Role Assignment for Agent Evaluation Under Uncertainty: A Distributionally Robust Approach

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.