A revelation mechanism for shared conditional preferences in multi-attribute negotiation
Bibliographic record
Abstract
Agents who negotiate over a space of multi-attribute agreements where conditional preferences may be present can encounter difficulties in converging toward Pareto-efficient outcomes. This is because of the fact that, while both agents may have strategic incentives for keeping their own preferences private, there may be a number of attributes for which, under certain conditions, the two agents have the same preference. If they could work together to discover such instances, and agree to eliminate a portion of the space of agreements that both dislike, it would greatly increase the probability and speed of reaching a mutually favourable deal. We present a negotiation mechanism for agents to eliminate portions of the agreement space that are mutually non-beneficial. The mechanism enables the semi-truthful revelation of conditional preferences in such a way that encourages agents to make progress towards finding non-Pareto efficient outcomes. We demonstrate the protocol for such negotiations and outline the set of strategies that (1) agents have incentive to follow and (2) will result in mutually favourable elimination. We also empirically measure the effectiveness of such agreement space reduction in terms of utility achieved.
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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 | 0.000 | 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) | 0.001 | 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".