A complex adaptive system contains diverse interacting agents that learn, evolve, imitate, or otherwise change behavior. Their local interactions and feedback create system-level patterns that no single agent designs.
Typical properties include emergence, nonlinearity, adaptation, path dependence, distributed information, and changing loop dominance. Examples can include ecosystems, markets, cities, immune systems, organizations, and online communities.
Why it matters
Interventions change the environment to which agents adapt. This limits simple prediction and favors monitoring, reversible experiments, diversity, and iterative governance.
Read Complex Adaptive Systems: How Local Interactions Produce Emergence.