Optimistic and pessimistic approaches for cooperative games
Bibliographic record
Abstract
Cooperative game theory explores how to fairly allocate the joint value generated by a group of decision-makers, but its application is compromised by the large number of counterfactuals needed to compute the value of all coalitions, a problem made even more complicated when externalities are present. We provide a theoretical foundation for a simplification used in many applications, in which the value of a coalition is computed assuming that they either select before or after the complement set of agents, providing optimistic and pessimistic values on what a coalition should receive. In a vast set of problems exhibiting what we call feasibility externalities, we show that ensuring a coalition does not receive more than its optimistic value is always at least as difficult as ensuring it receives its pessimistic value. Furthermore, under the presence of negative externalities, we establish the existence of stable allocations that respect these bounds. Finally, we examine well-known optimization-based applications and their corresponding cooperative games to show how our results lead to new insights and allow the derivation of further results from the existing literature.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".