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type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-water-engine\" 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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-njvtcfqt.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-njvtcfqt.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-njvtcfqt.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-njvtcfqt.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-njvtcfqt.nxe :where(ul,ol){padding-left:20px}#nxe-njvtcfqt.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-njvtcfqt.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-njvtcfqt.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-njvtcfqt.nxe svg{display:none}#nxe-njvtcfqt.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-njvtcfqt.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-njvtcfqt.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=\"editorial\" 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>R04 \u00b7 TOPIC \u00b7 deep-uncertainty<\/strong>GRF 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\">GRF<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Foresight Nexus<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Established<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 Foresight Nexus<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Deep uncertainty<\/h1><p class=\"nx-tagline\">Deep uncertainty describes situations where experts and stakeholders cannot agree on the models that link actions to outcomes, the probability of key future conditions, or how to value the results. Treating such conditions as if a single probability were known produces fragile decisions. This page sets out the methods institutions use when probabilities are contested: robustness, adaptive pathways and monitored triggers, and the mandates that must accept an explicit plan for changing course.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskforum.com\/foresight\/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:\/\/globalriskforum.com\/foresight\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\" data-variant=\"contents\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>In this dossier<\/strong><\/span><span class=\"nx-eyebrow\">7 parts<\/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\">The short answer<\/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\">Deep uncertainty: how the risk develops<\/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\">Terms used on this page<\/span><\/a><\/li><li><a href=\"#nx-sec-indicators\"><span class=\"nx-register-n\">04<\/span><span class=\"nx-register-k\">Indicators<\/span><span class=\"nx-register-v\">What is measured, and how well<\/span><\/a><\/li><li><a href=\"#nx-sec-pipeline\"><span class=\"nx-register-n\">05<\/span><span class=\"nx-register-k\">Canvas<\/span><span class=\"nx-register-v\">From sources to decisions<\/span><\/a><\/li><li><a href=\"#nx-sec-sources\"><span class=\"nx-register-n\">06<\/span><span class=\"nx-register-k\">Sources<\/span><span class=\"nx-register-v\">Where the evidence comes from<\/span><\/a><\/li><li><a href=\"#nx-sec-decisions\"><span class=\"nx-register-n\">07<\/span><span class=\"nx-register-k\">Decisions<\/span><span class=\"nx-register-v\">Who decides, and what the evidence serves<\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GRF \u00b7 Established \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 the Arctic<\/a><a href=\"#nx-s-4-sec\"><span>03<\/span>Policy wind tunnel, in outline<\/a><a href=\"#nx-sec-question\"><span>04<\/span>Deep uncertainty: how the risk\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=\"systemic\" data-variant=\"stack\" 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\">Deep uncertainty describes situations where experts and stakeholders cannot agree on the models linking actions to outcomes, the probabilities of key future conditions or how to value results. Decision making under deep uncertainty seeks options that perform acceptably across many futures, and plans that adapt when signposts reach pre-agreed triggers.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"risk\" 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 the Arctic.