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Understanding complex systems

Why understand complex systems?​

Across domains like food production, transport, and healthcare, outcomes depend on many interacting parts. Understanding these systems helps identify drivers, anticipate risks, and improve decisions.

What is a complex system?​

A complex system is a collection of interconnected parts that exhibit emergent behavior not explained by the parts in isolation. Interactions are nonlinear; multiple organizations and models (including people’s mental models) coexist and influence each other.

Capturing the what​

Start by representing what has happened using entities, events, locations, and relationships, with adequate resolution and provenance:

  • Organizations and people
  • Locations with coordinates
  • Events with timestamps
  • Objects and their relationships

Good data is representative, relatable across the end‑to‑end flow, and high‑resolution enough to capture dynamics.

Understanding why and how​

Use complementary approaches:

  • Mechanistic models: encode causal mechanisms (physics, biology, epidemiology) to explain and predict.
  • Non‑mechanistic models: find patterns (statistics, ML) that describe behavior when mechanisms are unknown or intractable.

Often a hybrid is best: use mechanistic structure where you can; augment with statistical/ML where helpful.

From insight to governance​

Systems are shaped for the behaviors they exhibit, making change hard. To steer:

  • Raise collective intelligence across specialties
  • Use shared, flexible information models to learn and adapt
  • Govern on end‑to‑end outcomes and flow performance