Bibliographic record
Abstract
Recently Argumentation Mechanism Design (ArgMD) was introduced as a paradigm for studying argumentation using game-theoretic techniques. To date, this framework has been used to study under what conditions a direct mechanism based on Dung's grounded semantics is strategy-proof (i.e. truth-enforcing) when knowledge of arguments is private to self-interested agents. In this paper, we study Dung's preferred semantics in order to understand under what conditions it is possible to design strategy-proof mechanisms. This is challenging since, unlike with the grounded semantics, there may be multiple preferred extensions, forcing a mechanism to select one. We show that this gives rise to interesting strategic behaviour, and we show that in general it is not possible to have a strategy-proof mechanism that selects amongst the preferred extensions in a non-biased manner. We also investigaet refinements of preferred semantics which induce unique outcomes, namely the skeptical-preferred and ideal semantics.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".