Plenary Talk: Learning in Strategic Queuing

Tardos, Éva

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https://hdl.handle.net/2142/130246 Copy

Description

Title

Plenary Talk: Learning in Strategic Queuing

Author(s)

Tardos, Éva

Issue Date

2025-09-17

Keyword(s)

Front matter

Abstract

Over the last two decades, we have developed a good understanding of how to quantify the impact of strategic user behavior on outcomes in many games (including traffic routing and online auctions) and showed that the resulting bounds extend to repeated games assuming players use a form of learning (no-regret learning) to adapt to the environment. However, these results assume that there are no carry-over effects between rounds: outcomes in one round have no effect on the game in the future. Many repeated games have an evolving state resulting in direct carry-over effects, such as repeated auctions with budgets, as well as queuing systems. In this talk we will study this phenomenon in the context of a game modeling queuing systems: routers compete for servers, where packets that do not get served need to be resent, resulting in a system where the number of packets at each round depends on the success of the routers in the previous rounds. We study the required excess server capacity needed to guarantee that all packets get served in two different queuing systems (with or without buffers) despite the selfish (myopic) behavior of the participants.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Other

Language

eng

Handle URL

https://hdl.handle.net/2142/130246

Copyright and License Information

Copyright 2025 is held by Éva Tardos.

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