Efficiency and Equilibrium Selection in an Allocation Problem
Bibliographic record
Abstract
We study two variants of an allocation problem where two parties lay proportional claims to an asset, and an arbiter has a final say on allocation. The two variants we study vary by the incentives to the arbiter. In one variant, the arbiter is incentivized proportional to the payoff to the lowest paid claimant and in the other, the arbiter is incentivized proportional to the payoff to the highest paid claimant. While neither incentive scheme changes the set of equilibria, they alter expected payoff in off-equilibrium outcomes, with implications for equilibrium selection. Accordingly, the first variant leads to egalitarian claims whereas the second leads to claims of the entire pot, and subsequently to high incidence of impasse. A level-k model of bounded rationality fits the observed outcomes. Thus, in bargaining with multiple equilibria, the level-k model is useful in designing arbiter incentives to maximize efficiency.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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".