GCRI · Systems Science and Modelling
Systems Science and Modelling
The shared mathematical and engineering foundation for every Nexus field: how coupled systems are modelled, tested, validated and explained, so that a flood model, a grid model and a contagion model can be compared and combined honestly. Systems Science and Modelling is an established platform of The Global Centre for Risk and Innovation.
- STEM methods
- 8
- Canonical methods this field applies
- Topics
- 8
- Subject pages in the field
- Counterparts
- 6
- Platforms in shared fields across the network
- Report series
- 2
- Reports series it contributes to
Systems Science and Modelling in seven parts.
- 01What the platform doesA methods commons for modelling coupled systems honestly
- 02CycleHow work moves in this field
- 03OverviewWhat Systems Science and Modelling does
- 04Mandate and boundariesWhat Systems Science and Modelling provides, and who owns each model
- 05Across the domainsCoupled models are how the domains are joined
- 06Reports, standards and contributionsWhere modelling evidence goes
A methods commons for modelling coupled systems honestly.
The platform is designed as the community that owns the methods themselves: how models are compared, validated, coupled and reproduced across fields.
Model intercomparison
Structured comparison of models of the same system, and what their disagreement reveals. See model intercomparison.
Validation protocols
Methods for testing models against observations and stating uncertainty. See uncertainty and validation.
Coupled systems
Linking water, energy, food, health and infrastructure models without hiding assumptions. See coupled systems modelling.
Signature outputs are designed to include model intercomparison studies, validation protocols, method notes in the Reports Methods and Technical series, and open teaching material.
Coupled model runner.
The screen a team would use to follow this field: its measures, states and decisions. Names are examples and values are illustrative.
Coupled model runner
Coupled model A · Scenario set 3
Model coupling graph
nodes: models · edges: exchanged variables
Coupled components
| Hydrology | 1 day | runoff | Yes |
| Power system | 1 h | demand, outages | Yes |
| Economy | 1 quarter | output, prices | Partial |
| Population | 1 year | exposure | Yes |
Ensemble output
The questions this field works on.
Model intercomparison
Comparing models of the same system, and what disagreement tells you.
TopicUncertainty and validation
Quantifying uncertainty, validation against observations and the limits of calibration.
TopicCoupled systems modelling
Linking water, energy, food, health and infrastructure models without hiding assumptions.
TopicNetworks and cascades
Network science for dependency, contagion and cascading failure.
TopicAgent-based and complexity models
Behaviour, adaptation and emergence in social-technical systems.
TopicOptimisation under uncertainty
Robust and adaptive decisions when futures are deeply uncertain.
TopicDigital twins: method and limits
What a twin can and cannot represent, and how it is kept honest.
TopicReproducible modelling
Versioned models, data lineage and re-runnable results.
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction. The map on this page shows North America; the model itself is not tied to a place.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- 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
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Systems Science and Modelling in Foundry
- Tested at Systems Science and Modelling in Universe
- Codified in Trust, Verification and Assurance in Standards
- Published in Systems Science and Modelling in Reports
- Taught in Faculty of Risk Science and Methods in Academy
How work moves in this field.
A question becomes evidence, a governed build, an organised community, a controlled test, a reviewed publication and, finally, a decision taken by the competent authority under its own rules.
- Question Universe National, regional or institutional priority. Question in Systems Science and Modelling
- Evidence Foundry STEM foundation of the platform. Evidence in Systems Science and Modelling
- Build Foundry Foundry. Build in Systems Science and Modelling
- Organise Universe Nexus ecosystem. Organise in Systems Science and Modelling
- Test Universe Universe (independent site). Test in Systems Science and Modelling
- Publish Reports Reports. Publish in Systems Science and Modelling
- Decide Competent authority Competent authorities and institutions, through NXS-RLS (participants, agreements and handoffs). Decide in Systems Science and Modelling
- Renew Universe Corrections and the next annual agenda. Renew in Systems Science and Modelling
Each step of the cycle in the pillar it runs in; the decision stays with the competent authority, outside the programme.
