Efficiency in Pure‐Exchange Economies With Risk‐Averse Monetary Utilities
Bibliographic record
Abstract
ABSTRACT We study Pareto efficiency in a pure‐exchange economy where agents' preferences are represented by risk‐averse monetary utilities. These coincide with law‐invariant monetary utilities, and they can be shown to correspond to the class of monotone, (quasi‐)concave, Schur concave, and translation‐invariant utility functionals. This covers a large class of utility functionals, including a variety of law‐invariant robust utilities. Given that Pareto optima exist and are comonotone, we provide a crisp characterization thereof in the case of law‐invariant positively homogeneous monetary utilities. This characterization provides an easily implementable algorithm that fully determines the shape of Pareto‐optimal (PO) allocations. In the special case of law‐invariant comonotone‐additive monetary utility functionals (concave Yaari‐dual utilities), we provide a closed‐form characterization of Pareto optima. As an application, we examine risk‐sharing markets where all agents evaluate risk through law‐invariant coherent risk measures, a widely popular class of risk measures. In a numerical illustration, we characterize PO risk‐sharing for some special types of coherent risk measures.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".