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Add deterministic prospective measurement-ranking interface - #10
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Ranks an explicit, caller-supplied set of prospective measurement candidates from a caller-computed risk or utility score per candidate (e.g. from expected_action_losses/bayes_action_set applied to a simulated future update). Deterministic stable-sort ordering, standard competition ranking with exact-tie sets, and strict input validation; no simulation, policy search, or domain assumptions live here. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
rank_prospective_measurements cast all scores to float64, so adjacent large int64 scores above 2**53 collapsed into a false tie. Utility ordering also negated raw scores, which silently overflows for the minimum representable signed integer. Integer scores now keep their exact dtype, and utility negation for integer scores uses arbitrary-precision Python ints instead of fixed-width negation. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Summary
Validation
All seven checks passed at the current head: Linux Python 3.10–3.13, macOS and Windows Python 3.12, and lint/format checks. No dependency changes.
Scores, probabilities, losses, and their interpretation remain caller-supplied assumptions. The ranking primitive does not fit a model or compute posterior updates.