GRF · Foresight Nexus
Strategic scenarios
Scenarios are structured descriptions of plausible alternative futures, built so that an institution can test its strategy against conditions it does not expect. They are not forecasts and carry no probability. Their worth lies in widening the range of futures that decision-makers take seriously, exposing hidden assumptions and preparing options in advance. This page sets out how scenario work is designed, how it succeeds or fails inside institutions, and who uses it.
Strategic scenarios on the record.
Strategic scenarios: how the risk develops.
Plausible alternative futures. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Drivers and critical uncertainties
Scenario design starts by separating predetermined elements, such as cohorts already born, from critical uncertainties whose direction is open and whose impact is large. The choice of uncertainties shapes everything that follows, so it is documented and challenged.
Divergent, consistent futures
Each scenario follows a coherent logic from today to the horizon. Consistency checks remove combinations that cannot coexist; divergence ensures the set spans futures that would call for different responses.
Testing strategy, not choosing a future
Policies and plans are wind-tunnelled against every scenario. A strategy that works in only one future is fragile; one that holds across the set, or can be adapted in time, is robust.
Capture and fatigue
Scenarios fail when they are written to confirm an agreed plan, when a preferred scenario quietly becomes the forecast, or when the set is never refreshed. Ownership by a decision-holder and a review schedule keep them in use.
Hazards and exposure in the Arctic.
Daytime surface heat, population density and rain over the Arctic, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Hazards and exposure, for this page
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- GPM IMERG (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite estimate, near real time
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds and overpass timing leave gaps)
- Source
- VIIRS NOAA-20 375 m (FIRMS) (NASA LANCE FIRMS)
- Provider
- NASA LANCE FIRMS
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds and overpass timing leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 500 m grid
- Uncertainty
- Satellite hot-spot detections for the day shown, including gas flares and industrial heat; not a confirmed fire, an incident report or a warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- GHRSST MUR25 (NASA JPL PO.DAAC)
- Provider
- NASA JPL PO.DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite analysis
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite analysis of how far sea temperature departs from its usual value for the date shown; a marine heat indicator, not a coral bleaching alert.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Hazards and exposure, framed on the Arctic.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NASA GPM IMERG EarlySources and licences
- 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © 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.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Live: NASA GPM IMERG Early. 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).
- NASA GIBS: 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).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (11 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.
Layers and dates
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026
What is measured, and how well.
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.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Scenario sets in current use by government | count of published sets | annual | Published foresight reports | Published statistics |
| Policies and strategies tested against a scenario set | count per planning cycle | per cycle | Cabinet and ministry planning records | Published statistics |
| Interval between scenario refreshes | months | per set | Publication record | Published statistics |
| Participant range in scenario exercises | persons by sector, region and age group | per exercise | Workshop records | REPORTED |
| Signposts tracked against each scenario | count of signposts | quarterly | Signpost register | REPORTED |
| Strategies citing scenario evidence | share of strategy documents | annual | Document analysis | ESTIMATED |
Policy wind tunnel.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Policy wind tunnel
Strategic scenarios · Scenario set B · Policy: POL-56
Policy performance by scenario
0 fails to 100 robust
Higher is more robust. Example scores.
Signposts to monitor
- S-1Trade restrictions on inputstriggers adaptation 2quarterly
- S-2Cost of sensorstriggers scale-upannual
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| IPCC AR6 Scenario Database (hosted by IIASA) | Quantified emissions, energy and land pathways from integrated assessment models assessed by the IPCC | Free with attribution, database licence terms |
| SSP Database (IIASA) | Population, urbanisation and economic projections for each shared socioeconomic pathway | Free with registration, IIASA terms |
| IEA World Energy Outlook scenarios | Stated policies, announced pledges and net zero pathways for energy supply and demand | IEA terms, part free and part licensed |
| NGFS climate scenarios | Transition and physical risk scenarios used by central banks and supervisors | Free with attribution |
| US National Intelligence Council Global Trends | Long-range scenarios on global structural forces, published each presidential term | Public domain, US government work |
| UK Ministry of Defence Global Strategic Trends | Long-horizon trend analysis and alternative futures for defence and security planning | Open Government Licence |
Compare instruments: rules, incentives and information.
Three families of instruments side by side against the baseline, each with its own lag and adoption speed, under the coverage and compliance you set; effect sizes are teaching values to replace with evidence. Scenario: named assumptions about the future, not a forecast. The map on this page shows The Arctic; the model itself is not tied to a place.