<\/h2><p class=\"nx-lede\">For deep uncertainty, in GRF's situational-awareness reading, the map opens on population density, flood water and rain with relief in 3D and clouds now from NASA GIBS, framed on the Arctic; 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=\"arctic:risk\" data-ml-src=\"risk\" data-ml-plan=\"p-17de8b0f\" data-ml-relief=\"1.3\"><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=\"-180,58,180,90\" 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, the Arctic: 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=\"flood,people||||\" aria-pressed=\"false\">People and floods<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"people||||\" aria-pressed=\"false\">Population<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"flood||||\" aria-pressed=\"false\">Floods this week<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"imerg||||\" aria-pressed=\"false\">Rain now<\/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=\"people\" aria-pressed=\"true\" data-wm-z=\"20\" 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=\"true\" data-wm-z=\"21\" 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=\"imerg\" aria-pressed=\"true\" data-wm-z=\"22\" data-wm-lag=\"0\" data-wm-short=\"Precipitation\" data-wm-prov=\"NASA GES DISC\" data-wm-kind=\"Satellite estimate, near real time\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Precipitation rate, daily mean<\/span><span class=\"nx-wm-layer-src\">NASA GES DISC GPM IMERG \u00b7 <span data-wm-date=\"imerg\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"imerg\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"lstd\" aria-pressed=\"false\" data-wm-z=\"23\" data-wm-lag=\"0\" data-wm-op=\"0.8\" data-wm-short=\"Land temperature, day\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite 8-day composite; persistent cloud still leaves gaps\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Land surface temperature, day, 8-day composite<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS NOAA-20 Level 3, 8-day \u00b7 <span data-wm-date=\"lstd\">14 Sep 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"lstd\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"ssta\" aria-pressed=\"false\" data-wm-z=\"24\" data-wm-lag=\"0\" data-wm-op=\"0.85\" data-wm-short=\"Marine heat (anomaly)\" data-wm-prov=\"NASA JPL PO.DAAC\" data-wm-kind=\"Satellite analysis\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Sea surface temperature anomaly<\/span><span class=\"nx-wm-layer-src\">NASA JPL PO.DAAC GHRSST MUR25 \u00b7 <span data-wm-date=\"ssta\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"ssta\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"ntl\" aria-pressed=\"false\" data-wm-z=\"25\" 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><\/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 deep uncertainty, in GRF's situational-awareness reading, the map opens on population density, flood water and rain with relief in 3D and clouds now from NASA GIBS, framed on the Arctic; 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 NOAA GOES-East ABI GeoColor<\/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=\"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.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.gpm.imerg\">GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); 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.podaac.ghrsst-mur\">GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.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=\"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=\"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>Live: NOAA GOES-East ABI GeoColor (CIRA). 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).<\/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>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>Precipitation rate, daily mean: GPM IMERG, NASA GES DISC \u00b7 <span data-wm-date=\"imerg\">12 Oct 2026<\/span><\/li><li>Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE \u00b7 <span data-wm-date=\"lstd\">14 Sep 2026<\/span><\/li><li>Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC \u00b7 <span data-wm-date=\"ssta\">12 Oct 2026<\/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><\/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:\/\/grf\/R04\/topics\/deep-uncertainty\/#map\" data-nxf-title=\"Foresight Nexus live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-s-4-sec\" class=\"nx-section\" data-hue=\"systemic\" 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\">Policy wind tunnel, 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-foresight-windtunnel\" data-cx=\"foresight.windtunnel\" data-variant=\"section\" aria-label=\"Policy wind tunnel: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Policy wind tunnel<\/h3><p class=\"nx-fk-sub\">Deep uncertainty \u00b7 Scenario set B \u00b7 Policy: POL-19<\/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=\"near\"><i aria-hidden=\"true\"><\/i>Fails in one scenario<\/span><span class=\"nx-cx-prod\">GRF \u00b7 Foresight Nexus<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"4\" data-t=\"matrix\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Policy performance by scenario<\/h4><p class=\"nx-fk-panel-sub\">0 fails to 100 robust<\/p><\/div><\/div><div class=\"nx-fk-chart\"><svg class=\"nx-fk-svg\" viewBox=\"0 0 520 110\" width=\"520\" height=\"110\" role=\"img\" focusable=\"false\" preserveAspectRatio=\"xMidYMid meet\" aria-label=\"Policy performance by scenario\" data-fk-chart=\"cx-foresight-windtunnel-heat\" data-nx-z=\"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\"><\/svg><\/div><p class=\"nx-fk-note\">Higher is more robust. Placeholder scores.