Complexity Science
Human systems are adaptive, emergent, path-dependent, and fat-tailed.
What non-linearities and tipping points are latent here?
Canonical thinkers
- Stuart Kauffman
- John Holland
- Nassim Taleb
- Brian Arthur
Seed concepts
- complex adaptive
- emergence
- self organization
- self-organization
- phase transition
- tipping point
- network effect
- path dependence
- attractor
- chaos
- antifragile
- black swan
- fat tail
- power law
Underlined seeds link to their full glossary entry. Plain seeds are pending a definition page.
Typed connections
Full graph →How this layer reinforces, counteracts, or depends on the rest of the system.
- Generalizes·LayerSystems Thinking generalizes this layer
Complexity is what systems thinking becomes once agents adapt to each other in real time.
- Depends on·LayerThis layer depends on Network Science
Emergent behavior is hard to reason about without knowing the topology that carries the interactions.
- Depends on·LayerInnovation depends on this layer
Real innovation requires variation, selection, and retention — the same engine that drives complex adaptive systems.
Elements in this layer
17 HBEsSystems that gain from disorder.
High-impact, hard-to-predict, retrospectively explainable events.
Nonlinear systems are highly sensitive to initial conditions.
A system with many interacting parts that learn and adapt.
Many interacting parts producing emergent behavior nobody designed.
The whole has properties that the individual parts do not.
Customers become trapped in a product due to switching costs or network effects.
Value grows with the number of users.
Where you can go is constrained by where you've been.
A relationship where a small number of inputs account for most outputs.
A small number of items account for most of the value.
Threshold past which small inputs cause disproportionate change.
The context that shapes behavior
Patterns between two people
Rules shape competition
Laws, systems, and governance
Complex adaptive systems