Competing in a queue for resource allocations among non-cooperative agents
Bibliographic record
Abstract
In this paper, we investigate a multi-agent noncooperative game for resource allocations based on an M/D/1 queuing model. Specifically, agents with common goals to maximize utility are deployed to compete with each other to bid or bribe for quicker service provided by the server. The bid/bribe of each agent in the queue is not revealed, but the outcomes, in terms of the pair of (bid/bribe; total waiting time), are publicly available from the server. Agents choose from one of three available strategies: random strategy, Nash equilibrium strategy and linear regression strategy, for their decision-makings. Bayesian update is integrated into the linear regression technique for searching an optimal bid/bribe. Besides, weighted average, second order autoregressive process (AR(2)), and random walk are utilized to predict service speed. After each agent obtained service, it reevaluates its strategy and adjusts it accordingly. Results show that in the long run, the dominant strategy depends on the service speed. When the service speed is low, random strategy dominates the society. But if the service speed is high, linear regression strategy dominates. The model can be extended to study agent-based social simulations and decentralized scheduling for resource allocations in an open multi-agent system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".