Eshwar Ram Arunachaleswaran
@epsilonrational.bsky.social
Studies Algorithmic game theory and online learning University of Pennsylvania/ Simons institute https://www.seas.upenn.edu/~eshwar/
Some fun work connecting no regret algorithms, pricing and collusion, with @aaroth.bsky.social , @ncollina.bsky.social , @jubaz.bsky.social and my advisor Sampath Kannan
Natalie Collina@ncollina.bsky.social · 10mo agoOur paper on algorithmic collusion was featured in a Quanta article! www.quantamagazine.org/the-game-the...
Our paper on algorithmic collusion was featured in a Quanta article! www.quantamagazine.org/the-game-the...
The Game Theory of How Algorithms Can Drive Up Prices | Quanta Magazine
Recent findings reveal that even simple pricing algorithms can make things more expensive.
quantamagazine.org
Ecstatic and deeply honored by this award. I've had great fun thinking about algorithms as strategies for repeated games over the past few years and hope that this highlight will push more researchers to come up with exciting directions in this field! Come to our talk on Monday to learn more!
Natalie Collina@ncollina.bsky.social · last yr.Delighted (and honestly a little bit stunned) that our paper “Swap Regret and Correlated Equilibria Beyond Normal-Form Games” was just awarded both the “Best Paper” and “Best Student Paper” at EC! arxiv.org/abs/2502.20229
What should swap-regret mean beyond normal-form games? We have a new paper (@ncollina.bsky.social, Mehryar Mohri, Yishay Mansour, Jon Schneider, Balu Sivan) tackling this question and providing a definitive answer! (thread) arxiv.org/abs/2502.20229
Swap Regret and Correlated Equilibria Beyond Normal-Form Games
Swap regret is a notion that has proven itself to be central to the study of general-sum normal-form games, with swap-regret minimization leading to convergence to the set of correlated equilibria and...
arxiv.org
Check out our new paper, on optimal algorithmic commitments against a distribution of opponents!
Natalie Collina@ncollina.bsky.social · 2y agoNew paper with @epsilonrational.bsky.social and Jon Schneider! Say you’re playing a repeated game against an opponent who will best-respond to your algorithm, but you only have a prior over their utility. What algorithm should you deploy to maximize your expected utility? arxiv.org/abs/2412.182...