A Bargaining-Based Solution to the Team Mobility Planning Game
Bibliographic record
Abstract
Despite extensive research on mobility planning, a situation in which multiple travellers participate in a cooperative endeavor to help each other optimize their objectives has not been investigated. Furthermore, due to the inherent multi-participant nature of the mobility problem, the existing solutions fail to produce ground-truth optimal mobility plans in the practical sense despite their claimed and well-proven theoretical optimality. This paper presents a team mobility trip-planning solution. The solution comes in the form of a game theoretic treatment of the planning problem. The solution allows multiple participants to engage in a collective solution-finding endeavor. The game theoretic solution utilizes the bargaining game to allow for the drivers to negotiate using their chosen strategies. This paper provides mathematical formulations of the team-planning problem, the developed solution, and the bargaining game. Furthermore, various aspects of the described game, such as game solution existence and game balancedness, are investigated. Simulation results are reported to demonstrate the efficiency of the developed game theoretic formulation under various scenarios.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".