A Restricted Markov Tree Model for Inference and Generation in Social Choice with Incomplete Preferences
Bibliographic record
Abstract
We introduce a probabilistic graphical model of ranked preferences for social choice based on a restricted version of a kth-order Markov tree. The system is similar to Plackett's model, and is based on the intuition that, in some domains, an agent's next most preferred alternative is highly predictable given a few of the immediately preceding alternatives. We provide a bound on the data requirements to learn the parameters of such a model and show eventual consistency between most probable sequences of the model and both the centroid of a Mallows model and the induced ranking of a RUM, theoretically and on artificial data; this is followed by a full evaluation of the model as a social choice system, including comparisons with existing approaches on 6 real world datasets. An application of the system to a simulated multiagent coordination task is also demonstrated, in which the proposed system offers pronounced advantages over existing approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".