Spatial financeWhere capital meets hazard, exposure and transition
Complex systems and agent-based modelling for financial stability
Complex systems science studies how interactions among many agents produce system behaviour that no single agent intends: tipping points, feedback loops and sudden regime shifts. On this platform it explains why the financial system can move from calm to crisis quickly.
What complex systems and agent-based modelling does here.
Complex systems and agent-based modelling study systems whose behaviour emerges from interactions among many components. They help explain nonlinear dynamics, tipping points and collective behaviour. In financial stability, risk management and systemic risk and capital markets and investment, complex systems approaches explore how interacting risks can produce sudden, systemic shifts. They support stress testing of policies against emergent behaviour. The canonical description is on the STEM Commons.
Agent-based market models
Heterogeneous traders, funds and dealers are simulated to study how margin rules, leverage limits and liquidity tools change the dynamics of stress.
Early warning of regime shifts
Indicators such as rising autocorrelation and variance are tested for whether they precede transitions in financial time series.
Feedback loop mapping
Causal loop diagrams show how leverage, prices, margins and funding reinforce each other, as a basis for scenario design.
03Geography
Activity and exposure in Europe.
Population density, night lights and flood water over Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
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.
Activity and exposure, framed on Europe.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
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 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
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
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
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
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
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
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.
Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
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).
Optional layers (5 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
Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
04Data
What the method consumes.
Datasets and records used by complex systems and agent-based modelling on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
Data
Role
Label
Market microstructure data
Model calibration
OBSERVED
Historical crisis episodes
Validation
REPORTED
Supervisory data
Agent parameters
Published statistics
05Stress test
How a shock to assets becomes a system loss.
The equity the system loses after interbank contagion, against the initial hit, as leverage, interconnection and recovery rates change, the core of a macroprudential stress test. Teaching model with stated illustrative values, not a prediction. The map on this page shows Europe; the model itself is not tied to a place.
Controls
Fall in external assets
2.0%
Leverage (assets over equity)
10 times
Interbank share of assets
10%
Recovery on interbank claims
40%
Banks in the system
20
Core banks
20% of banks
Readouts
Equity lost to the initial shock
18.0%
Equity lost after contagion
45.0%
Added by contagion (DebtRank)
27.0 points
Banks in default
0
Numbers, methods and sources
Round
System equity lost (%)
Banks in default
0
18.00
0
1
28.80
0
2
35.28
0
3
39.17
0
4
41.50
0
5
42.90
0
6
43.74
0
7
44.24
0
8
44.55
0
9
44.73
0
10
44.84
0
11
44.90
0
12
44.94
0
13
44.96
0
14
44.98
0
15
44.99
0
16
44.99
0
17
45.00
0
18
45.00
0
19
45.00
0
20
45.00
0
21
45.00
0
22
45.00
0
23
45.00
0
24
45.00
0
25
45.00
0
26
45.00
0
27
45.00
0
28
45.00
0
29
45.00
0
30
45.00
0
Methods
DebtRank Battiston and others (2012), Scientific Reports 2:541
Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
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.
How a shock to assets becomes a system loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: DebtRank (Battiston and others (2012), Scientific Reports 2:541); Differential DebtRank (Bardoscia and others (2015), PLOS ONE 10); Contagion in financial networks (Gai and Kapadia (2010), Proc. R. Soc. A 466).
Models are evaluated by matching multiple stylised facts and patterns at different scales. Sensitivity and robustness analysis test dependence on assumptions.
Check
How it is done
Stylised facts
Models must reproduce known features such as fat tails and volatility clustering
Parameter sensitivity
Results are reported across plausible parameter ranges
Purpose stated
Each model states whether it explores mechanisms or supports quantitative decisions
Many plausible models can reproduce the same patterns. Calibration and validation data are often limited.
07Twin and cascade simulator
Trace a shock to the balance sheet.
Assembled from the Nexus Standards area profile Financial stability and prudential risk: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in complex systems and agent-based modelling for financial stability reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
Channel structure after the Basel Committee's work on climate-related risk drivers and transmission channels; links shown are typical paths, not estimated exposures.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
10Stewardship
Who maintains the method.
Systems Science and Modelling stewards complex systems and agent-based modelling for the Consortium and reviews how it is applied across platforms.
What are the limits of complex systems and agent-based modelling in this field?
Many plausible models can reproduce the same patterns. Calibration and validation data are often limited.
How are results checked?
Models are evaluated by matching multiple stylised facts and patterns at different scales. Sensitivity and robustness analysis test dependence on assumptions.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
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.
Global Risks AllianceFinancial StabilityScenario and Stress-TestGlobal Systems Risk and Resilience
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