl5\/weszQKmcX1KJmqa7BC8\/Mz+aVpQt\/Kq1l2KkGVFreX56BZWr+4nb1QvXgeD0VanNYQ84VPYKRQV0CKjo+HbuL6L6cvVZd77dfWGisc\/JPmegpmeYPIyJLeko2vpKqP9VlMk+zRXhia5any0mmudgQSy5V8mEpOhlGObMfMm064hHS79LqsZkyWhAU9wfu3RMGVAYcRLatYarDFz4dlGlz3BzKbXwSnBGd7E05pPOmGa3nxiVbukxpKhNUITO66vHddq6uQm8moxh8YiZt1WFdKsRkEBcWQkAnuhamPhbnPl++pyM5SQEP\/\/oeU1MlMnSefsjPscnQx8Zm\/R0KgfTBq2r9D36j5vJ+YudJoMebLoCaYhZzpKdFEqf60muhQhYw2gQ0SW3FSCW7daFuLKGD8rGFU5bbJvefF1jucqmfucab0Cpty258\/NSGeLSUrY7frIXCU9yx\/tpSDNqzEVo2k6784KqlTPfUo9r6nPJUkhImrhk8zApTUZr0ts1JMDvi\/prksidEEiXuAIYrx6wzfwwmcV6hUsdyL\/ijeRfHrJBkbynHkCEkF1ltVX6AnV5eoteO5k9mZzAVp5zLHydU\/\/oXB8mcvXWNw1Ea+XmpnRAOSoh+8XUKajBLUx6MMJXTclEoelCgJehDRvUMbNVD5G\/3SsTIqCtK9FK3xI9TArVGR+UoCm5djBgNQlz1ULqg083ozYgsgCXEXiMsVNJJXCTIkWUkvu34BD4jX6nA0N95tnvN\/uLApP22okluYQWZqNbzHJ0jEEE9AEY7LwsoTuD9s23RhmO5UxdOl0If28fv5u7UcyVMCTU375ydp3WjEyuigveKK03BtwjY2pDn+q+YypuE9EefgVl6gPMNqhz9JIOON+5awcKqUgd7bbo9jzrhJ7lGZQNVa3sVLOzISCW5oHRqxdWpmG0oCbP1g0QZQiu0Mz5oJ4W3XyidzGMmVHWnotfsovcnllCgF+NX4Os0V4WTU4TwrTmlx3ntqul1+YJN2a7NzG1Tl62KMNSW0gGrSq5u3LUw3euOT26AODGnlopjks20+OF6TwuKH5D3LRXG9Eh6sljhk6BFtYozAWHZKRLSCZtyyOOF7pUTiybjTKEyNk9Mr+sBiVwN+RYWM93gHbMnWRcoNDTna+LVUblRdGM6+GuqnNNZiBtq3zuGO8ds\/\/GbZPJfn1fMHPap5IKFhIVSInxyXrMygoz4tOpFAYEHO0wSQniuq6CE2\/vy5yEAVsUAvJtEuSHpMVUpkSJgr3UWjtT6D51N8oFuBjfVOhY4iTekhG+aevdCO+RGLEXdyFutl0nil35dMV7gXFipomdePNU8\/2t08qZ1Y3ohPOru618z7AyOt4v8rKi5E1M\/QuRsrYJEqQQYXaZMgQJCLKmkvhEygHH7Qi3dgvZRVPjh2pDMndDSBZMSu807TsThiW9VIcuZm9MVudtRasdrMEZdPrSWN5ee5ZIloTIrWrkfkNO1oquRJvcGepvfdUGQJ0WFFqPqvH\/1B7+KqN6oQ6ESUO\/tTUEuJf0fCfrMD+bWPhLN+Skgi7Pgm9eDU6z0ESYTxXktFVMwj6LYrkmY75mhQwkGGuKzfgcw5\/fQXmWQ5\/aEYLFsE68Sex4C41+sarITUjpM6\/67GXUOrR6+pN6e+OXuDMGCZufpRbBkO8uNyWFqNR2cHMj62TQy20GtCLemIvC6pcL\/ajRPi8qvF5vT32tvCSlxp+1Fe2sfvL2oD2u3UYYz2umDj4axrXoCF4zHYgxsen4HOqITyJn5neTRMNxbrW+V0NiOW7GsFMTqBu2boSCpPvBdQvk0YQKgRiUGgNtTMM2ELEfcOX+ISvol31AlnzKU5Rsu4eDfVnPkc62FmyEHLYt6CC2KCu0cT9lacbD2moN2EYWqHlBpIM45oM8zYxCiVOcSNpPwOIu7W6++5np95yBTyOrkDIo5eci46r6TS6sCIID7OFF\/BIDHETTtjSWyzOhqGGfoYtYHxjiZj907yt0JjIJsosGLGSMr6ItmbBreYaLzhdWY3ZnvACXnxYdYiRiiwFGAqy\/Cq5BI0fg5pniIryRL5unWNQU44z5wsy8iID6kZv95b2Uxped4LdlSQ0WottjqTNlbR5kjZ\/vZ476T7z0JKJj31nTn5MrbSWtzsT7e5EuzfRvmRBqnzZYuB+cxcCJ3dai22OpM2VtHmStgWTRlmZlkya9Js5aZJeai22OZI2V9LmSdoWkv5Ssl9E8jJyl5G6jMxlJL5o0igr1aJZk45zp02SbK0ljY6s0ZU1erLGBXP\/vmTe32fP+btlr1F8kP0b1+ysUePKFx64a9S4CoUH3ho1riLhgb9GjfO\/frNom+Nuc798I2xynONMbHLFJk9sWvC9SU6wJRPue86cc5\/obC1rdaStrrTVk7YuWIJXVEN3\/vxxt5mTx4nW1kKTIza5YpMnNi2Y7fFyWTJb3G3mbHGit7XQ5IhNrtjkiU0LZgtgvWS2uNvM2eJEc2uhyRGbXLHJE5sWzBZliFsyXdJv5nxJqru12OZI2lxJmydpW0LQYBH7wt3mEjQQGBfOsyc2uWKTJzYtEr0LUC+TvXHH2cI3Tve3ljQ6skZX1ujJGhfMHefoWzL3ruPMuXcpB9eSRkfW6MoaPVnjkrmfFmlcuNvceZ8EfQvnOxSbXLHJE5sWzBZ5myyZLuk3c74k6eJabHMkba6kzZO0LZg0ypa4ZNKk38xJk7SPa7HNkbS5kjZP0iafNOOERzI\/Tl1WU8khtZBwqkglIPyGHg5JLamGRN7RwsLJKJWQ8BtaOF3ySiWk7h0tLJTuUgkIvaCFgrNjKsHgN7RwSDZNJSDyihZSnkAlmFwedM3AIMk6lWDIK3q6xvtJAanbcAejkEMRtKMD7SwG7epAu4tBezrQ3mLQvg60bwhaYwpCPjbLbEGk52xjEMkTu5a1OtJWV9rqSVsNOHSPgWJ9ByQNYTl6WI4pLFcPyzWF5elheaawfD0sXwsLZahVciP0wqN948v3L0\/x2wO+eA\/o7s\/dA7r7W\/eA7v7QPaC7vzICBI71YxYbAXrIYiNAD1lsBOghi40APWSxyU2KAlJ31WIEx9HBcczguDo4rhkcTwfHM4Pj6+DoWReVYVwt3Y\/vLTr7qVTl+nG6FxcPhJOe64fBry0eRGaZkQyCX1s8CErCrh8DvbV4iDg3WKo4X75QR2gwwBHe8SVIXnmDb0FeXDwQzlCvHwa\/9ggxliSgXyLG9j1nirF9Vv21rNWRtrrSVk\/aqrWuoHiMRhY\/M+EdIyRE8x7oUczHEix0Te3nNRnrYUsd4\/\/7J+IUPyv+qB9roAaU0RzJuqBGxhtqHE1iYDkciJnRBLbId1PzeRMUtg5lkeIWmtd+hcOWsdeX3+dIlfhVI4e5prWa9qOAE97zd7mDYTRvj8iLip3L3jZql\/g3VZA66x6ryykljz9mGRkqjKfmYegNreCAC+8p4eA39DYzVNiPg0MqG57z9095uWrqY7zhbGioz3oVBL9tMAVeQA1LvemR1P6bORbptF55NjsYo80hf1uYrqQ9dVh9V4q53w3U9e9KAfe7gZL+XSnafjdQzb8rhdrvBgr5d6U4+91AliUFFjkgeFcp06gNpRl1Xr1VC1knd0lqdCFsZ5FTKAoRg22jyUMiTSvexzn4QgqzIbDDMOvGvAArgvKN74ThU\/xtMqWG81MiBR+fNOW+NB1CULJB3g4u8qvPhjwr8It8HbNERYoEHGZ0+xNyFRnEQGonb5hj6AzLKz7ojTvkZW76bvOWX3Bi9J+cxkiT2r9jL4RteK5ShkHroUlvISRQpGsauB4TE+z\/orRGj60NgyPC3NnbDq3d3Hiwgf4eVITAob4Fjq9SZFNkMxvdkVJx4CYKdog1r+VJy8d9el\/i8gEOYVs476guOntM2Ggcpbl6iRcFlYyhTZd3knrgbvL27grurCokKRhHfzNlTlSKLhU4TedH7TLdSNTAcMiS9pgNr2ZKRhrmQPZirhHtis7kEwOHR92\/POVlc4FJO\/J4XJ9tY9j7DEFzrSHXe8gHkNRV06A1nJ7EC\/xQi6vbMQ8AlRpAkxGgj6\/Py08eCqbf0JSBeNd6TW2QwWbwj36+ROL5gojZn1vAB6\/63PMF9eHrxHSpaGYX65HU5nF\/Tm0ejDUy+8iS7fuMnWhGuPYA9Wuavy6J\/HeNoK\/SlAoHvW8NSG2ZKVtiSMlgSsMUSaFvZohjLKxKsEgUZvPB+Kx8Ef1DJU50xPRL6rndo0DNzpeEBvyHU6VQLolVcwZFcXtQACxWiUwsHcFo6Qjo4oj6TBYqsWOelQMjO5fj4k5f\/yS1jWh6WUCnS8Y3Os38RLywkXAzh\/Grd4DMqPHIpIujFj0kbfYpyzgubjBjEfC26fkAfLNqiGY6KxEbxojhguoVn1YT0P8l4IIGvfGJAzjjYqDIB1rDNG+biSQEMkPlwuQA3UBfm+sZfXuTE2XIq8LbufhbHeNR9\/tepEthC\/Kisc6gfqHreRmBs84ki576OCFZ3mBS1V0h52uZQky4\/CPjLLq43wDGsEoUwZn9c655WktBROTioapu4uTimdE4X4v8piVTI0grsrLzqhopcyzFsKBzQ42Tc6gcX9aYN7Bv6dVflOB7SdV1oqgda4AI\/csTaAsUAMVghTlGD7g\/DbrsgPgaYCITaA0vELSfHHfDFLPDJu7rubTIZkWTQXXP9JedZInExESaHl9HlbO9KupRMy+fYHExebXnpHgJyLxQUvI\/I2G+XG0NUeu5KQNlt\/rzynLI4tZIixvA+upy9+21UVboXJhTGy3JTaEFLeLvzHFEqwEGvIlChrLtOI6dnPTJaoKF51FXou8mr1hxt+1yKp+szHT5C08KZd7xvmoh0V0mRk6uKF6i\/Wf0l8inxs+3DeV8qhvsRlJU75MCnC+ffGyz2fqvp02I+2nKNA2pT+sx+c1U3STHVQMk5YsxRHwHqSja0E2N5Kmmhtu6v0nqsOB\/bbp691ZTTUIqBYWdsM6YMyPyr1EGalRaAdvzDfJOE84+5n7+WbmkZ4yDMyULbJlhqC6V94lOvO8qmQ+TE3ljko3YIH0VlbKX6KL6l7HPFwEd6FL3N9w3NcyuPpAAtUEFdyhdWi6cvp7sDm7rUftCnXlR6I1VdT6RsY7w0Ok15k5FxlDxmNC7TAyjSpHSXHPyDAjPg2YPA1D9BUnPX3A\/DNgPO34muebYarhZXhLkfCRM\/\/jLGaY5WH261DCDdWPVML0mMLXOVe83if6eZtNdQVW6EhbWkEQEbrSzSFLkEudDIqKm1Rnk5bcbOr5SpFLvGc+q6qUAo2tVasXrVeBSqV+Yk3dqIVJwXH2+IseqblD02zeju\/euaxuTjkSr\/3b\/JbyDxPrua1CfNrQVx2QNj3mDDEEo7SdugR8wRrlnJfUJqI9NxArksgbqZvLr4kMJCQz5ZTVlPWpP8AyttjoeC7iKr23bZfAfLCgh7T2kyRg7Je2eC+sMpBmUceHdPX2E2pP5yhNQvoJJg8m5sKpyIGr86g1\/zbzI24\/9iljqTDqTGy\/W5nMBpfWO\/\/uhnOPq8xlcijyu4bHosJiirM4dTV6E2KXvfwjBzRi3qi6wxu7S1hE27bWGVpPUEJaTd9hob6Pz3UAOnrAONqDk7kanlf1zYWUQpg2D9aW6XC\/9HcuNzcDr9hmzH3XNGyjKXMMCvgKcm1Rn3afv7ZDQ5k5c281bN1emBfnzlxKdoyZmL9XXQFdMVscWhvOYFsB8TSlYvnCmcQZLQ6x+XjHNEQHyxwWVUhYuS2WufDMAMhlcmZymd9vglfrxucD2Bzhdf4OpUIT\/oJw6mPPtN5lLhs\/cQPwUR86HsK2J22zed3fG3VC\/tLPuhvpO0gouykgAvg5ZZ5qpJ5PGTpYEScAVUcL68ArrNk9A0Zs0znmaqtJtI9V29f8y8zBwN8kL2Hx5qnpnxIEiBzchJW\/AZYEapUgxJgwnX0JebWGgakc5mLZQjS2NT8H8QevbIrkkm3ViSEjWFa1UkpomDqqQqndfGk85G29kiVS2IU\/siWfW9EPyRP7MUjxUfi5CZufCesnL9MtTUl3Ltv7oayncUWpWvpA\/xddFPpO8\/Xj6Np9N0Ic24ySDjVgOqQBu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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-n1tk1fe0.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n1tk1fe0.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n1tk1fe0.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n1tk1fe0.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n1tk1fe0.nxe :where(ul,ol){padding-left:20px}#nxe-n1tk1fe0.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n1tk1fe0.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n1tk1fe0.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n1tk1fe0.nxe svg{display:none}#nxe-n1tk1fe0.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n1tk1fe0.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n1tk1fe0.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=\"dossier\" 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>G-P01 \u00b7 TOPIC \u00b7 quantitative-and-qualitative<\/strong>GCRI 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\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Systemic Risk Management<\/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 Systemic Risk Management<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Quantitative and qualitative methods<\/h1><p class=\"nx-tagline\">Risk assessment uses numbers where data support them and structured judgement where they do not. Probabilistic models, loss distributions, expert elicitation, bow-tie diagrams and risk matrices each answer different questions and fail in different ways. This page sets out how the main methods work, when each is fit for purpose, and how combining them keeps an assessment honest about what is known and what is assumed.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/risk-management\/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:\/\/therisk.global\/risk-management\/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\">G-P01 \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\">3<\/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\">GCRI \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>Hazards and exposure in Asia<\/a><a href=\"#nx-s-4-sec\"><span>03<\/span>Cascade and stress lab, in outline<\/a><a href=\"#nx-sec-question\"><span>04<\/span>Quantitative and qualitative\u2026<\/a><a href=\"#nx-sec-terms\"><span>05<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>06<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>07<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>08<\/span>Where the evidence comes from<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"risk\" 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\">Risk assessment methods are the techniques used to identify, analyse and evaluate risk, from probabilistic models and loss distributions to expert elicitation, bow-tie diagrams and risk matrices. Each answers different questions and fails differently. Combining them, and recording data, assumptions and ranges, keeps an assessment honest about what is known.<\/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\">Geography<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Hazards and exposure in Asia.<\/h2><p class=\"nx-lede\">For quantitative and qualitative methods, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, land cover and night lights, framed on Asia; every other NASA layer, live feed and forecast waits in the layer list.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-cmap-field\" data-nx-map=\"ctx-field\" data-cmap=\"asia:risk\" data-ml-points=\"on\" data-ml-src=\"risk\" data-ml-plan=\"p-7728a88e\"><div class=\"nx-wm\" data-nx-water=\"ctx-risk\" data-wm-domain=\"ctx-risk\" data-wm-tiles=\"on\" data-wm-flat=\"on\" data-wm-focus=\"25,-11,150,56\" data-ws-live=\"off\" data-claim=\"INTEGRATED\" data-wm-panel=\"closed\" data-wm-mode=\"2d\"><div class=\"nx-wm-stage\" tabindex=\"0\" role=\"application\" aria-roledescription=\"interactive map\" aria-label=\"Hazards and exposure, Asia: satellite and model layers from NASA on a 3D terrain of ASTER GDEM elevation. Arrow keys move; with Shift they turn and tilt in 3D; plus and minus zoom, 0 resets; I inspects, question mark lists the keys.\" aria-describedby=\"nx-wm-help\"><canvas class=\"nx-wm-canvas\" aria-hidden=\"true\"><\/canvas><p class=\"nx-wm-noscript\">The interactive map needs JavaScript; the layer list and sources are below.