Evolution is the fundamental physical process that gives rise to biological phenomena. Yet it
is widely treated as a subset of population genetics, and thus its scope is artificially limited …

Owing to their connection with generative adversarial networks (GANs), saddle-point
problems have recently attracted considerable interest in machine learning and beyond. By …

Regularized learning is a fundamental technique in online optimization, machine learning,
and many other fields of computer science. A natural question that arises in this context is …

The Nash equilibrium—a combination of choices by the players of a game from which no
self-interested player would deviate—is the predominant solution concept in game theory …

A systematic, rigorous, comprehensive, and unified overview of evolutionary game theory.
This text offers a systematic, rigorous, and unified presentation of evolutionary game theory …

Evolutionary game dynamics is the application of population dynamical methods to game
theory. It has been introduced by evolutionary biologists, anticipated in part by classical …

Most existing results about last-iterate convergence of learning dynamics are limited to two-
player zero-sum games, and only apply under rigid assumptions about what dynamics the …

We introduce α-Rank, a principled evolutionary dynamics methodology, for the evaluation
and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical …

We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online
Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash …

Modern game theory goes back to a series of papers by the mathematician John von
Neumann in the 1920s. This program started a completely new branch of social sciences …