AI and Systems

Algorithmic monoculture appears when many decisions depend on the same model, data source or optimization rule, making local efficiency create system-wide correlation and fragility.

A digital twin may mirror current state, while a system dynamics model explains how feedback and accumulation can produce future behavior.

Multi-agent AI systems can produce capabilities and failures that are properties of interaction, not of any single agent viewed alone.

AI alignment depends on the interaction of models, objectives, organizations, users, markets, governance, and changing deployment environments.

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 systems view of AI agents explains compounding errors, automation bias, control delays, and safer workflow design.