- Labs
- Foundry
- Universe
- Nexus Standards
- Reports
- Risk Academy
- Competent authority (outside the programme)
Model Systems Science and Modelling as a system. Run the cascade.
Assembled from the Nexus Standards area profile Compound and cascading hazard standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Natech event
- Chemical spill or leak (set off downstream)
- Emergency-response overload (set off downstream)
- Toxic gas release (set off downstream)
- Industrial fire (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H107Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)NRS-H7Hazard family: Large-scale circulation anomalies (NHS-1 atmo.climate-mode)
Observation
- Copernicus EMS rapid mapping activations (listed for reference)
- OpenStreetMap planet and replication (listed for reference)
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- Direct economic loss attributed to disasters in relation to global GDP
- Number of disruptions to basic services attributed to disasters
Example geography
- Bangladesh: 134 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
What Systems Science and Modelling does.
Systems modelling is the practice of representing complex systems in mathematical and computational models to understand, predict and test decisions. Systems Science and Modelling covers intercomparison, uncertainty and validation, coupled systems, networks and cascades, agent-based models, optimisation under uncertainty, digital twins and reproducible modelling. Model owners and users decide.
Its community
Systems scientists; modellers; statisticians; complexity scientists; operations researchers; data scientists; model validators.
Its methods
Systems engineering; uncertainty quantification; network and complex-systems modelling; optimisation; model validation and intercomparison.
Its outputs
Model intercomparison studies; validation protocols; method notes in Reports Methods and Technical; open teaching material.
How Systems Science and Modelling is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Model, equations and assumptions
The model structure used for Systems Science and Modelling, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityEAL = ∑k pk · Lkover events k with annual probability pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
System model
The couplings between the systems in Systems Science and Modelling: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What Systems Science and Modelling provides, and who owns each model.
Systems Science and Modelling, is the shared methods foundation: how coupled models are built, tested, validated, compared and explained across the network. Model owners and the institutions that use models decide how to use them; the platform vouches for no model and publishes the evidence on each.
Model diagrams and outputs here are illustrations. Validation results are published with the conditions they hold under.
Hazards and exposure in North America.
Population density, flood water and night lights over North America, 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
- 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
- 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
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. 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
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- 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 North America.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 JPL OPERA DIST-ALERTSources 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
- 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
- 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
- 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 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
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into 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.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). 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
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
Coupled models are how the domains are joined.
- WaterHow this field couples to itHydrological models supply inflows to power and irrigation models.
- EnergyHow this field couples to itGrid models need weather, water and demand inputs from other models.
- FoodHow this field couples to itCrop models couple weather, soil and management.
- HealthHow this field couples to itEpidemic models couple behaviour, mobility and health system capacity.
- BiodiversityHow this field couples to itEcosystem models link land use, climate and species.
Where modelling evidence goes.
Intercomparisons and validation studies are designed for the Methods and Technical series. Models would carry cards and versioned code under NS-12 of the proposed Nexus Standards.
- Modellers can join intercomparison exercises.
- Researchers can contribute validation datasets.
- Reviewers can audit model documentation.
Models of connected systems are built in isolation.
Every field in the Nexus Consortium relies on models. Hydrologists model river flows, power engineers model grids, epidemiologists model transmission, economists model markets and catastrophe modellers model losses. Each discipline has developed its own methods, data conventions and standards of evidence, and many of these models are excellent within their scope.
The difficulty begins when systems interact. A heatwave raises electricity demand, lowers the efficiency of power plants, reduces river flows used for cooling, increases hospital admissions and strains supply chains, all at once. Understanding that event requires linking models that were never designed to work together. Their time steps, spatial scales, units and assumptions differ. Uncertainty in one model propagates into the next, often unrecorded. When outputs are passed between disciplines, assumptions can be lost and confidence can be overstated.
