Equilibria in multiagent online problems with predictions
<|reference_start|>Equilibria in multiagent online problems with predictions: 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.<|reference_end|>
@article{istrate2024equilibria,
    title={Equilibria in multiagent online problems with predictions},
    author={Gabriel Istrate, Cosmin Bonchic{s}, Victor Bogdan},
    journal={arXiv preprint arXiv:2405.11873},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11873},
    primaryClass={cs.GT cs.DS cs.MA}
}