Causes influence each other in multiple directions
Feedback relationships
Effects loop back to influence their own causes
Nonlinear influence
Small inputs may create large effects (or none)
Emergent outcomes
Results arise from interactions rather than one source
Distributed causation
Responsibility is spread across multiple factors
Reinforcing and balancing loops
Dynamics created by cycles of amplification or stabilization
Interdependent drivers
Factors cannot be isolated without losing explanatory power
Systemic causality
The system structure produces outcomes
Is Not
Other (IS NOT)
Why It’s Different
Linear cause → effect chain
Single directional progression
Root cause explanation
Assumes one primary cause explains the outcome
Single-variable causation
One factor responsible for the outcome
Event-based explanation
Focus on discrete incidents rather than relationships
Blame attribution
Assigns responsibility to a person or action
Boundary
A causal structure where multiple factors interact and influence each other through feedback, producing outcomes that cannot be explained by a single cause or linear chain. 1
**Linear models assume causes act independently.
A web of causality assumes causes modify each other.
Examples
There is not a single person or committee that is over oil prices (not even OPEC). What drives the price of oil is supply and demand. There are many causes that can influence both the supply and demand of oil. So the cost isn’t caused by one factor, but rather a web of factors.