5 papers
Breaking Algorithmic Collusion in Human-AI Ecosystems
Natalie Collina, Eshwar Ram Arunachaleswaran, Meena Jagadeesan
AI agents are increasingly deployed in ecosystems where they repeatedly interact not only with each other but also with humans. In this work, we study these human-AI ecosystems fro…
Swap Regret and Correlated Equilibria Beyond Normal-Form Games
Eshwar Ram Arunachaleswaran, Natalie Collina, Yishay Mansour +3
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 corre…
Learning to Play Against Unknown Opponents
Eshwar Ram Arunachaleswaran, Natalie Collina, Jon Schneider
We consider the problem of a learning agent who has to repeatedly play a general sum game against a strategic opponent who acts to maximize their own payoff by optimally responding…
Algorithmic Collusion Without Threats
Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan +2
There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, i…
An Elementary Predictor Obtaining Distance to Calibration
Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth +1
Blasiok et al. [2023] proposed distance to calibration as a natural measure of calibration error that unlike expected calibration error (ECE) is continuous. Recently, Qiao and Zhen…