The consequences are practical. Decisions on infrastructure, public health and finance increasingly rest on integrated models and digital twins. If those models hide assumptions, cannot be reproduced, or report false precision, decision-makers may be confident about the wrong things. Cascading failures are precisely the phenomena that isolated models miss and poorly coupled models misrepresent.
How the platform anchors the STEM Commons and connects across the Consortium.
The platform is the steward of six STEM methods in the GCRI STEM Commons: systems engineering, probability and statistics, uncertainty quantification and model validation, network science and contagion, complex systems and agent-based modelling and optimisation and operations research. It also applies decision analysis under uncertainty and high-performance, cloud and edge computing. Its STEM applications show how they combine.
Within GCRI, it works with Labs on experimental protocols and with Systemic Risk Management on systemic risk methods, and it wires into the Foundry systems NXS-DSS (decision cases and deliberation) and NXS-LAB (research, experiments and simulations). At The Global Risks Alliance (GRA), Actuarial and Quantitative Risk is its counterpart for quantitative methods in financial risk, and Financial Stability applies network methods to the financial system. At The Global Risks Forum (GRF), the Research Nexus and Foresight Nexus convene dialogue on evidence and futures. See all counterparts.
Method notes are designed for the Reports Methods and Technical series and Scenario and Stress-Test series, and the platform designs a Universe exercise on cross-system model integration, described on its Universe page.
The areas of methods and modelling this platform takes on.
Systems Science and Modelling is the shared methods foundation of the network: how coupled systems are modelled, tested, validated and explained, so that a flood model, a grid model and a contagion model can be compared and combined honestly. Its pages cover model intercomparison, uncertainty and validation, coupled systems, networks and cascades, agent-based and complexity models, optimisation under uncertainty, digital twins and reproducible modelling, with every value labelled by its evidence and every limit stated. Model owners and the institutions that use models decide.
Model intercomparison
Running different models on common experiments to see where they agree, differ and why.
Uncertainty and validation
Stating how far model results can be trusted, and testing them against independent evidence.
Coupled systems modelling
Linking models of water, energy, food, health, economy and infrastructure so their interactions show.
Networks and cascades
Representing dependencies as networks and simulating how failures spread across them.
Agent-based and complexity
Models built from interacting agents whose behaviour produces system-level patterns.
Optimisation under uncertainty
Choosing plans and designs that perform acceptably across many possible futures.
Digital twins
Models continuously updated with data from a real system to support monitoring and decisions.
Reproducible modelling
Versioned code, data and documentation so that models can be rerun, checked and reused.
Built on named science, technology, engineering and mathematics.
Every claim on Systems Science and Modelling rests on methods with a canonical page in the STEM Commons, so readers can see what a finding depends on and where its limits lie.
How this works across the six pillars.
Systems Science and Modelling works through six pillars of one annual programme, led by the Nexus Consortium, with GCRI, GRF and GRA each contributing in its role: explore, experiment and prototype in Labs, build, simulate and prepare in Foundry, convene and test in Universe, codify, correct and apply in Nexus Standards, publish in every format in Reports, teach in the open in Risk Academy.
handoff in playrecorded handoffno direct handoff
Programme yearAll phases
Route
The whole programme
Every pillar hands its work to another; together they close the year’s feedback loops.
