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.
GRA · Financial Stability
Spatial financeWhere capital meets hazard, exposure and transition
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.
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.
Heterogeneous traders, funds and dealers are simulated to study how margin rules, leverage limits and liquidity tools change the dynamics of stress.
Indicators such as rising autocorrelation and variance are tested for whether they precede transitions in financial time series.
Causal loop diagrams show how leverage, prices, margins and funding reinforce each other, as a basis for scenario design.
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.
Layers 3
Activity and exposure, for this page
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.
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.
Layers and dates
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 |
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
Readouts
| 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 |
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
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.
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.
Hazards
Systems and couplings
Controls
NS-0 Constitution and principlesNS-3 Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9 Contextual UNIs (deployments, instances, programme instances)NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14 Conformance and crosswalk to external standardsNRS-H62 Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H113 Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H118 Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)NRS-H79 Hazard family: Mental health crises (NHS-1 health.mental)NRS-U9 Universe platform profile: Financial Regulation NexusNRS-C5 Scenarios and stress testingObservation
Example geography
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.
How a shock in complex systems and agent-based modelling for financial stability reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
Systems Science and Modelling stewards complex systems and agent-based modelling for the Consortium and reviews how it is applied across platforms.
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