<\/p><div class=\"nx-wm-presets\" role=\"group\" aria-label=\"Quick views\"><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"true||||\" aria-pressed=\"false\">True colour<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"cover||||\" aria-pressed=\"false\">Land cover<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"ntl||||\" aria-pressed=\"false\">Night lights<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"people||||\" aria-pressed=\"false\">Population<\/button><\/div><div class=\"nx-wm-panel\" id=\"nx-wm-panel\" role=\"region\" aria-label=\"Map layers\"><div class=\"nx-wm-panel-head\"><p class=\"nx-wm-panel-title\">Layers <span class=\"nx-wm-count\">3<\/span><\/p><button type=\"button\" class=\"nx-wm-icon nx-wm-panel-close\" data-wm-act=\"panel\" aria-label=\"Close the layer list\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-panel-body\"><div class=\"nx-wm-group\"><p class=\"nx-wm-group-title\">Hazards and exposure, for this page<\/p><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"true\" aria-pressed=\"true\" data-wm-z=\"20\" data-wm-lag=\"1\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite image\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">True colour<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS NOAA-20 corrected reflectance \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"true\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"cover\" aria-pressed=\"true\" data-wm-z=\"21\" data-wm-lag=\"0\" data-wm-time=\"latest\" data-wm-op=\"0.7\" data-wm-short=\"Land cover\" data-wm-prov=\"NASA LP DAAC\" data-wm-kind=\"Satellite classification; latest year GIBS publishes\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Land cover type (IGBP), yearly<\/span><span class=\"nx-wm-layer-src\">NASA LP DAAC MODIS Terra and Aqua land cover (MCD12Q1) \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"cover\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"ntl\" aria-pressed=\"true\" data-wm-z=\"22\" data-wm-lag=\"0\" data-wm-op=\"0.9\" data-wm-short=\"Night lights\" data-wm-prov=\"NASA LAADS DAAC\" data-wm-kind=\"Satellite radiance, corrected for moonlight and atmosphere\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Night-time lights, daily<\/span><span class=\"nx-wm-layer-src\">NASA LAADS DAAC VIIRS Black Marble (VNP46A2) \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"ntl\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"people\" aria-pressed=\"false\" data-wm-z=\"23\" data-wm-lag=\"0\" data-wm-op=\"0.8\" data-wm-short=\"Population density\" data-wm-prov=\"NASA SEDAC (CIESIN, Columbia University)\" data-wm-kind=\"Census-based estimate, static (2020)\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Population density, 2020<\/span><span class=\"nx-wm-layer-src\">NASA SEDAC (CIESIN, Columbia University) Gridded Population of the World v4.11 \u00b7 <span data-wm-date=\"people\">static<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"people\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"flood\" aria-pressed=\"false\" data-wm-z=\"24\" data-wm-lag=\"0\" data-wm-short=\"Flood water\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite detection; clouds leave gaps\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Flood water, 3-day composite<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS Global Flood Product \u00b7 <span data-wm-date=\"flood\">14 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"flood\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"fires\" aria-pressed=\"false\" data-wm-z=\"25\" data-wm-lag=\"0\" data-wm-op=\"1\" data-wm-breaks=\"10,50,200\" data-wm-short=\"Fires and flares\" data-wm-prov=\"NASA LANCE FIRMS\" data-wm-kind=\"Satellite detection; clouds and overpass timing leave gaps\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Active fires and thermal anomalies, last day<\/span><span class=\"nx-wm-layer-src\">NASA LANCE FIRMS VIIRS NOAA-20 375 m (FIRMS) \u00b7 <span data-wm-date=\"fires\">13 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"fires\" hidden><\/div><\/div><\/div><\/div><div class=\"nx-wm-rail\" role=\"toolbar\" aria-orientation=\"vertical\" aria-label=\"Map controls\"><button type=\"button\" class=\"nx-wm-icon nx-wm-open\" data-wm-act=\"panel\" aria-controls=\"nx-wm-panel\" aria-expanded=\"false\" aria-label=\"Layers\" title=\"Layers\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M8 2.5l-6.5 3.5 6.5 3.5 6.5-3.5zm-6.5 6.5 6.5 3.5 6.5-3.5m-13 2.8 6.5 3.5 6.5-3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"tools\" aria-controls=\"nx-wm-menu\" aria-expanded=\"false\" aria-label=\"Expert tools\" title=\"Expert tools\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3 4.5l10 0m-10 3.5 10 0m-10 3.5 10 0m-7.5-8.5 0 3m5 .5 0 3m-3.5.5 0 3\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><span class=\"nx-wm-sep\" aria-hidden=\"true\"><\/span><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"in\" aria-label=\"Zoom in\" title=\"Zoom in\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M8 3v10M3 8h10\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"out\" aria-label=\"Zoom out\" title=\"Zoom out\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3 8h10\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"reset\" aria-label=\"Reset the view\" title=\"Reset the view\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.2 8a4.8 4.8 0 1 0 1.5-3.5M3.2 2.6v2.6h2.6\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"full\" aria-label=\"Full screen\" title=\"Full screen\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M2.5 6l0-3.5 3.5 0m7.5 3.5 0-3.5-3.5 