New Video - Beyond Active Inference: Calibrating Uncertainty

Active Inference tells us that biological systems minimize surprise, but what happens when minimizing surprise isn't enough? When the stakes of getting it wrong actually matter?

In this video, we introduce support sufficiency: a theoretical framework for how cognitive systems calibrate the depth of their uncertainty processing based on the consequences of their decisions. Not all beliefs need the same level of scrutiny. A system that treats every inference the same, whether it's identifying a coffee mug or diagnosing a threat, is wasting resources or courting disaster.

We discuss:

→ What support sufficiency is and how it extends beyond content-plus-confidence models of belief

→ The relationship between support sufficiency and the Free Energy Principle / Active Inference

→ How consequence-sensitive compression reshapes belief arbitration

→ The role of metacognition in regulating inferential depth

→ What we're looking to simulate in future work.