Team Formation amidst Conflicts
This work addresses team formation challenges in educational and human-resource management settings, though it appears incremental as it applies known rounding techniques to a new formulation.
The paper tackles the problem of assigning individuals to tasks while considering their preferences and interpersonal conflicts, using dependent rounding schemes to develop efficient approximation algorithms. The algorithms outperform natural baselines and human experts in educational settings, improve team diversity in HR applications, and demonstrate good scalability on synthetic data.
In this work, we formulate the problem of team formation amidst conflicts. The goal is to assign individuals to tasks, with given capacities, taking into account individuals' task preferences and the conflicts between them. Using dependent rounding schemes as our main toolbox, we provide efficient approximation algorithms. Our framework is extremely versatile and can model many different real-world scenarios as they arise in educational settings and human-resource management. We test and deploy our algorithms on real-world datasets and we show that our algorithms find assignments that are better than those found by natural baselines. In the educational setting we also show how our assignments are far better than those done manually by human experts. In the human resource management application we show how our assignments increase the diversity of teams. Finally, using a synthetic dataset we demonstrate that our algorithms scale very well in practice.