Equilibria in multiagent online problems with predictions

TL;DR

This paper explores how predictions influence equilibria in multiagent online problems, specifically through a novel multiagent ski-rental model where agents collaborate or rent individually based on predicted behaviors.

Contribution

It introduces a multiagent ski-rental problem incorporating predictions, analyzing how these predictions affect equilibria and agent collaboration strategies.

Findings

Predictions can significantly alter equilibrium outcomes.

Collaboration improves resource utilization when predictions are accurate.

The model demonstrates the strategic impact of predictions in multiagent online settings.

Abstract

We study the power of (competitive) algorithms with predictions in a multiagent setting. To this goal, we introduce a multiagent version of the ski-rental problem. In this problem agents can collaborate by pooling resources to get a group license for some asset. If the license price is not met then agents have to rent the asset individually for the day at a unit price. Otherwise the license becomes available forever to everyone at no extra cost. We investigate the effect of using predictors for self and others' behavior in such a setting, as well as the new equilibria formed in this way.