Bibliographic record
Abstract
We consider a set of users who are located along a pipeline with a single source. These users consume a good that is extracted from the source and flows downstream, with diminishing marginal returns for each user. In addition, flows along each edge in the pipeline create negative externalities, which are nondecreasing as a function of flow. The users cooperate toward obtaining group welfare maximization. In both the continuous and discrete cases, we obtain the group optimal solutions, and we then use cooperative game theory to determine how best to allocate the damages, using optimistic and pessimistic formulations for the characteristic function. Using core stability as our guiding principle, we provide a set of stable allocations that apportions the damages at a location among the set of downstream users, notably an average damage allocation and a marginal damage allocation. Given that the joint optimization forces agents to reduce (unequally) their consumption, we also examine the Shapley value of the optimistic game, also in the core, that allows to compensate agents who have sacrificed their consumption for the benefit of the group. Finally, we show that our pipeline externalities model generalizes some well-known problems from the literature, including the river sharing problem of Ambec and Sprumont 2002 and the joint production problem of Moulin and Shenker 1992.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".