Demo Data from SIGIR 2026 / reviewers in the wild / expert
Grzegorz Kosiorowski
dblp:242/6283
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › learning in games
learning dynamics |
0.5 | 1 | 2021 | Follow-the-Regularized-Leader Routes to Chaos in Routing Games · ICML 2021 |
Algorithmic game theory and mechanism design › congestion games
selfish routing |
0.5 | 1 | 2021 | Follow-the-Regularized-Leader Routes to Chaos in Routing Games · ICML 2021 |
Algorithmic game theory and mechanism design
congestion games |
0.1 | 1 | 2021 | Follow-the-Regularized-Leader Routes to Chaos in Routing Games · ICML 2021 |
Methods — techniques the papers use, named apart from their topics
multiplicative weights update · 0.5follow-the-regularized-leader · 0.5chaos theory · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Follow-the-Regularized-Leader Routes to Chaos in Routing GamesabstractWe study the emergence of chaotic behavior of Follow-the-Regularized Leader (FoReL) dynamics in games. We focus on the effects of increasing the population size or the scale of costs in congestion games, and generalize recent results on unstable, chaotic behaviors in the Multiplicative Weights Update dynamics to a much larger class of FoReL dynamics. We establish that, even in simple linear non-atomic congestion games with two parallel links and \emph{any} fixed learning rate, unless the game is fully symmetric, increasing the population size or the scale of costs causes learning dynamics to becomes unstable and eventually chaotic, in the sense of Li-Yorke and positive topological entropy. Furthermore, we prove the existence of novel non-standard phenomena such as the coexistence of stable Nash equilibria and chaos in the same game. We also observe the simultaneous creation of a chaotic attractor as another chaotic attractor gets destroyed. Lastly, although FoReL dynamics can be strange and non-equilibrating, we prove that the time average still converges to an \emph{exact} equilibrium for any choice of learning rate and any scale of costs. Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michal Misiurewicz, Georgios Piliouras |
ICML | 4 |