We study the power of (competitive) algorithms with predictions in a multiagent setting. For this 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 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. Our main contribution is a best-response analysis of a single-agent competitive algorithm that assumes perfect knowledge of other agents' actions (but no knowledge of its own renting time). We then analyze the setting when agents have a predictor for their own active time, yielding a tradeoff between robustness and consistency. We investigate the effect of using such a predictor in an equilibrium, as well as the new equilibria formed in this way.

翻译：我们研究在多方场景下（竞争性）算法与预测的效力。为此，我们引入了滑雪租赁问题的多智能体版本。在该问题中，智能体可通过汇聚资源协作获取某项资产的团体许可证。若许可费用未达阈值，各智能体须以单价逐日单独租赁资产；反之，该许可证将永久免费开放给所有智能体。本文主要贡献在于对单智能体竞争算法进行最佳响应分析，该算法假设可完美获知其他智能体的行动（但未知自身租赁时长）。进而，我们分析了智能体拥有自身活跃时间预测器时的场景，揭示了鲁棒性与一致性之间的权衡。我们研究了在均衡状态中应用此类预测器的影响，以及由此形成的新型均衡态。