Conditional Beliefs and Higher-Order Preferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we provide the Bayesian foundations of type structures--—such as those used for epistemic analysis of iterated admissibility by Brandenburger et al. (2008)--—in which beliefs are LPS’s (lexicographic probability systems) rather than standard probability measures as in Mertens and Zamir (1985). This turns out to be a setting in which the distinction between preference hierarchies (Epstein and Wang, 1996) and belief hierarchies is meaningful and the former has conceptual advantages. In particular, using preference hierarchies allows us to identify conditions under which the distinction between LPS beliefs about types and LCPS (lexicographic conditional probability system) beliefs about types is a meaningful one. Furthermore, we construct “universal” LPS/LCPS type structures and find that they describe the same finite-order preferences even though the universal LPS type structure describes more hierarchies. Finally, we give an epistemic condition for iterated admissibility using coherent hierarchies that cannot be types.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it