L · F · U · S · R · A
- Pillars
- 6 of 6
- Handoffs
- 18 of 18
- Loops
- 17 closed
Handoffs in play
- F01Universe → Foundry · Admitted mission becomes a cycle project with a decision caseReadiness preparation
- F02Foundry → Universe · Core Build Report and go or no-go evidenceControlled build
- F03Foundry → Reports · Sealed observations and indicator series behind a profileCloseout
- F04Universe → Reports · After-action and scenario findings for chapters 10 and 13After-action review
and 14 more
- LabsWhere questions become experiments and prototypes: new methods, data and tools are tried in the open, with their limits on record, before the Foundry builds them, under the Nexus Consortium.Labs
- FoundryWhere methods, data and models become reproducible builds, each run with its inputs, version and limits on record.Systems Science and Modelling in FoundryFoundry
- UniverseWhere a build is tested under controlled conditions with independent evaluators, so a method’s skill is measured, not assumed.Systems Science and Modelling in Universeuniverse.nexus
- Nexus StandardsWhere methods, identifiers and data schemas are codified in the open and corrected when a test finds a fault.Trust, Verification and Assurance in Standardsnexustandards.com
- ReportsWhere findings are published with their method, uncertainty and evidence class, and corrected as new versions.Systems Science and Modelling in ReportsReports
- Risk AcademyWhere methods and systems are taught on real data and real places, by working the case, on Risk Academy.Faculty of Risk Science and Methods in Academyrisksacademy.com
What the work is read against.
The platform does not set standards. It reads its evidence against the frameworks that competent bodies have adopted, so a finding can be placed where a decision-maker already works.
| Framework or standard | Body | What it sets |
|---|---|---|
| ASME V&V 10, V&V 20 and V&V 40 | American Society of Mechanical Engineers | Standards for verification and validation of computational models and assessing their credibility |
| NASA-STD-7009 Standard for Models and Simulations | US National Aeronautics and Space Administration | Requirements for documenting and assessing the credibility of models and simulations |
| ISO/IEC/IEEE 15288 Systems and software engineering: system life cycle processes | ISO, IEC and IEEE | Processes for engineering systems across their life cycle |
| ODD protocol for describing individual- and agent-based models | Grimm and colleagues, maintained by the modelling community | A standard structure for documenting agent-based models |
| TRACE documentation framework | Ecological modelling community | Transparent and comprehensive documentation of model development, testing and analysis |
| CMIP and ISIMIP intercomparison protocols | World Climate Research Programme; Inter-Sectoral Impact Model Intercomparison Project | Common experiment designs and data standards for model intercomparison |
| IPCC guidance note on consistent treatment of uncertainties | Intergovernmental Panel on Climate Change | Calibrated language for confidence and likelihood in assessments |
| FAIR principles for research software (FAIR4RS) | Research Data Alliance, FORCE11 and ReSA | Principles for making research software findable, accessible, interoperable and reusable |
| ISO 23247 Digital twin framework for manufacturing | International Organization for Standardization | A reference framework for digital twins of manufacturing systems |
The same field, seen three ways.
GCRI studies a field as science, GRF governs it and GRA builds shared financial understanding of it. Systems Science and Modelling links to its counterparts so that work on one side is visible on the others.
- Research NexusInstitutionGRFShared fieldResearch and evidence
- Policy NexusInstitutionGRFShared fieldRisk management and systemic risk
- Foresight NexusInstitutionGRFShared fieldRisk management and systemic risk
- Governance NexusInstitutionGRFShared fieldRisk management and systemic risk
- Financial StabilityInstitutionGRAShared fieldRisk management and systemic risk
- Actuarial and Quantitative RiskInstitutionGRAShared fieldMethods and modelling
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
- ASME V&V 40-2018, assessing credibility of computational modelling through verification and validation (ASME)
- NASA-STD-7009, Standard for Models and Simulations (NASA)
- Grimm and others, Towards better modelling and decision support: documenting model development, testing and analysis using TRACE, Ecological Modelling 280 (2014)
- ISO 23247-1:2021 Digital twin framework for manufacturing (ISO)
- Nexus Standards, the proposed NS series (nexustandards.com)
Pages in Systems Science and Modelling.
Mandate and publisher
PageField overview
PageResearch and evidence
PageInnovation and practical technology
PageProgrammes and projects
PageData and technical methods
PageSpecialist community
PageLearning and professional development
PageEvents and technical dialogues
PageResearch and publications
PageParticipation paths
PageContact the specialist team
PageTrust, rights and status
PageSTEM foundation of Systems Science and Modelling
PageHow work moves in this field
PageBuild in Foundry
PageTest in Universe
PagePublish in Reports
PageThe same field across the Consortium
PageTopics
Where this page leads across the Consortium.