<\/p><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"2\" data-t=\"queue\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Signposts to monitor<\/h4><\/div><\/div><ol class=\"nx-cx-q\"><li><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>S-1<\/span><span class=\"nx-cx-qb\"><span class=\"nx-cx-qt\">Trade restrictions on inputs<\/span><span class=\"nx-cx-qm\">triggers adaptation 2<\/span><\/span><span class=\"nx-cx-age\">quarterly<\/span><\/li><li><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>S-2<\/span><span class=\"nx-cx-qb\"><span class=\"nx-cx-qt\">Cost of sensors<\/span><span class=\"nx-cx-qm\">triggers scale-up<\/span><\/span><span class=\"nx-cx-age\">annual<\/span><\/li><\/ol><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Policy wind tunnel: the screen's structure, terms and states for Deep uncertainty.<\/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=\"risk\" 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\">The question<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Deep uncertainty: how the risk develops.<\/h2><p class=\"nx-lede\">Methods when probabilities are contested. 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\"><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\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Contested probabilities<\/h3><p class=\"nx-p nx-mt-1\">Long horizons, novel conditions and reflexive human behaviour leave no reliable frequency record. Different credible models give different answers, and choosing one hides the disagreement that decision-makers need to see.<\/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\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Robustness over optimality<\/h3><p class=\"nx-p nx-mt-1\">Under deep uncertainty the question shifts from which option is best in an expected future to which option performs acceptably across many futures, and where it fails. Vulnerabilities, not predictions, become the focus.<\/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=\"6\" cy=\"19\" r=\"3\"\/><path d=\"M9 19h8.5a3.5 3.5 0 0 0 0-7h-11a3.5 3.5 0 0 1 0-7H15\"\/><circle cx=\"18\" cy=\"5\" r=\"3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Adaptive pathways and tipping points<\/h3><p class=\"nx-p nx-mt-1\">Plans are sequenced so that early actions keep later options open. Each pathway has an adaptation tipping point, the condition beyond which it no longer meets its objective, and a lead time to switch.<\/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=\"M5 22h14\"\/><path d=\"M5 2h14\"\/><path d=\"M17 22v-4.172a2 2 0 0 0-.586-1.414L12 12l-4.414 4.414A2 2 0 0 0 7 17.828V22\"\/><path d=\"M7 2v4.172a2 2 0 0 0 .586 1.414L12 12l4.414-4.414A2 2 0 0 0 17 6.172V2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Signposts, triggers and commitment<\/h3><p class=\"nx-p nx-mt-1\">Adaptive plans need monitored signposts, pre-agreed triggers and an institution with authority to act on them. Without that commitment an adaptive plan becomes a static plan with a review clause.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"systemic\" data-tone=\"alt\" 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\">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\">Regret<\/td><td data-label=\"Definition\">The difference between an option's outcome in a given future and the best outcome available in that future.<\/td><\/tr><tr><td data-label=\"Term\">Adaptation tipping point<\/td><td data-label=\"Definition\">The point at which a strategy no longer meets its objectives as conditions change.<\/td><\/tr><tr><td data-label=\"Term\">Dynamic adaptive policy pathways<\/td><td data-label=\"Definition\">A method that maps sequences of actions and the tipping points at which to switch between them.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"risk\" 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\">Spread of outcomes across a scenario ensemble<\/td><td data-label=\"Unit\">units of the decision objective<\/td><td data-label=\"Cadence\">per assessment<\/td><td data-label=\"Source type\">Model ensembles<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Options meeting performance thresholds across futures<\/td><td data-label=\"Unit\">share of sampled futures<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Robustness analysis<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Regret of the preferred option<\/td><td data-label=\"Unit\">units of the decision objective<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Regret analysis across scenarios<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Plans with stated adaptation tipping points<\/td><td data-label=\"Unit\">count of plans<\/td><td data-label=\"Cadence\">per planning cycle<\/td><td data-label=\"Source type\">Published plans<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Signposts monitored with pre-agreed triggers<\/td><td data-label=\"Unit\">count of signposts<\/td><td data-label=\"Cadence\">quarterly<\/td><td