Selfish Jobs with Favorite Machines: Price of Anarchy vs Strong Price of Anarchy
Cong Chen, Paolo Penna, Yinfeng Xu

TL;DR
This paper analyzes the efficiency loss in selfish machine scheduling, providing tight bounds on the price of anarchy and strong price of anarchy, and compares these concepts in related machine settings.
Contribution
It extends prior work by establishing exact bounds on the price of anarchy and strong price of anarchy for related machines, offering new insights into their relationship.
Findings
Strong price of anarchy is strictly smaller than 2 for related machines.
The setting is easier than unrelated machines in terms of equilibrium efficiency.
Provides tight bounds that quantitatively compare the two concepts.
Abstract
We consider the well-studied game-theoretic version of machine scheduling in which jobs correspond to self-interested users and machines correspond to resources. Here each user chooses a machine trying to minimize her own cost, and such selfish behavior typically results in some equilibrium which is not globally optimal: An equilibrium is an allocation where no user can reduce her own cost by moving to another machine, which in general need not minimize the makespan, i.e., the maximum load over the machines. We provide tight bounds on two well-studied notions in algorithmic game theory, namely, the price of anarchy and the strong price of anarchy on machine scheduling setting which lies in between the related and the unrelated machine case. Both notions study the social cost (makespan) of the worst equilibrium compared to the optimum, with the strong price of anarchy restricting to a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
