Model collapse can occur when generated outputs re-enter future training data, progressively distorting or narrowing the learned distribution.
Automation bias is not simply human gullibility; it emerges from repeated interaction among people, tools, workload, incentives, and feedback.
A behavior-over-time graph shows how an important variable changes across a relevant time horizon, shifting attention from events to patterns.
Systems thinking is a broad orientation and family of approaches; system dynamics is a modeling methodology focused on feedback, accumulation, and behavior over time.
Organizational silos persist because structures make local optimization rational, not simply because people lack a collaborative mindset.
When a measure becomes a high-stakes target, people adapt to the measure and can break its relationship with the outcome it represented.
Efficiency reduces resource use under expected conditions; resilience preserves or restores essential function when conditions change.
Self-organization occurs when local interactions and feedback produce system-level order without a central designer specifying the final pattern.
The iceberg model helps move analysis from visible events toward patterns, structures, and assumptions that make those events more likely.
Systems archetypes are recurring feedback structures that help teams form hypotheses about persistent patterns without forcing every problem into a template.