data-label=\"Source type\">Monitoring register<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Spread of elicited expert judgement<\/td><td data-label=\"Unit\">range between elicited quantiles<\/td><td data-label=\"Cadence\">per elicitation<\/td><td data-label=\"Source type\">Structured expert elicitation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Structural diversity of the model ensemble<\/td><td data-label=\"Unit\">count of independent model structures<\/td><td data-label=\"Cadence\">per assessment<\/td><td data-label=\"Source type\">Model intercomparison records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/grf\/R04\/topics\/deep-uncertainty\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"systemic\" 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 Deep uncertainty 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-r04-topics-deep-uncertainty-foresight-t nx-dag-r04-topics-deep-uncertainty-foresight-d\" data-nx-z=\"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\"><title id=\"nx-dag-r04-topics-deep-uncertainty-foresight-t\">Deep uncertainty: from horizon scanning to wind-tunnelled policy<\/title><desc id=\"nx-dag-r04-topics-deep-uncertainty-foresight-d\">Signals from horizon scanning are clustered into drivers; the most uncertain and impactful define scenario axes; narratives are written; policies are wind-tunnelled against each scenario and signposts monitored. 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=\"risk\" 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\">IPCC Sixth Assessment Report, Working Groups I and II<\/td><td data-label=\"What it gives\">Assessed ranges, calibrated confidence language and storylines for low-likelihood, high-impact outcomes<\/td><td data-label=\"Licence class\">IPCC terms, free with attribution<\/td><\/tr><tr><td data-label=\"Source\">Thames Estuary 2100 Plan (Environment Agency)<\/td><td data-label=\"What it gives\">Adaptive pathways for tidal flood risk with monitored indicators and decision points<\/td><td data-label=\"Licence class\">Open Government Licence<\/td><\/tr><tr><td data-label=\"Source\">Dutch Delta Programme (Deltaprogramma)<\/td><td data-label=\"What it gives\">Adaptive delta management, decision points and annual programme reporting<\/td><td data-label=\"Licence class\">Public records, Dutch government terms<\/td><\/tr><tr><td data-label=\"Source\">NGFS climate scenarios<\/td><td data-label=\"What it gives\">A structured set of transition and physical risk pathways for financial supervisors<\/td><td data-label=\"Licence class\">Free with attribution<\/td><\/tr><tr><td data-label=\"Source\">Society for Decision Making under Deep Uncertainty<\/td><td data-label=\"What it gives\">Published methods, case studies and open tools for robust and adaptive decisions<\/td><td data-label=\"Licence class\">Terms per publication, many open access<\/td><\/tr><tr><td data-label=\"Source\">CMIP model intercomparison archives (WCRP)<\/td><td data-label=\"What it gives\">Multi-model climate outputs that show where model structure drives disagreement<\/td><td data-label=\"Licence class\">Free and open, CC BY 4.0 for most datasets<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/grf\/R04\/topics\/deep-uncertainty\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"systemic\" data-tone=\"alt\" 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\">Decisions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Foresight Nexus 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\">Finance ministries and treasuries<\/td><td data-label=\"The question\">Whether appraisal of long-lived public investment should test robustness rather than a single central case<\/td><td data-label=\"Evidence this page serves\">Regret and robustness results beside the central appraisal, with the futures in which each option fails<\/td><\/tr><tr><td data-label=\"Who decides\">Infrastructure and flood risk authorities<\/td><td data-label=\"The question\">When to commit to the next stage of an adaptive plan<\/td><td data-label=\"Evidence this page serves\">Signpost status against triggers, the lead time to switch, and the tipping point of the current pathway<\/td><\/tr><tr><td data-label=\"Who decides\">Central banks and financial supervisors<\/td><td data-label=\"The question\">How to set supervisory expectations when climate and transition paths are contested<\/td><td data-label=\"Evidence this page serves\">Scenario ranges with model and assumption differences shown, not averaged away<\/td><\/tr><tr><td data-label=\"Who decides\">Environment and climate ministries<\/td><td data-label=\"The question\">Which adaptation measures are low-regret now and which should wait for more information<\/td><td data-label=\"Evidence this page serves\">Robustness across ensembles and the value of waiting, stated with its assumptions<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Vulnerability maps that show where options fail across futures<\/li><li>Adaptive pathway diagrams with tipping points and switching lead times<\/li><li>Signpost and trigger registers for plan owners<\/li><li>Method notes on when probabilities are and are not defensible<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"risk\" 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 practitioners ask.