REU Project

My research focuses on extending the PACE (Pacing According to Current Estimated Utility) algorithm to fair, real-time resource allocation. While traditional fair division mechanisms rely on static, complete data, real-world systems like recommendation feeds must serve items sequentially without knowing what comes next. By generalizing PACE to multi-item recommendation slates under K-sharing auction rules, our approach splits user attention fairly among the top-paced items. Across both real-world MovieLens data and concentrated preference profiles, K-sharing significantly smooths budget expenditure, prevents item starvation, and speeds up equilibrium convergence reducing maximum relative allocation errors by over 80% as slate size grows.