Automated Dynamic Negotiation over Environmental Issues
Bibliographic record
Abstract
Negotiation is a common means of resolving conflicts in social interactions. A popular approach for modeling social negotiation is automated negotiation. It is a distributed search in the space of potential agreements, facilitated by an agent-based model (ABM). Although automated negotiation is extensively applied in different fields of e-commerce, its application in environmental studies is still unexplored. This paper aims to lead the negotiation process over environmental issues in an efficient way where the possible agreement can be reached in few rounds of negotiation. To achieve this goal, an ABM is developed which has two significant characteristics. First, the proposer agent automatically learns the preferences of all stakeholder using the arguments and responses received from them in the rounds of negotiation. Second, the proposer accelerates the negotiation by automating the process of proposal-offering. To this end, first, the problem of proposal selection in one-to-one negotiation with each stakeholder is modeled using Markov Random Fields (MRF) and is solved using a belief propagation-based approach. Then, the proposer applies statistical analysis to identify the most optimal proposal and conducts a one-to-many 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".