Joint-task truthfulness of the DMI mechanism
For mechanism designers in peer grading or peer review, this clarifies the robustness of DMI's truthfulness guarantees under more realistic agent strategies.
The paper shows that the Determinant Mutual Information (DMI) mechanism remains truthful against joint-task strategies when other agents use consistent strategies, but fails against joint-task strategies without that restriction.
The Determinant Mutual Information (DMI) mechanism of Kong (2020, 2024) is dominantly truthful within the class of *consistent* reporting strategies, those that apply the same single-task strategy to every task. In settings where agents see multiple tasks before reporting, such as peer grading or peer review, it is natural to consider *joint-task* strategies that may condition reports on the full signal vector. Perhaps surprisingly, we show that the DMI mechanism preserves truthful reporting as a best response among all joint-task strategies when other agents play consistent strategies, so that truthfulness remains a Bayes--Nash equilibrium in the joint-task class. Without the restriction of peers to consistent strategies, however, both dominant truthfulness and informed truthfulness fail against joint-task peer strategies.