Multiagent Evaluation Mechanisms - Alon, Tal; Dobson, Magdalen; Procaccia, Ariel; Talgam-Cohen, Inbal; Tucker-Foltz, Jamie (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
We consider settings where agents are evaluated based on observed features, and assume they seek to achieve feature values that bring about good evaluations. Our goal is to craft evaluation mechanisms that incentivize the agents to invest effort in desirable actions; a notable application is the design of course grading schemes. Previous work has studied this problem in the case of a single agent. By contrast, we investigate the general, multi-agent model, and provide a complete characterization of its computational complexity.