Modelling Multiagent Bayesian Networks with Inclusion Dependencies
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Bibliographic record
Abstract
Multiagent Bayesian networks (MABNs) are a powerful new framework for uncertainty management in a distributed environment. In a MABN, a collective joint probability distribution is defined by the conditional probability tables (CPTs) supplied by the individual agents. It is assumed, however, that CPTs supplied by individual agents agree on the variable domains, an assumption that does not necessarily hold in practice. In this paper, we suggest modelling MABNs with inclusion dependencies. Our approach is more flexible, and perhaps realistic, by allowing CPTs supplied by different agents to disagree on variable domains. Our main result is that the input CPTs define a joint probability distribution if and only if certain inclusion dependencies are satisfied. Other advantages, both practical and theoretical, of modelling MABNs with inclusion dependencies are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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