0m-7.5 7.5 0 3.5 3.5 0m7.5-3.5 0 3.5-3.5 0\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-menu\" id=\"nx-wm-menu\" role=\"menu\" aria-label=\"Expert tools\" hidden><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"inspect\">Inspect a point<kbd>I<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"measure\">Measure distance<kbd>M<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"compare\">Compare two dates<kbd>C<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"share\">Copy a link to this view<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"png\">Export as PNG with credits<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"xt\" data-wm-xt=\"6oPh6EhWj+gDMzpLLT8H\/wVfxJakWdyAB8snze2Nado=\">More expert tools<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"keys\">Keyboard shortcuts<kbd>?<\/kbd><\/button><\/div><div class=\"nx-wm-keys\" role=\"dialog\" aria-label=\"Keyboard shortcuts\" hidden><p class=\"nx-wm-keys-title\">Keyboard, with the map focused<\/p><dl class=\"nx-wm-keys-list\"><\/dl><button type=\"button\" class=\"nx-wm-icon nx-wm-keys-close\" data-wm-tool=\"keys\" aria-label=\"Close the shortcuts\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><p class=\"nx-wm-hint\" hidden>Hold Ctrl or \u2318 and scroll to zoom<\/p><p class=\"nx-wm-toolbar\" role=\"status\" hidden><\/p><div class=\"nx-wm-tip\" role=\"tooltip\" hidden><\/div><div class=\"nx-wm-pin\" hidden><div class=\"nx-wm-pin-head\"><p class=\"nx-wm-pin-title\"><\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"unpin\" aria-label=\"Close the card\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-pin-chart\"><\/div><div class=\"nx-wm-pin-body\"><\/div><\/div><div class=\"nx-wm-dock\"><div class=\"nx-wm-time\" role=\"group\" aria-label=\"Imagery date\"><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"prev\" aria-label=\"One day earlier\">\u2039<\/button><p class=\"nx-wm-time-label\">Latest available<\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"next\" aria-label=\"One day later\">\u203a<\/button><span class=\"nx-wm-track\" role=\"slider\" tabindex=\"0\" aria-label=\"Imagery date, up to 30 days back\"><\/span><button type=\"button\" class=\"nx-wm-latest\" data-wm-step=\"latest\" aria-pressed=\"true\">Latest<\/button><\/div><div class=\"nx-wm-chips\" role=\"group\" aria-label=\"Legends\"><\/div><p class=\"nx-wm-attrib\">NASA GIBS \/ EOSDIS \u00b7 ASTER GDEM (METI, NASA) \u00b7 Blue Marble (NASA) \u00b7 \u00a9 OpenStreetMap contributors (ODbL) \u00b7 Natural Earth<\/p><div class=\"nx-wm-legends\" aria-live=\"polite\" hidden><\/div><\/div><\/div><div class=\"nx-container nx-wm-below\"><p class=\"nx-wm-status\" role=\"status\">Loading the latest NASA imagery.<\/p><p id=\"nx-wm-help\" class=\"nx-wm-help\">Drag to move. Ctrl or \u2318 and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.<\/p><p class=\"nx-caption\"><span>For quantitative and qualitative methods, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, land cover and night lights, framed on Asia; every other NASA layer, live feed and forecast waits in the layer list.<\/span><span data-honesty=\"true\">Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.<\/span><\/p><details class=\"nx-wm-credits\"><summary><span class=\"nx-wm-credits-line\">Data: NASA GIBS \u00b7 ASTER GDEM \u00b7 Blue Marble \u00b7 \u00a9 OpenStreetMap contributors \u00b7 Natural Earth \u00b7 NASA EONET \u00b7 GDACS \u00b7 Copernicus Sentinel-2 L2A<\/span><span class=\"nx-wm-credits-more\">Sources and licences<\/span><\/summary><p class=\"nx-wm-credit\"><ul class=\"nx-wm-credit-list\"><li data-wm-credit=\"nasa.gibs.field-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.gibs.water-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.lance.viirs-water\">VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.modis-viirs.land\">MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.laads.black-marble\">VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"sedac.gpw-v4\">Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" rel=\"noopener\" target=\"_blank\">CC BY 4.0<\/a><\/li><li data-wm-credit=\"nasa.lance.firms\">NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa-meti.aster-gdem-v3\">ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"naturalearth.all-scales\">Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. <a class=\"nx-link\" href=\"https:\/\/www.naturalearthdata.com\/about\/terms-of-use\/\" rel=\"noopener\" target=\"_blank\">Public domain<\/a><\/li><li data-wm-credit=\"nasa.gibs.reference-osm\">\u00a9 OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.openstreetmap.org\/copyright\" rel=\"noopener\" target=\"_blank\">ODbL<\/a><\/li><li data-wm-credit=\"nasa.earth-observatory.blue-marble\">Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li>Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, \u00a9 OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.<\/li><li>Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, \u00a9 MapLibre contributors.<\/li><li>Live: NASA EONET Events are indicative, not official warnings.<\/li><li>Live: GDACS (EC JRC \/ UN OCHA) Events are indicative, not official warnings.<\/li><li>Observed (EO): Copernicus Sentinel-2 L2A (contains modified Copernicus Sentinel data 2026), Microsoft Planetary Computer<\/li><li>Optional layers (35 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.