Applying Bargaining Game Theory to Web Services Negotiation
Bibliographic record
Abstract
Service Level Agreements (SLAs) have obvious value for Service-Oriented Computing and have received attention from both academics and industry. However, SLAs still lack a theoretical basis and effective techniques to facilitate automatic SLA establishment. In this paper, we classify negotiations into four types, and focus on the 1-to-1 Web services negotiation between a single service provider and a single service consumer. We make three contributions. Firstly, we represent the 1-to-1 Web services negotiation as a bargaining game. Here, we are interested in a bargain that takes into account the interests of both a service provider and a service consumer, in other words, a fair solution. Secondly, we determine a Nash equilibrium that can be regarded as the fair solution to a two-player bargaining game. We also determine the fair solution to the 1-to-1 Web services negotiation. Finally, we discuss issues that may arise with the 1-to-1 Web services negotiation under credible threats, incomplete information, time constraints, and multiple attributes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".