This paper proposes a novel learning-augmented deterministic and truthful mechanism for the strategic unrelated machine scheduling problem using fewer predictions to achieve the best possible consistency and robustness.

We specify and analyze a simple probabilistic model to analyze the average-case complexitiy of deterministic game-solving algorithms, adressing limitations of previous independence-based models.