1/14/26
Trent Carter
Calibration Drift Map (CDM)_Subtitle:_ ITBE × Knowingness Quad for Humans & AI
Why it fits: the core phenomenon you’re visualizing is how perceived capability drifts relative to actual capability as evidence accumulates, with predictable phase structure (the quad) and characteristic overshoot/undershoot (ITBE + DK dynamics).
Alternate names (if you want something punchier)• The Knowingness–Calibration Loop (KCL)
• Evidence–Confidence Phase Model (ECPM)
• Perception–Reality Convergence Map (PRCM)
• ITBE–Knowingness Calibration Framework (IKCF)
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Abstract (using the images as examples)We introduce the Calibration Drift Map (CDM), a unified framework that fuses the Initial Trait Bias Effect (ITBE) with the Knowingness Quad to model how judgments of capability evolve as evidence accumulates—across human self-assessment (Dunning–Kruger-like dynamics), human assessment of others (ITBE projection), and AI capability perception versus measured performance. The CDM formalizes a common calibration trajectory: early optimistic overestimation under low evidence, a mid-phase correction and underconfidence “valley,” and eventual convergence toward calibrated judgment as measurement density increases. We visualize the framework six ways: a quadrant phase map showing calibration error vs. evidence with trajectories for self/other/AI; comparative error curves capturing overshoot and valley depth differences between domains; a perceived-versus-actual plot that makes the ITBE “gap” explicit and shows how it closes; a 2D error surface heatmap demonstrating how increasing meta-awareness (knowingness) damps calibration error for a given evidence level; a state-transition diagram treating the quad as a developmental machine driven by evidence and feedback; and a phase portrait illustrating how perception tracks reality over time, including directional “looping” through over- and under-estimation. Together, these views provide a compact language for describing why humans and AI systems are systematically misjudged early, why backlash phases occur, and how evaluation regimes and meta-awareness interventions can accelerate calibration toward reliable, evidence-grounded confidence.