<\/li><\/ul><p class=\"nx-wm-credit\"><strong>Layers and dates<\/strong><\/p><ul class=\"nx-wm-credit-list\"><li>True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/li><li>Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/li><li>Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/li><li>Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 \u00b7 <span data-wm-date=\"people\">static<\/span><\/li><li>Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE \u00b7 <span data-wm-date=\"flood\">14 Oct 2026<\/span><\/li><li>Active fires and thermal anomalies, last day: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS \u00b7 <span data-wm-date=\"fires\">13 Oct 2026<\/span><\/li><\/ul><\/p><\/details><\/div><\/div><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G-P01\/topics\/quantitative-and-qualitative-methods\/#map\" data-nxf-title=\"Systemic Risk Management live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-s-4-sec\" class=\"nx-section\" data-hue=\"risk\" 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\">Console<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Cascade and stress lab, in outline.<\/h2><p class=\"nx-lede\">The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-systemic-cascade\" data-cx=\"systemic.cascade\" data-variant=\"section\" aria-label=\"Cascade and stress lab: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Cascade and stress lab<\/h3><p class=\"nx-fk-sub\">Quantitative and qualitative methods \u00b7 National system \u00b7 Scenario SC-40<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure's values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"not\"><i aria-hidden=\"true\"><\/i>Cascade crosses three systems<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Systemic Risk Management<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"map\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Interdependency graph<\/h4><p class=\"nx-fk-panel-sub\">red: failed in scenario<\/p><\/div><\/div><div class=\"nx-cx-map\"><svg class=\"nx-cx-svg\" viewBox=\"0 0 480 250\" role=\"img\" aria-label=\"Interdependency graph: schematic on placeholder geography. Illustrative.\" focusable=\"false\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"tl\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Cascade sequence<\/h4><\/div><\/div><ol class=\"nx-cx-tl\"><li data-s=\"watch\"><span class=\"nx-cx-tt\">T+0<\/span><span class=\"nx-cx-tag\">HAZ<\/span><span class=\"nx-cx-tx\">Heatwave and drought<\/span><\/li><li data-s=\"alert\"><span class=\"nx-cx-tt\">T+6h<\/span><span class=\"nx-cx-tag\">ENG<\/span><span class=\"nx-cx-tx\">Thermal plant derated, cooling water<\/span><\/li><li data-s=\"watch\"><span class=\"nx-cx-tt\">T+9h<\/span><span class=\"nx-cx-tag\">ICT<\/span><span class=\"nx-cx-tx\">Data centre on backup power<\/span><\/li><li data-s=\"alert\"><span class=\"nx-cx-tt\">T+14h<\/span><span class=\"nx-cx-tag\">WAT<\/span><span class=\"nx-cx-tx\">Pumping reduced, pressure loss<\/span><\/li><li data-s=\"alert\"><span class=\"nx-cx-tt\">T+20h<\/span><span class=\"nx-cx-tag\">HLT<\/span><span class=\"nx-cx-tx\">Hospital water supply at risk<\/span><\/li><\/ol><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Service loss by system<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 520 210\" width=\"520\" height=\"210\" role=\"img\" focusable=\"false\" preserveAspectRatio=\"xMidYMid meet\" aria-label=\"Service loss by system\" data-fk-chart=\"cx-systemic-cascade-c2\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Cascade and stress lab: the screen's structure, terms and states for Quantitative and qualitative methods.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" 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\">The question<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Quantitative and qualitative methods: how the risk develops.<\/h2><p class=\"nx-lede\">Probability, uncertainty and judgment. 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=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Models need data that fit<\/h3><p class=\"nx-p nx-mt-1\">Frequency and severity models work where events are numerous and recorded consistently. For rare, novel or changing risks the data are thin, and a precise-looking number can carry more assumption than evidence.<\/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\">Judgement carries biases<\/h3><p class=\"nx-p nx-mt-1\">Experts anchor on recent events, overestimate their own precision and converge in groups. Structured elicitation reduces these effects by separating estimates, asking for ranges and scoring calibration.<\/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=\"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\">Matrices compress information<\/h3><p class=\"nx-p nx-mt-1\">Likelihood and consequence matrices are quick to use, but they rank risks coarsely, can reverse orderings and hide correlated losses. They suit screening, and need other methods before a major decision.<\/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 width=\"8\" height=\"8\" x=\"3\" y=\"3\" rx=\"2\"\/><path d=\"M7 11v4a2 2 0 0 0 2 2h4\"\/><rect width=\"8\" height=\"8\" x=\"13\" y=\"13\" rx=\"2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Methods combine through structure<\/h3><p class=\"nx-p nx-mt-1\">Fault trees, event trees and bow-tie diagrams link causes, controls and consequences. They provide a frame into which quantitative estimates and expert judgement can be placed and checked.