Alignment Workspace

Item 103: A decision theory for logical inductors without the split between a belief market and a choice learner

Deference line — second wave · difficulty open · project deference · open · node item-103

The kernel's extension (projects/deference/rounds/2026-09-27-corrigibility-kernel-phase2/SPEC.md §6) realizes the decision interface twice — Continuation BRIA beside the inductor, and a maximizer over the inductor with exploration restricted to the permitted set — and both keep the split the pinned Logical Induction paper leaves open: the market prices sentences, a separate learner chooses, and the coupling is a named hypothesis (unbiasedness from feedback; the auction's budget accounting). The known pathologies of argmax over the inductor's own-action conditionals live in that split. Wanted: a principled decision theory for logical inductors with no separate choice learner. The candidate direction: claims of the form "if chosen, this option scores x" as securities of the inductor, settled only when the option is chosen, with a choice rule that forces tests of options promised to be better — the bounded-inductive-rationality criterion stated inside the market rather than beside it.

Deliverable shape: a single learner over the inductor that satisfies the extension's decision interface (evaluations of the stated form; a maximizer off exploration; exploration in the permitted set; overestimation B(K) = o(K)) with explicit accounting of B(K); Lean where the pinned formalization admits it, paper-level elsewhere with named hypotheses. Acceptance check: python3 tests/run.py green; Lean audits clean; the extension's rate theorem instantiated on the learner (realized_rate) with its B(K) discharged rather than assumed. Context: the phase-2 round's SPEC.md §6 and REPORT.md; KernelExtension.lean; Continuation-BRIA.md §9 (the LI coupling); items 86, 90, 102. Consumed by: the extension's exploration realization; item 102's re-scoped question. A solution ships: the learner with its accounting, or the obstruction.

Deliverable shape: a single learner over the inductor that satisfies the extension's decision interface (evaluations of the stated form; a maximizer off exploration; exploration in the permitted set; overestimation B(K) = o(K)) with explicit accounting of B(K); Lean where the pinned formalization admits it, paper-level elsewhere with named hypotheses.

Acceptance check: python3 tests/run.py green; Lean audits clean; the extension's rate theorem instantiated on the learner (realized_rate) with its B(K) discharged rather than assumed.

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