Parsimonious Predictions for Strategyproof Scheduling

Richard Cole, Anupam Gupta, Pranav Jangir

Keywords: algorithmic game theory, mechanism design, scheduling, learning with predictions

TL;DR: 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.

Abstract:

We consider the problem of scheduling $m$ jobs on $n$ unrelated
strategic machines to minimize the maximum load of any machine, but
the machines are strategic and may misreport processing times to
minimize their own load. The pioneering work of Nisan and Ronen gave
an $n$-approximate deterministic strategyproof mechanism for this
setting, and this was recently shown to be best possible by the
breakthrough results of Christodoulou et al. This large approxation
guarantee begs the question: how can we avoid these large worst-case
results. In this work, we use the powerful framework of algorithms
with (machine-learned) predictions to bypass these strong
impossibility results. We show how we can predict $O(m+n)$ values to
obtain a deterministic strategyproof algorithm whose makespan is
within a constant factor of the optimal makespan when the
predictions are correct, and $O(n)$ times the optimum no matter how
poor the predictions are.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 25222