Mechanism design for abstract argumentation
Bibliographic record
Abstract
Since their introduction by Dung over a decade ago, abstract argumentation frameworks have received increasing interest in artificial intelligence as a convenient model for reasoning about general characteristics of argument. Such a framework consists of a set of arguments and a binary defeat relation among them. Various semantic and computational approaches have been developed to characterise the acceptability of individual arguments in a given argumentation framework. However, little work exists on understanding the strategic aspects of abstract argumentation among self-interested agents. In this paper, we introduce (game-theoretic) argumentation mechanism design (ArgMD), which enables the design and analysis of argumentation mechanisms for self-interested agents. We define the notion of a direct-revelation argumentation mechanism, in which agents must decide which arguments to reveal simultaneously. We then design a particular direct argumentation mechanism and prove that it is strategy proof under specific conditions; that is, the strategy profile in which each agent reveals its arguments truthfully is a dominant strategy equilibrium.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".