AI and Systems

Recommendation systems and users co-create filter bubbles through feedback. Here is why the loop narrows your world and how to break it.

AI pursues the goal you set, not the one you meant. Reward hacking shows why the gap between measure and intent matters.

AI Governance helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior. This guid

AI Governance helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior. This guid

Human-AI Feedback Loop helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior.

Human-AI Feedback Loop helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior.

Algorithmic Monoculture helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior.

Algorithmic Monoculture helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior.

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.