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" data-variant=\"split\" 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\">Key terms<\/span><\/div><h2 id=\"nx-s-6\" 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\">Risk matrix<\/td><td data-label=\"Definition\">A grid that ranks risks by combining likelihood and consequence categories, useful for screening.<\/td><\/tr><tr><td data-label=\"Term\">Bow-tie analysis<\/td><td data-label=\"Definition\">A diagram linking the causes of an event, the controls that prevent it, and the consequences and controls that limit them.<\/td><\/tr><tr><td data-label=\"Term\">Structured expert judgement<\/td><td data-label=\"Definition\">A formal elicitation method that scores experts on calibration questions and combines their estimates accordingly.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" 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\">Indicators<\/span><\/div><h2 id=\"nx-s-7\" 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\"><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\">Risks assessed with quantitative estimates<\/td><td data-label=\"Unit\">percent of principal risks<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Assessment records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Calibration of expert estimates<\/td><td data-label=\"Unit\">calibration score on seed questions<\/td><td data-label=\"Cadence\">per elicitation<\/td><td data-label=\"Source type\">Structured expert judgement records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Loss event records<\/td><td data-label=\"Unit\">count of events with cause and amount recorded<\/td><td data-label=\"Cadence\">continuous<\/td><td data-label=\"Source type\">Internal loss databases<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Assessment uncertainty range<\/td><td data-label=\"Unit\">ratio of upper to lower bound<\/td><td data-label=\"Cadence\">per assessment<\/td><td data-label=\"Source type\">Assessment outputs<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Controls with tested effectiveness<\/td><td data-label=\"Unit\">percent of key controls<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Control testing records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Method documentation completeness<\/td><td data-label=\"Unit\">percent of assessments with method, data and assumptions recorded<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Assessment files<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gcri\/G-P01\/topics\/quantitative-and-qualitative-methods\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" 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\">Canvas<\/span><\/div><h2 id=\"nx-s-8\" 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 Quantitative and qualitative methods 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-g-p01-topics-quantitative-and-qualitative-t nx-dag-g-p01-topics-quantitative-and-qualitative-d\" data-nx-z=\"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\"><title id=\"nx-dag-g-p01-topics-quantitative-and-qualitative-t\">Quantitative and qualitative methods: a cascade across interdependent systems<\/title><desc id=\"nx-dag-g-p01-topics-quantitative-and-qualitative-d\">A hazard hits one system; dependencies carry the failure into others; impact grows until buffers, redundancy or restoration priorities stop it; the analysis ranks the links to strengthen. 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=\"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\">Sources<\/span><\/div><h2 id=\"nx-s-9\" 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\"><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\">IEC 31010 Risk assessment techniques<\/td><td data-label=\"What it gives\">Descriptions, strengths and limits of more than forty techniques<\/td><td data-label=\"Licence class\">Licensed standard, purchase required<\/td><\/tr><tr><td data-label=\"Source\">EM-DAT international disaster database (CRED, UCLouvain)<\/td><td data-label=\"What it gives\">Disaster events with human and economic impacts<\/td><td data-label=\"Licence class\">Free for non-commercial use with registration<\/td><\/tr><tr><td data-label=\"Source\">Sendai Framework Monitor and national disaster loss databases<\/td><td data-label=\"What it gives\">Official reporting of disaster losses against Sendai targets<\/td><td data-label=\"Licence class\">Public, UN terms of use<\/td><\/tr><tr><td data-label=\"Source\">TU Delft expert judgement database<\/td><td data-label=\"What it gives\">Past structured expert judgement studies with calibration data<\/td><td data-label=\"Licence class\">Licence to check<\/td><\/tr><tr><td data-label=\"Source\">Published operational loss data consortia summaries<\/td><td data-label=\"What it gives\">Aggregate operational loss patterns in financial services<\/td><td data-label=\"Licence class\">Members only; public summaries (to check)<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G-P01\/topics\/quantitative-and-qualitative-methods\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" 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\">Decisions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Systemic Risk Management 