Controls
- Baseline trend of the outcome
- 2.0% a year
- Coverage of the instrument
- 70%
- Compliance
- 75%
- Effect size, against the teaching value
- 100%
- Years ahead
- 20 years
Readouts
- Largest reduction at the horizon
- Regulation
- Its reduction against the baseline
- 21%
Numbers, methods and sources
| Year | Baseline | Regulation | Economic incentive | Information and capacity |
|---|---|---|---|---|
| 0 | 100.0 | 99.5 | 98.8 | 99.1 |
| 1 | 102.0 | 101.2 | 100.1 | 99.8 |
| 2 | 104.0 | 102.5 | 101.1 | 99.9 |
| 3 | 106.1 | 103.5 | 101.6 | 100.0 |
| 4 | 108.2 | 103.7 | 101.8 | 100.7 |
| 5 | 110.4 | 103.2 | 101.7 | 102.1 |
| 6 | 112.6 | 102.0 | 101.6 | 103.9 |
| 7 | 114.9 | 100.4 | 101.6 | 105.9 |
| 8 | 117.2 | 99.2 | 102.1 | 108.0 |
| 9 | 119.5 | 98.6 | 102.9 | 110.1 |
| 10 | 121.9 | 98.9 | 104.2 | 112.3 |
| 11 | 124.3 | 99.7 | 105.7 | 114.5 |
| 12 | 126.8 | 101.1 | 107.4 | 116.8 |
| 13 | 129.4 | 102.7 | 109.4 | 119.2 |
| 14 | 131.9 | 104.5 | 111.4 | 121.6 |
| 15 | 134.6 | 106.5 | 113.5 | 124.0 |
| 16 | 137.3 | 108.5 | 115.7 | 126.5 |
| 17 | 140.0 | 110.7 | 118.0 | 129.0 |
| 18 | 142.8 | 112.9 | 120.4 | 131.6 |
| 19 | 145.7 | 115.1 | 122.8 | 134.2 |
| 20 | 148.6 | 117.4 | 125.2 | 136.9 |
Methods
- Tools of government Hood (1983), The Tools of Government, Macmillan
- Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
- Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Borders Natural Earth
- Cities Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Foresight Nexus in Foundry
- Tested at Foresight Nexus in Universe
- Codified in Systemic Risks in Standards
- Published in Foresight Nexus in Reports
- Taught in Faculty of Risk Science and Methods in Academy
Who decides, and what the evidence serves.
Foresight Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Cabinet offices and prime ministers’ strategy units | Whether a national strategy holds across plausible futures, or needs contingent options | Scenario sets with their uncertainties and assumptions, wind-tunnel results and the signposts to watch |
| Central banks and prudential supervisors | Which climate and macro-financial scenarios to use in supervisory exercises | Scenario narratives, their parameter choices and their coverage of severe but plausible conditions |
| Ministries of defence and foreign affairs | Which capabilities and partnerships remain sound across alternative security environments | Alternative futures with the drivers that separate them and the institutional responses each calls for |
| Energy ministries and system operators | Which investments hold across energy transition pathways | Published pathways compared on demand, technology and policy assumptions |
Outputs this topic produces
- Foresight outlooks with scenario sets, drivers and assumptions stated
- Scenario learning protocols for workshops and policy wind-tunnelling
- Signpost registers that track which scenario conditions are emerging
- Comparisons of scenario sets published by different institutions
From mandate to evaluation, by pillar.
The steps an instrument takes from mandate to evaluation, each with the pillar that runs it, and the decisions that stay with the authority. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Arctic; the model itself is not tied to a place.
- Mandate and problem framing Competent authority (outside the programme)
A competent authority frames the problem and the mandate under its own rules. Howlett, Ramesh and Perl (2009) - Evidence and system model Foundry
The system is modelled with its feedbacks, data and stated limits. OECD (2014), principle 2 - Options appraised with scenarios Foundry
Instruments are compared under scenarios, with uncertainty shown. Hood (1983) - Exercise with stakeholders Universe
The options are tested in an exercise with those who would implement and bear them. OECD (2014), principle 3 - Standard or guidance drafted Nexus Standards
What works is codified as a standard or guidance, open to comment. Nexus Standards process - Decision by the authority Competent authority (outside the programme)
The authority decides, outside the programme, with the evidence on record. Howlett, Ramesh and Perl (2009) - Capacity and training Risk Academy
Those who implement are trained on the decided instrument and its data. Sendai Framework (2015), priority 2 - Monitoring and public report Reports
Indicators are published with sources, limits and corrections. OECD (2014), principle 5 - Evaluation and revision Nexus Standards
Results return to the standard and the model: the cycle closes. Howlett, Ramesh and Perl (2009)
Methods
- Policy cycle Howlett, Ramesh and Perl (2009), Studying Public Policy, Oxford University Press
- Governance of critical risks OECD (2014), Recommendation of the Council on the Governance of Critical Risks
- Layered drawing of directed graphs Sugiyama, Tagawa and Toda (1981), IEEE SMC 11
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Foresight Nexus in Foundry
- Tested at Foresight Nexus in Universe
- Codified in Systemic Risks in Standards
- Published in Foresight Nexus in Reports
- Taught in Faculty of Risk Science and Methods in Academy
What practitioners ask.
- Which driving forces are both highly uncertain and highly important for the question at hand?
- How many scenarios are useful, and how different should they be?
- How can scenarios include surprises and discontinuities without becoming implausible?
