Probabilistic Logics for Reasoning about Decision Making under Uncertainty
Bibliographic record
Abstract
We investigate probabilistic propositional logics as a way of expressing, and reasoning about decision making under uncertainty of a probabilistic type, with the goal of providing a deductive basis for various decision support problems. We introduce logics with language features for representing probability, utility, and independence, and provide an interpretation with a possible world semantics. We extract as axioms and inference rules the main properties that characterize the reasoning tailored to acting rationally in the decision making scenarios. The introduced calculus is complete for the type of reasoning performed by the corresponding graphical models. The thesis addresses the advantages and challenges of the use of probabilistic logic in modelling and reasoning about decision making under uncertainty. It provides a survey into probabilistic logics, the problem of complete axiomatization of conditional independence, as well as an overview of some of the most important approaches towards decision making under uncertainty
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".