activity
20242026
most citedSink equilibria and the attractors of learning in games

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.GT2026

On the Complexity of Learning Nash Equilibria

Oliver Biggar, Christos Papadimitriou, Georgios Piliouras

We know that the Nash equilibria of a game cannot be computed efficiently unless . But can they be learned? Are there dynamics that (1) can be computed efficiently by the…

cs.GT20261 cited

Sink equilibria and the attractors of learning in games

Oliver Biggar, Christos Papadimitriou

Characterizing the limit behavior -- that is, the attractors -- of learning dynamics is one of the most fundamental open questions in game theory. In recent work on this front, it…

cs.GT2026

Computing stable limit cycles of learning in games

Oliver Biggar, Christos Papadimitriou

Many well-studied learning dynamics, such as fictitious play and the replicator, are known to not converge in general -player games. The simplest mode of non-convergence is cycl…

cs.GT2025

Charting the Shapes of Stories with Game Theory

Constantinos Daskalakis, Ian Gemp, Yanchen Jiang +3

Stories are records of our experiences and their analysis reveals insights into the nature of being human. Successful analyses are often interdisciplinary, leveraging mathematical…

cs.GT2024

No-regret learning in harmonic games: Extrapolation in the face of conflicting interests

Davide Legacci, Panayotis Mertikopoulos, Christos H. Papadimitriou +2

The long-run behavior of multi-agent learning - and, in particular, no-regret learning - is relatively well-understood in potential games, where players have aligned interests. By…

cs.GT2024

Swim till You Sink: Computing the Limit of a Game

Rashida Hakim, Jason Milionis, Christos Papadimitriou +1

During 2023, two interesting results were proven about the limit behavior of game dynamics: First, it was shown that there is a game for which no dynamics converges to the Nash equ…