Allocator-recipient asymmetries in resource allocation preferences: A focus on bequests.
Bibliographic record
Abstract
In many financial situations, an allocator divides resources between recipients with different levels of need. We examine how allocators and recipients prefer resources to be allocated between recipients (i.e., the allocators do not benefit from the allocation) in interpersonal contexts. We propose allocator-recipient asymmetries in preferred allocations: the allocator and the higher need recipient weigh equality more heavily than the lower need recipient in their preferred allocations. We document these asymmetries in a familiar and important context: parents allocating bequests between children with varying financial needs. In our experiments, participants in the role of parents, higher need child, or lower need child indicate their preferred bequest allocations. The results show that the proportion of bequests parents allocate to their higher need child and the proportion that child prefers to receive are smaller than the proportion the lower need child wants their parents to allocate to their needier sibling. We further suggest that such asymmetries arise because, in interpersonal contexts (e.g., the context of parental bequests), the allocator and the higher need recipient are more concerned than the lower need recipient about the negative impact of unequal allocation on the relationship between recipients. Supporting this account, these asymmetries diminish in noninterpersonal contexts. Finally, we find that a perspective-taking intervention (i.e., prompting the allocator to consider the lower need recipient's preferences) reduces these asymmetries, leading to allocations that align more closely with the desires of the lower need recipient while enhancing the higher need recipient's financial well-being. Boundary conditions, alternative mechanisms, and implications for models of inequality aversion and social preferences are discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".