pproenca Inquiry — acting well under not-knowing
The skills elsewhere assume the unknown is already named and turned into a test. This is the faculty for the moment BEFORE that: the fog, where you do not yet know what question to ask. A capable agent in unfamiliar terrain neither guesses nor freezes — it runs a loop it can state precisely: predict, observe the surprise, update. This skill makes that loop measurable, so it becomes critical thinking rather than narration.
Why measurable, and why it is not novel
Statistical significance was not a discovery about the world; it was a decision rule under uncertainty — a threshold that separates "I observed something" from "I observed something that should change my belief." Before it, hunches and confirmation bias; after it, a framework. This skill is that move applied to the agent's own understanding: a way to measure whether a surprise is real — large enough, against what was expected, to update on — and whether the agent's confidence can be trusted at all.
The loop (state a prediction BEFORE you look)
- Predict. Before observing, state a claim about the terrain AND a confidence (0-1). The confidence must be recorded before the observation — a prediction backfilled after looking is not a prediction, it is a rationalization, and the log is built to make that visible.
- Falsify cheapest-first. Make the single cheapest observation that would most change the picture if the prediction is wrong. Try hardest to KILL your own prediction, not to confirm it — confirmation-seeking is the psychosis of understanding, the belief-level twin of judging a result against your own baseline instead of the floor.
- Record the surprise. Surprise magnitude = confidence × wrongness. A confident claim that was wrong scores high (significant — it must teach); a hedged claim that was wrong scores low (expected — noise). This is the threshold that separates a lesson from a shrug.
- Teach. A surprise above threshold feeds
mental-modelswith a reframing whose authority is "a confident prediction was wrong" — the belief-level analogue of a result far from floor. The reframe is what would have predicted better. - Narrow. The sharpened picture makes the next prediction better, until the fog has a shape you can name as a problem — at which point the change-kind spine takes over.
Calibration — the measurable proxy for trustworthy judgment
Across many predictions, bucket them by stated confidence and compare to the actual hit rate. If Cairn says 80% and is right ~80% of the time, it is CALIBRATED — its intuitions can be trusted. If it says 80% and is right 50%, it is OVERCONFIDENT — a measurable defect that should keep its gates up. Calibration is the empirical self-trust signal: not "did I solve it" but "were my beliefs honest about their own uncertainty."
Calibration is ADVISORY until the sample is large enough to mean anything — the skill refuses to report it as meaningful below a minimum N, the way a scientist will not quote significance on three data points. Computing self-trust from noise would be the exact "competence declared, not demonstrated" failure the capability ledger forbids. When the sample is real, calibration MAY later become licensing evidence — built then, on evidence, not now in anticipation.
Cairn builds its own version
The thresholds and confidence buckets start as defaults but are recalibrated from Cairn's OWN history: if its 70% bucket consistently hits 85%, the framework learns Cairn runs underconfident and adjusts. The empirical lens is applied to the lens. Over many repos, the accumulated record of "what I confidently believed that turned out wrong, and what corrected me" IS Cairn's critical thinking — unique to its history, not inherited. The framework is given; the conclusions are earned.
The one law here
A prediction's confidence is stated before the observation, or it does not count. Honesty about uncertainty is the whole faculty; a prediction that cannot be wrong teaches nothing, and a confidence retrofitted to match the answer is the lie this skill exists to make measurable.
Files
references/predicting.md— how to state a falsifiable prediction and pick the cheapest observation.scripts/store.py— the prediction-log port (JSONL).scripts/predict.py— log a prediction + confidence BEFORE observing.scripts/observe.py— record the outcome, compute surprise, feed mental-models.scripts/calibration.py— report calibration (or refuse, below minimum N).