Questions and answers.
Who publishes Systems Science and Modelling?
Is Systems Science and Modelling active?
How can I contribute?
Where are findings published?
Which values on this platform are real?
How do I report an error?
Why does the Consortium need a modelling platform separate from the field platforms?
Does the platform build models for other platforms?
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
How Systems Science and Modelling works.
Choose a view and press Play to follow the platform’s process, its programme year, the standards it applies and how its findings are published. The outline shows how the work is designed to run, not the status of any decision.
Systems Science and Modelling
How Systems Science and Modelling works
Platforms · Programme year · Standards · Reports
A platform joins a field to named methods, labelled evidence and the outputs it publishes, without becoming the authority it serves.
01 Scope
- 01Field and familiesStart · The platform leadThe home states the field, its families and who it serves.
- 02Topic dossiersRecord · Platform authorsTopics explain the subject with labelled evidence.
02 Evidence
- 03Evidence labelsRecord · GCRIEach value is labelled: observed, published statistics, estimated, modelled, scenario or unknown.
- 04MethodsStep · Method ownersMethods come from the STEM Commons, each with one canonical page.
03 Work
- 05Scenarios on the systemsModel · Foundry pillarScenarios run on the Nexus systems, labelled as scenario.
- 06Standards that applyRecord · Nexus StandardsThe platform names the standards that apply. A validator report is evidence for review.
04 Publish
- 07EditionsRecord · ReportsFindings are published through Reports with their limits.
- 08Authority decidesOutcome · The competent authorityDecisions and execution stay with the government, operator or licensed institution.
Systems Science and Modelling home · Platforms · Shared methodsAn outline of how the work is designed to run; it does not report the status of any decision or record.
Take part in Systems Science and Modelling.
Start from who you are. Answer an open call. Or form and lead a competence cell on the Systems Science and Modelling platform.
National institutions
Ministries, statistics offices and agencies: a national desk and chapter, and a seat in the working groups. National chapters
Sectors and professions
Operators, regulators, banks, insurers and professional bodies whose work touches Systems Science and Modelling. Participate
Learning
Courses and practice, with a record of every course completed. Free to study. Open learning
Standards
Draft, review and adopt the methods and data standards with the working groups. Working groups
Communities and research
Universities, civil society and individual experts: projects, data and review. Write to the team
| Open call | Seeks | Closes |
|---|---|---|
| Founding call for experts to the working groups CALL-FOUNDING-EXPERTS | expert, editor, chair | 2026-12-15 |
| Open nomination for the three independent expert seats of the Global Standards Council CALL-GSC-IND | council-member | 2026-12-01 |
| Nominations of interim regional members (applies only if the transitional provision is decided) CALL-GSC-REG | council-member | 2026-12-15 |
| Review of the wave-1 risk core outlines (C1, C2, C3, C4, C8, C10, C11) CALL-REVIEW-WAVE1 | contributor | 2027-01-31 |
| Experts for the core standards of risk science: uncertainty and evidence grading, scales, structured expert judgement, tolerability, extremes and compound events, catastrophic and existential risk, ethics and equity, units and measurement (NRS-C17 to NRS-C24) CALL-CORE-SCIENCE | expert, editor, chair | 2027-01-31 |
| Independent reviewers for the families of the hazard atlas, three from at least two regions for each family (NHS2-R19), priority families first CALL-ATLAS-REVIEW | expert | 2027-03-31 |
| Members of the interim Frontier Safety Review Panel and red-team reviewers (biosafety and biosecurity, AI safety and security, critical-infrastructure security); applications treated in confidence CALL-FRONTIER-SAFETY-REVIEW | expert | 2026-11-30 |
| Experts in ontology, metrology, software verification and conformity assessment for the requirement register, the NHS-2 machine representation, the conformance test suites and the conformity assessment scheme CALL-CONFORMANCE-ONTOLOGY | expert, editor | 2027-01-31 |
Calls past their closing date are hidden automatically. Every call, proposal and route: open areas and calls.
