Reputation-aware task allocation for human trustees
Bibliographic record
Abstract
Compared to automated entities, human trustees have two distinct characteristics: 1) they are resource constrained (with limited time and effort to serve requests), and 2) their utility is not linearly related to income. Existing research in reputation-aware task delegation did not consider these two issues together. This limits their effectiveness in human-agent collectives such as crowdsourcing systems. In this paper, we propose a distributed reputation-aware task allocation approach - RATA-NL - to address these issues simultaneously. It is designed to help an individual human trustee determine the optimal number of task requests to accept at each time step based on his situation to maximize his long term well-being. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of the trustees, and provides provable performance guarantees. RATA-NL has been compared with five state-of-the-art approaches through extensive simulations based on human task delegation behavior abstracted from a user study involving over 100 trustees for eight weeks. The results demonstrated significant advantages of RATA-NL, especially under high workload conditions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".