Belief Revision in a Probabilistic Setting
Bibliographic record
Abstract
This work develops an approach to qualitative belief revision in a fully probabilistic setting. We begin with a logic where possible worlds are assigned probabilities. In this logic an agent may believe a formula is true even though the subjective probability of the formula is less than 1.0. Similarly, after revision by a formula ϕ, the agent will believe ϕ is true, even though the agent’s subjective probability of ϕ may be less than 1.0. We establish a correspondence with the hallmark AGM postulates for belief revision. Moreover, we use Jeffrey Conditionalisation to establish a link with iterated belief change. To this end, we develop an approach that satisfies appropriately modified Darwiche-Pearl postulates (with clear justification). Thus, we provide a connection between quantitative probabilistic approaches on the one hand and the qualitative formulation of belief change, on the other. This work holds potential for the development of practical belief revision systems by applying a (qualitative) approach to belief change in probabilistic, uncertain domains.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".