MBNP: A Multi-bid Protocol for Integrating Matching and Negotiation in Constraint-based Environments
Bibliographic record
Abstract
Negotiation in complex, constraint-based multiagent environments is a significant challenge. Traditional approaches treat partner selection (matching) and agreement formation (negotiation) as separate problems, leading to suboptimal outcomes and scalability issues. This paper introduces the Multi-bid Negotiation Protocol (MBNP), a novel decentralized framework that seamlessly integrates these two processes. MBNP solves this challenge by allowing agents to explore potential partnerships and agreement terms in parallel, using a multibid proposal mechanism combined with Deferred Acceptance-inspired dynamics. Our empirical evaluation on standard benchmarks demonstrates that MBNP significantly outperforms traditional baselines-including a theoretically optimal centralized algorithm (Munkres)-in both agreement quality (AAU) and coverage (AR), especially in larger populations. These results validate our integrated approach as a robust and scalable solution for complex multi-agent negotiation.
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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 | 0.001 |
| 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".