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\" data-lv=\"ledger\"><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\">Risk functions and analysts<\/td><td data-label=\"The question\">Which method fits a given risk and decision<\/td><td data-label=\"Evidence this page serves\">Method selection guides; documentation standards<\/td><\/tr><tr><td data-label=\"Who decides\">Boards and executives<\/td><td data-label=\"The question\">How much confidence to place in an assessment before acting on it<\/td><td data-label=\"Evidence this page serves\">Uncertainty ranges; method documentation; calibration records<\/td><\/tr><tr><td data-label=\"Who decides\">Regulators and supervisors<\/td><td data-label=\"The question\">Whether an institution's risk models are fit for the use made of them<\/td><td data-label=\"Evidence this page serves\">Model documentation; validation and back-testing evidence<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Method selection guides keyed to the question and the evidence available<\/li><li>Structured expert elicitation protocols with calibration<\/li><li>Bow-tie and fault-tree templates linked to registers<\/li><li>Assessment files that record data, assumptions and ranges<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" 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\">Core questions<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">What methods experts ask.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>Is there enough relevant data to estimate probabilities, or would numbers convey a false sense of precision?<\/li><li>What kind of uncertainty dominates: randomness, lack of knowledge, or disagreement about how the system works?<\/li><li>How sensitive is the decision to the parts of the analysis that are least certain?<\/li><li>How can expert judgement be elicited so that it is transparent and less prone to bias?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"risk\" 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\">Mechanisms<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Kinds of uncertainty.<\/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=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Aleatory<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Randomness<\/h3><p class=\"nx-p nx-mt-1\">Natural variability that persists even with perfect knowledge, such as weather from year to year. Probability handles it well.<\/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=\"m8 11 2 2 4-4\"\/><circle cx=\"11\" cy=\"11\" r=\"8\"\/><path d=\"m21 21-4.3-4.3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Epistemic<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Lack of knowledge<\/h3><p class=\"nx-p nx-mt-1\">Uncertainty that more data or research could reduce. It should be shown, not hidden in a single number.<\/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=\"10\"\/><polygon points=\"16.24 7.76 14.12 14.12 7.76 16.24 9.88 9.88 16.24 7.76\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Deep<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Disputed structure<\/h3><p class=\"nx-p nx-mt-1\">Where experts disagree on models, probabilities or values. Scenarios and robust decision methods suit it better.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-methods-and-limits\" 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 and limits<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">A toolkit, matched to the problem.<\/h2><\/div><div class=\"nx-section-body\"><ol class=\"nx-rows\"><li><span class=\"nx-eyebrow nx-index\">01<\/span><div><h3 class=\"nx-h4\">Statistical and probabilistic models<\/h3><p class=\"nx-p nx-mt-1\">Frequency analysis, extreme value methods and Bayesian inference from <a class=\"nx-link\" href=\"https:\/\/therisk.global\/risk-management\/stem\/probability-statistics\/\">probability and statistics<\/a>. Limits: rare events are poorly sampled, and past data may not reflect a changing system.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">02<\/span><div><h3 class=\"nx-h4\">Uncertainty quantification<\/h3><p class=\"nx-p nx-mt-1\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/risk-management\/stem\/uncertainty-quantification\/\">Uncertainty quantification and model validation<\/a> propagates input uncertainty through models and tests them against data. Limits: it cannot capture what the model leaves out.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">03<\/span><div><h3 class=\"nx-h4\">Structured expert judgement<\/h3><p class=\"nx-p nx-mt-1\">Formal elicitation protocols that calibrate and combine expert views. Limits: time-consuming, and experts share blind spots.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">04<\/span><div><h3 class=\"nx-h4\">Qualitative assessment<\/h3><p class=\"nx-p nx-mt-1\">Scales, matrices and narrative analysis. Useful for communication and screening, but risk matrices can rank risks inconsistently and should not be the only tool.