- How should quantitative models and qualitative narratives be combined?
- How can scenarios be used to test strategies rather than simply describe futures?
Exercise a cascade in strategic scenarios with the people who decide.
Assembled from the Nexus Standards area profile Future generations and long-term risk: the hazards it names, the systems and communities that carry the loss, the controls and institutions that dampen it and the data that observe it. Pick an initiating event and step through the cascade to see where mandates meet and which decisions fall to whom. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise for dialogue, not a forecast, an assessment or a position. To model a place of your own choosing, open the full System Builder.
Hazards
- Nuclear war
- Dam failure
- Financial contagion
- Cryptographically relevant quantum computer
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Nuclear detonation (set off downstream)
- Health-system overload (set off downstream)
- Data breach (set off downstream)
- Mass account takeover (set off downstream)
- Harvest-now, decrypt-later collection (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Sovereign debt crisis (set off downstream)
- Currency crisis (set off downstream)
- Nuclear winter (set off downstream)
- High-altitude electromagnetic pulse (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-13Standards development process (open proposals, review, versioning, adoption)NRS-U12Universe platform profile: Foresight NexusNRS-H26Hazard family: Land and wetland degradation (NHS-1 eco.land)NRS-H92Hazard family: Ageing and degraded infrastructure (NHS-1 infra.ageing)NRS-P4Foresight, horizon scanning and strategyNRS-H11Hazard family: Coastal and sea-level hazards (NHS-1 water.coastal)NRS-H31Hazard family: Persistent and bioaccumulative substances (NHS-1 poll.persistent)
Observation
- Our World in Data datasets (listed for reference)
- CMIP6 climate projections (Copernicus Climate Data Store) (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Number of countries with mechanisms in place to enhance policy coherence of sustainable development
Example geography
- Bangladesh: 134 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
Drivers and dynamics.
Scenario practice has deep roots in military planning and corporate strategy, where it helped organisations prepare for shocks that conventional forecasts missed. A common approach identifies the critical uncertainties, the forces that matter most and are least predictable, and builds contrasting futures around them. Other approaches start from a desired or feared end state and work backwards, or develop scenarios through participatory processes with stakeholders.
Scenarios change decisions when they are developed with the people who will use them, when they are challenging but plausible, and when they are used to test specific strategies. They fail when they are too close to the planning baseline an institution already uses, when one scenario is treated as the preferred future, or when they are produced by outsiders and handed over without engagement.
Why now: institutions face compound shocks that single forecasts did not anticipate, and scenario sets are increasingly used to test strategies. Foresight Nexus builds scenario sets with the people who will use them, and those scenarios set the conditions for Universe exercises, always labelled as scenarios.
How strategic scenarios is governed: instruments, mandates and commitments.
The instruments, mandates, actors and accountability arrangements a policymaker, diplomat or development agency works with, citing the agreements that set them. Structures follow the frameworks named. Any state or progress drawn is illustrative, not a record.
Policy instrument matrix
The kinds of instrument available on strategic scenarios, by level of government and cooperation.
| Instrument type | Local | National | Regional | Global |
|---|---|---|---|---|
| Regulatory | Zoning, building codes | Laws, standards, licensing | Harmonised rules | Conventions and treaties |
| Economic | Local levies, grants | Budget lines, taxes, insurance schemes | Risk pools, regional funds | Financing mechanisms |
| Information | Local warnings | Disclosure, early warning, statistics | Data sharing arrangements | Global monitoring and reporting |
| Institutional | Local committees | Lead agencies, coordination councils | Regional bodies and secretariats | Multilateral mandates |
| Cooperative | Community agreements | Partnership compacts | Memoranda and joint programmes | Agreements and joint commitments |
How it is approached.
- 01
Driver analysis
Structured identification of forces, ranked by importance and uncertainty.
- 02
Narrative development
Coherent, internally consistent stories that describe how each future could emerge.
- 03
Quantified scenarios
Models from scenario design and stress testing and complex systems and agent-based modelling add consistency and scale where data allows.
- 04
Limits
Scenarios reflect the imagination and biases of those who build them, cannot be validated in advance, and can be misread as forecasts if not carefully labelled.
How it is studied, and what the methods cannot say.
- 01Scenario design and stress testingExploratory scenario development · Policy wind-tunnellingApplied here
- 02Decision analysis under uncertaintyRobust decision making · Dynamic adaptive policy pathways · Structured expert elicitationApplied here
- 03Complex systems and agent-based modellingSystems mapping of drivers · Agent-based exploration of social changeApplied here
- 04Forecasting and time-series analysisAggregated judgemental forecasting · Probabilistic population projectionsApplied here
Where this page leads across the Consortium.
Questions and answers.
Why do scenarios not say which future is most likely?
How many scenarios should a set contain?
What is wind-tunnelling?
Are climate pathways the same as strategic scenarios?
Why not just pick the most likely scenario?
Are scenarios the same as stress tests?
How many scenarios should there be?
Who reviews coverage of this topic?
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