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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".