What this diagram shows: NAL belief revision merges independent evidence about the same proposition. Unlike Bayesian updating, it tracks confidence (how much evidence) separately from frequency (what the evidence says).
Formula: Convert confidence to evidence weight w=c/(1-c). Revised frequency is the weighted average. Revised confidence pools all evidence: c_rev = (w1+w2)/(w1+w2+1). Confidence always increases with more evidence.
Real data: The reliable_agent belief started at c=0.484 after initial observation, rose to 0.766 after second confirming evidence, then 0.899 after third. Each revision approaches but never reaches 1.0 — the asymptotic bound ensures epistemic humility.
Contradiction case: When evidence conflicts (f=0.9 vs f=0.1), revision does NOT discard either. It produces moderate frequency with HIGH confidence — the system is very sure the truth is ambiguous.