<\/h2><\/div><div class=\"nx-section-body\" data-lv=\"columns\"><ul class=\"nx-list\"><li>When is uncertainty deep, and when can it be represented by probabilities?<\/li><li>How can decisions be made robust across many possible futures?<\/li><li>How should decisions be designed to adapt as the future unfolds?<\/li><li>How can disagreement among experts be represented honestly?<\/li><li>How should decision-makers communicate choices made under deep uncertainty?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"systemic\" data-variant=\"stack\" 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\">Drivers and dynamics.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Economists have long distinguished risk, where probabilities are known, from uncertainty, where they are not. Under deep uncertainty, optimizing for a single best estimate can produce strategies that fail badly if the estimate is wrong. Decision science has therefore developed approaches that seek robustness, performing acceptably across many futures, and adaptiveness, changing course as new information arrives.<\/p><p class=\"nx-p\">Methods such as robust decision making, dynamic adaptive policy pathways and exploratory modelling run many plausible futures through models to identify the conditions under which strategies fail. Decisions are then designed with signposts and triggers: observable indicators that would prompt a change of course. These approaches have been used in water management, flood protection and long-term infrastructure planning.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-limits\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" data-variant=\"split\" 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\">How it is approached.<\/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\">Exploratory modelling<\/h3><p class=\"nx-p nx-mt-1\">Running models across many assumptions to map where strategies succeed or fail, using <a class=\"nx-link\" href=\"https:\/\/globalriskforum.com\/foresight\/stem\/complex-systems\/\">complex systems and agent-based modelling<\/a>.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">02<\/span><div><h3 class=\"nx-h4\">Robustness analysis<\/h3><p class=\"nx-p nx-mt-1\">Comparing strategies by their performance across futures, drawing on <a class=\"nx-link\" href=\"https:\/\/globalriskforum.com\/foresight\/stem\/decision-analysis\/\">decision analysis under uncertainty<\/a>.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">03<\/span><div><h3 class=\"nx-h4\">Adaptive pathways<\/h3><p class=\"nx-p nx-mt-1\">Sequencing actions with signposts and triggers so that plans can change as conditions do.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">04<\/span><div><h3 class=\"nx-h4\">Limits<\/h3><p class=\"nx-p nx-mt-1\">These methods need significant analytic capacity, depend on the range of futures considered, and still require value judgements about acceptable outcomes.<\/p><\/div><\/li><\/ol><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"systemic\" 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:\/\/globalriskforum.com\/foresight\/stem\/decision-analysis\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Decision analysis under uncertainty<\/span><span class=\"nx-mledger-role\">Robust decision making \u00b7 Dynamic adaptive policy pathways \u00b7 Structured expert elicitation<\/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\" target=\"_blank\" rel=\"noopener\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskforum.com\/foresight\/stem\/scenario-stress\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Scenario design and stress testing<\/span><span class=\"nx-mledger-role\">Exploratory scenario development \u00b7 Policy wind-tunnelling<\/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\" target=\"_blank\" rel=\"noopener\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskforum.com\/foresight\/stem\/complex-systems\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Complex systems and agent-based modelling<\/span><span class=\"nx-mledger-role\">Systems mapping of drivers \u00b7 Agent-based exploration of social change<\/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\/complex-systems\/\" aria-label=\"Complex systems and agent-based modelling: canonical page on the STEM Commons\" target=\"_blank\" rel=\"noopener\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskforum.com\/foresight\/stem\/forecasting\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Forecasting and time-series analysis<\/span><span class=\"nx-mledger-role\">Aggregated judgemental forecasting \u00b7 Probabilistic population projections<\/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\/forecasting\/\" aria-label=\"Forecasting and time-series analysis: canonical page on the STEM Commons\" target=\"_blank\" rel=\"noopener\">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\">Robust and adaptive methods need many model runs, careful scenario sampling and stakeholder agreement on objectives, all of which take time and judgement. Robustness is only as wide as the futures considered; an unexplored region can hide a failure. Adaptive plans work only if monitoring is funded and triggers are honoured when political pressure runs the other way. Foresight Nexus documents methods and cases; commitment to a pathway belongs to the authority that holds it.