<\/p><\/div><\/li><\/ol><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"risk\" 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\">Methods<\/span><\/div><h2 id=\"nx-s-14\" 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:\/\/therisk.global\/risk-management\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Frequency and severity modelling \u00b7 Dependence between risks \u00b7 Combining data and judgement<\/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:\/\/therisk.global\/risk-management\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Propagating input uncertainty \u00b7 Separating kinds of uncertainty \u00b7 Deep uncertainty framing<\/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:\/\/therisk.global\/risk-management\/stem\/decision-analysis\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Decision analysis under uncertainty<\/span><span class=\"nx-mledger-role\">Multi-criteria treatment choice \u00b7 Decision trees for contingent plans<\/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\/decision-analysis\/\" aria-label=\"Decision analysis under uncertainty: 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:\/\/therisk.global\/risk-management\/stem\/scenario-stress\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Scenario design and stress testing<\/span><span class=\"nx-mledger-role\">Cross-sector scenario design \u00b7 Reverse stress testing \u00b7 Second-round effects<\/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\/scenario-stress\/\" aria-label=\"Scenario design and stress testing: 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\">No assessment method removes uncertainty; each makes different assumptions visible or hidden. Loss databases are incomplete, especially for small events and poorer places. Expert elicitation depends on who is asked and how. Matrices and scores are conventions, and comparisons between organizations using different scales mislead. Systemic Risk Management describes methods and their limits; it does not assess any organization's risks or approve its models.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-15-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-15\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">14<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-15\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>Are risk matrices reliable?<\/summary><div class=\"nx-answer\">They are useful for screening and communication, but they rank risks coarsely and can mislead when risks are correlated or consequences are uncertain. Important decisions need methods that show ranges and dependencies.<\/div><\/details><details><summary>What is structured expert judgement?<\/summary><div class=\"nx-answer\">A formal process for eliciting estimates from experts, often with seed questions whose answers are known, so that each expert's calibration and informativeness can be scored and estimates combined accordingly.<\/div><\/details><details><summary>When should risk be quantified?<\/summary><div class=\"nx-answer\">When data or models can support a number whose uncertainty can be stated, and when the decision needs it. Where they cannot, a well-documented qualitative assessment is more honest than a false number.<\/div><\/details><details><summary>Are quantitative methods always better?<\/summary><div class=\"nx-answer\">No. They are better when data and models are adequate. Where they are not, numbers can mislead, and structured qualitative methods or scenarios are more honest.<\/div><\/details><details><summary>What is wrong with risk matrices?<\/summary><div class=\"nx-answer\">They are simple and widely understood, but coarse scales can rank different risks equally, hide uncertainty and ignore dependence. They work best as a screening tool alongside other methods.<\/div><\/details><details><summary>How should uncertainty be communicated?<\/summary><div class=\"nx-answer\">With ranges, scenarios and plain statements of what is not known, so that decision-makers can see how much the conclusion depends on uncertain inputs.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-16\" class=\"nx-h3 nx-mt-2\">Around Systemic Risk Management.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/therisk.global\/risk-management\/topics\/risk-governance-and-appetite\/\">Risk governance and appetite <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:\/\/therisk.global\/risk-management\/topics\/identification-and-risk-taxonomy\/\">Identification and risk taxonomy <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:\/\/therisk.global\/risk-management\/topics\/cascades-and-interdependence\/\">Cascades and interdependence <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:\/\/therisk.global\/risk-management\/topics\/scenario-and-stress-testing\/\">Scenario and stress testing <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:\/\/therisk.global\/risk-management\/topics\/resilience-engineering\/\">Resilience engineering <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:\/\/therisk.global\/risk-management\/topics\/enterprise-and-public-risk\/\">Enterprise and public risk <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:\/\/therisk.global\/risk-management\/\">Systemic Risk Management 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:\/\/therisk.global\/risk-management\/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:\/\/therisk.global\/\">The Global Centre for Risk and Innovation 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:\/\/therisk.global\/trust\/\">GCRI 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-17\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Systemic Risk Management<\/span><h2 id=\"nx-close-17\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. 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