Equilibria in multiagent online problems with predictions

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.