Gather the cell
Bring together three to seven people with complementary skills: data, field practice, policy and communication.
Register it
Write to the team with the cell’s name, members, area and region. You receive a reference and a contact. Write to the team
Choose a charter
Pick one focus: research and evidence, data and maps, standards review, or community preparedness.
Learn together
Follow shared study paths at Risk Academy, with practice and a record of every course completed. Hazards and resilience
Lead and report
Hold regular sessions. Publish what the cell learns through Reports. Bring findings to the working groups. Working groups
What a national team or a local community can do on The Global Risks Index (NXS-GRX) today.
Your country’s profile
Open risk profiles for 195 countries, with sources and licences on every value. Open the Index
Correct or reply
Correct a value or reply for your country. The change is logged with its effect. Open the Index
Offer data
Offer a dataset. It enters through the licence gate, with your credit. Open the Index
Every figure on this page is indexed: hazards, climate, conflict, North America.
The Index is the Consortium’s indexing system for risk data. Every indicator, hazard, series and country profile carries its Nexus Standards identifier, its source and its licence, so what this page shows can be traced, corrected and reused.
- 01 · Sources205 registered sourcesEvery value keeps its source and licence; withheld values are counted.Data and licences
- 02 · Nexus StandardsNIS-1 and NHS-1451 indicators, 905 hazards; every record carries a UNI.Nexus Standards
- 03 · The Index54 series, 195 country profilesComputed on the standards; no value is published until an edition is released.The Global Risks Index
- 04 · This pageSystems Science and ModellingMaps, figures and feeds read the Index in the context of hazards, climate, conflict and North America.Public API
Series in this context hazards, climate, conflict
- GLBGlobal SystemsThe state of interdependent global risk: the twenty domain indices aggregated to the world, the five core systems (water, energy, food, health, biodiversity) and their couplings, the global tail register and the gap…NXS-GRX-AR-000002Hazards · Climate · Conflict
- THDisaster Risk Intelligence (DRI)Signals, observations, event streams, geospatial layers and scenario packs turned into structured risk intelligence for preparedness and planning.NXS-GRX-AR-000111Hazards · Climate · Conflict
- GSGlobal CitiesUrban exposure, heat, flooding, informal settlement, service dependency and the economic weight of cities at risk.NXS-GRX-AR-000068Hazards · Infrastructure · Climate
- GSGlobal ClimateChronic and acute physical climate hazard, transition exposure, adaptation gaps and scenario pathways to 2030, 2050 and 2100.NXS-GRX-AR-000065Climate · Hazards · Finance
Country profiles North America
Every country profile carries the source and licence of each value. Correct a value or reply
Hazard domains NHS-1
- Atmosphere and weatherNXS-GRX-AR-000135
- Solid EarthNXS-GRX-AR-000138
- Systemic and compound eventsNXS-GRX-AR-000148
Geodata and API open
Open layers on an equal-area hexagon grid, shown as densities per km² or as symbols: People · GHSL 2025People · WorldPop 2025Built-up areaPortsAirportsRoadsRailwaysRivers and lake centrelines
/api/v1/public/grix · https://nexustandards.com/uni/{uni}
Developers · Data · Made with Natural Earth, Public domain; GHSL and WorldPop, CC BY 4.0
Snapshot of the Index registries pinned 2026-10-23 · 22c6eed. Research and risk-analytics information: not a rating, an official classification, a warning, a benchmark or advice.
Bring a method, a dataset or a question.
Information and enquiries are open today. 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.
Legal documents.
Maps, data and figures on this site are shown for context and general information. They are not advice, forecasts or warnings; the limits of the information are set out in the Disclaimers and Data Notices.