GCRI · Mathematics, statistics and data
Complex systems and agent-based modelling
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. Systems Science and Modelling stewards this page for the Nexus Consortium.
Complex systems and agent-based modelling on the record.
- 01InstitutionThe Global Centre for Risk and Innovation (GCRI)
- 02The methodWhat complex systems and agent-based modelling covers
- 03Reference practiceComplex systems methods rely on transparent model documentation
- 04In systemic risk workEmergence and tipping
- 05Applied inPlatforms that apply this method
- 06StewardshipWho maintains this page
- 07SourcesWhere this page’s references lead
What complex systems and agent-based modelling covers.
- 01
Core methods
agent-based simulation; system dynamics modelling; nonlinear dynamics analysis; early warning indicators; emergence and pattern analysis; co-evolution modelling.
- 02
Data and instruments
micro-level behavioural data; survey and census data; observational time series; laboratory experiments; model calibration targets.
- 03
Validation
Models are evaluated by matching multiple stylised facts and patterns at different scales. Sensitivity and robustness analysis test dependence on assumptions.
- 04
Limits
Many plausible models can reproduce the same patterns. Calibration and validation data are often limited.
- 05
Use in risk decisions
Complex systems approaches explore how interacting risks can produce sudden, systemic shifts. They support stress testing of policies against emergent behaviour.
Complex systems methods rely on transparent model documentation.
| Reference | What it sets |
|---|---|
| ODD protocol for agent-based models | Standard model description |
| IPBES Transformative Change Assessment | Systems change evidence |
| IRGC guidance on systemic risk | Governance of complex risks |
How the process runs.
Process
Complex systems and agent-based modelling: Cascade across systems
An initial hazard can trigger later risks and events; this illustration draws one cascade across systems such as energy, water, ICT, health and transport.
01 Hazard
- 01Initiating hazardDataset · Competent authorityCascading risk is the risk that hazard events trigger later risks and events, through chains of interrelated hazards or impacts.
02 First-order systems
- 02Example system: energyModel · System operatorAn example of a system a hazard can hit first. What depends on it is set by a dependency survey.
- 03Example system: waterModel · System operatorA second example of a system a hazard can hit first. Its links to other systems are shown as examples only.
03 Second-order systems
- 04Example system: ICTModel · System operatorAn example of a system that may depend on others, for example on backup power. Survey data set the real dependencies.
- 05Example system: health servicesModel · System operatorAn example of a system that may depend on water, power and data. Survey data set the real dependencies.
- 06Example system: transportModel · System operatorAn example of a system that may depend on ICT, fuel and logistics. Survey data set the real dependencies.
04 Limiting the cascade
- 07Buffers and redundancyStep · Competent authorityBuffers, redundancy and restoration can limit how far an impact spreads. The OECD report moves from single assets to system resilience.
- 08Restoration prioritiesDecision · Competent authorityThe responsible party sets which services are restored first, under its own rules.
Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval. The dependencies are examples. A dependency survey for the place sets them.
Emergence and tipping.
Agent-based and system dynamics models explore how interactions produce emergent outcomes such as panics, cascades and tipping. Their value is exploratory: identifying mechanisms and conditions, not forecasting. Documentation is essential because these models have many assumptions.
- Document models with the ODD protocol.
- Use for exploration, not point prediction.
- Validate patterns, not only aggregates.
- Applications on Systems Modelling and Health Nexus.
Who maintains this page.
Systems Science and Modelling stewards complex systems and agent-based modelling: it keeps this description current and reviews how the method is applied across the platforms.
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.
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