16 citations · 27 across the 12 of their papers we have counts for
7 papers · 1 filter
Autobidding Equilibria in Sponsored Shopping
Paul Dütting, Yuhao Li, Renato Paes Leme +2
As commerce shifts to digital marketplaces, platforms increasingly monetize traffic through Sponsored Shopping auctions. Unlike classic ``Sponsored Search", where an advertiser typ…
Chaos in Autobidding Auctions
Ioannis Anagnostides, Ian Gemp, Georgios Piliouras +1
As autobidding systems increasingly dominate online advertising auctions, characterizing their long-term dynamical behavior is brought to the fore. In this paper, we examine the dy…
Tight Inapproximability for Welfare-Maximizing Autobidding Equilibria
Ioannis Anagnostides, Ian Gemp, Georgios Piliouras +1
We examine the complexity of computing welfare- and revenue-maximizing equilibria in autobidding second-price auctions subject to return-on-spend (RoS) constraints. We show that co…
Auto-bidding and Auctions in Online Advertising: A Survey
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro +23
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opp…
Complex Dynamics in Autobidding Systems
Renato Paes Leme, Georgios Piliouras, Jon Schneider +2
It has become the default in markets such as ad auctions for participants to bid in an auction through automated bidding agents (autobidders) which adjust bids over time to satisfy…
Nash, Conley, and Computation: Impossibility and Incompleteness in Game Dynamics
Jason Milionis, Christos Papadimitriou, Georgios Piliouras +1
Under what conditions do the behaviors of players, who play a game repeatedly, converge to a Nash equilibrium? If one assumes that the players' behavior is a discrete-time or conti…
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality
Stefanos Leonardos, Georgios Piliouras, Kelly Spendlove
The interplay between exploration and exploitation in competitive multi-agent learning is still far from being well understood. Motivated by this, we study smooth Q-learning, a pro…