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-15-sec\" class=\"nx-section\" data-hue=\"risk\" 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>When is uncertainty deep rather than ordinary?<\/summary><div class=\"nx-answer\">When credible experts cannot agree on the system model, the likelihood of key conditions, or how outcomes should be valued. Ordinary uncertainty can be expressed as an agreed probability distribution; deep uncertainty cannot, without hiding a disagreement.<\/div><\/details><details><summary>Does deep uncertainty mean doing nothing?<\/summary><div class=\"nx-answer\">No. It favours actions that perform acceptably across many futures, that keep options open, and that come with monitored triggers for changing course. Waiting is itself an option, with its own risks.<\/div><\/details><details><summary>What is an adaptation tipping point?<\/summary><div class=\"nx-answer\">The condition at which a policy or plan stops meeting its objective, for example a sea level a defence can no longer hold. It is defined by the objective, not by a date, and monitoring shows how close it is.<\/div><\/details><details><summary>Is deep uncertainty an excuse not to act?<\/summary><div class=\"nx-answer\">No. Robust and adaptive methods are designed to support action when waiting for certainty would be costly.<\/div><\/details><details><summary>What is a signpost?<\/summary><div class=\"nx-answer\">An observable indicator that, if it changes, would prompt a planned change of strategy.<\/div><\/details><details><summary>Do these methods replace forecasts?<\/summary><div class=\"nx-answer\">No. Forecasts remain useful where they are reliable; deep uncertainty methods apply where they are not.<\/div><\/details><details><summary>Who reviews coverage of this topic?<\/summary><div class=\"nx-answer\">Published analysis is designed to be reviewed by a named subject reviewer, to cite its references and to include independent specialist voices. Where specialists disagree, the disagreement is reported rather than resolved by omission.<\/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 Foresight Nexus.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/globalriskforum.com\/foresight\/topics\/horizon-scanning\/\">Horizon scanning <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:\/\/globalriskforum.com\/foresight\/topics\/strategic-scenarios\/\">Strategic scenarios <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:\/\/globalriskforum.com\/foresight\/topics\/technology-futures\/\">Technology futures <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:\/\/globalriskforum.com\/foresight\/topics\/geopolitical-futures\/\">Geopolitical futures <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:\/\/globalriskforum.com\/foresight\/topics\/demographic-and-social-futures\/\">Demographic and social futures <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:\/\/globalriskforum.com\/foresight\/topics\/future-generations\/\">Future generations <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:\/\/globalriskforum.com\/foresight\/\">Foresight Nexus 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:\/\/globalriskforum.com\/foresight\/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:\/\/globalriskforum.com\/\">The Global Risks Forum 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:\/\/globalriskforum.com\/trust\/\">GRF 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\">Foresight Nexus<\/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. 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@globalriskforum.com?subject=Foresight%20Nexus%3A%20Deep%20uncertainty\">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:\/\/globalriskforum.com\/foresight\/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:\/\/globalriskforum.com\/foresight\/topics\/deep-uncertainty\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskforum.com\/foresight\/topics\/deep-uncertainty\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"When is uncertainty deep rather than ordinary?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"When credible experts cannot agree on the system model, the likelihood of key conditions, or how outcomes should be valued. 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