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Systems Science and Modelling · Dependencies and consequences

Dependencies and consequences

Q: How is model failure reported?

Through the correction routes on the platform's trust page , with dated corrections kept visible.

The interdependence of this field with others is unusual: systems science is not one system among many, but the method through which the links between all other systems are represented. Its strengths and weaknesses propagate into every field that uses it. This page sets out how the field depends on others, how others depend on it, and the consequences when modelling goes wrong, as part of the field overview.

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Publisher: The Global Centre for Risk and Innovation
Field: Methods and modelling, Risk management and systemic risk, Research and evidence
Status: In development
Service level: R0: information and enquiries

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Hazards and exposure, for this page

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.

For dependencies and consequences, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, flood water and night lights with relief in 3D, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · Copernicus Sentinel-2 L2ASources 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 H3 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 H3 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 H3 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 H3 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 H3 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 H3 cells where shown (modified). 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 H3 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 H3 cells where shown (modified). 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 H3 cells where shown (modified). ODbL
Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 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.
Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
Observed (EO): Copernicus Sentinel-2 L2A (contains modified Copernicus Sentinel data 2026), Microsoft Planetary Computer
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.

Layers and dates

True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
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
Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
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

STEM methods: 8; Canonical methods this field applies
Topics: 8; Subject pages in the field
Counterparts: 6; Platforms in shared fields at GCRI, GRF and GRA
Report series: 2; Nexus Reports series it contributes to

01In brief

The short answer.

Dependencies and consequences in Systems Science and Modelling describe how methods and modelling, risk management and systemic risk, and research and evidence connect to other systems through physical, economic, social and ecological links, and how stress travels along them toward fields such as those of Research Nexus and Policy Nexus. Every dependency map states its assumptions, data and uncertainty; it describes connections, not a forecast.

02Canvas

One field, three institutions.

Canvas · one field, three institutionsDesign, not a record

Fig.How GCRI, GRF and GRA share one field while the authorities decide. Design, not a record.

03Console

Field overview, in outline.

The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.

Field overview: the screen's structure, terms and states for Systems science.Placeholder names and illustrative values; a design, not a record or a live system.

04What the field depends on

Inputs that models cannot do without.

Dependency	Why it matters	Where it is studied
Domain science	Models are only as good as the understanding of processes they encode	GCRI field platforms such as Water Nexus and Climate and Hazards
Observations	Calibration and validation require data	Space and Earth Observation and Data and Compute
Computing	Large ensembles and coupled models need compute	Computing in the STEM Commons
Experiments	Testbeds provide controlled evidence	Nexus Labs

05What depends on the field

Where model quality travels.

Fields

Every platform

Each Nexus field uses statistics, networks, optimisation or simulation.

Systems

Foundry tools

The Nexus Foundry DSS system relies on validated models and stated uncertainty.

Finance

Risk models

Financial risk models share methods, developed with GRA's Actuarial and Quantitative Risk.

06Consequences of failure

What happens when modelling goes wrong.

False precision: results reported without uncertainty lead decision-makers to overconfidence.
Hidden assumptions: when models are coupled, assumptions in one can silently constrain another.
Common-mode error: when many institutions use similar models, they may share the same blind spots, a concern also studied by Financial Stability.
Irreproducibility: results that cannot be re-run cannot be checked or corrected. See reproducible modelling.
Misplaced trust in twins: digital replicas taken as reality rather than representations. See digital twins: method and limits.

07Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

Term	Definition
Interdependency	A two-way dependency between infrastructures, such as power needing telecoms for control while telecoms need power to run.
Cascading risk	Risk that a failure in one system or component triggers failures in others that depend on it.
Compound event	Two or more hazards or drivers occurring together or in sequence whose joint impact exceeds what either would cause alone, such as drought followed by intense rain; IPCC assessments use the term.
Dependency mapping	Tracing the people, processes, technology, facilities and suppliers a service relies on.

08Feedback with society

Models change the systems they describe.

Models are not neutral observers. When a flood model informs zoning, it changes where people build; when a market model is widely used, it can change how markets behave. Social scientists call this reflexivity. It means that models of social-technical systems must be revisited as their use changes the system, a theme explored in agent-based and complexity models.

At The Global Risks Forum (GRF), the Policy Nexus convenes dialogue on how model evidence enters policy decisions.

09Questions

Questions and answers.

Why is this field described as a dependency of every other field?

Because every field relies on its methods, so improvements and errors in modelling methods propagate across the Consortium.

What is common-mode model error?

Error that many users share because they use similar models or assumptions, so their mistakes are correlated rather than independent.

How is model failure reported?

Through the correction routes on the platform's trust page, with dated corrections kept visible.

How does the platform reduce common-mode error?

By comparing independently built models, documenting shared components and assumptions, and reporting where models agree only because they share the same lineage.

Around Systems Science and Modelling.

Systems Science and Modelling

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