Role Assignment for Agent Evaluation Under Uncertainty: A Distributionally Robust Approach
Bibliographic record
Abstract
Role-based collaboration (RBC) is an emerging and advanced methodology for problem-solving. A critical aspect of RBC theory is agent evaluation, which aims to assess agents’ abilities through a qualification value derived from a comprehensive analysis of their characteristics. This evaluation directly impacts the quality of role assignments. Existing research typically assumes that the qualification value is either predetermined, based on multiscale criteria, or following a predefined distribution. These assumptions, however, are overly idealistic and difficult to generalize, failing to capture the inherent volatility of the qualification value. To address this challenge, this article introduces a Wasserstein-based ambiguity set to model potential fluctuations in the qualification value, drawing on empirical distributions derived from historical sample data. Building upon the RBC framework and its abstract model environments, classes, agents, roles, groups, and objects (E-CARGO), we propose two data-driven models: distributionally robust group role assignment (DRGRA) and group multirole assignment (DRGMRA). These models aim to achieve more robust and optimal role assignments under uncertainty in agent evaluation. Leveraging strong duality, we reformulate DRGRA and DRGMRA as tractable finite mixed 0–1 convex problems, providing an approximation framework that reduces computational complexity. Notably, these models are adaptable to other problems with no uncertainty in agent evaluation, highlighting their modeling scalability. Experimental results demonstrate the effectiveness and robustness of the proposed models.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".