no code implementations • 29 Jan 2024 • Guru Guruganesh, Yoav Kolumbus, Jon Schneider, Inbal Talgam-Cohen, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Joshua R. Wang, S. Matthew Weinberg
We initiate the study of repeated contracts with a learning agent, focusing on agents who achieve no-regret outcomes.
1 code implementation • 24 Sep 2022 • Mira Finkelstein, Lucy Liu, Nitsan Levy Schlot, Yoav Kolumbus, David C. Parkes, Jeffrey S. Rosenshein, Sarah Keren
This has given rise to a variety of approaches to explainability in RL that aim to reconcile discrepancies that may arise between the behavior of an agent and the behavior that is anticipated by an observer.
no code implementations • 14 Dec 2021 • Yoav Kolumbus, Noam Nisan
The usage of automated learning agents is becoming increasingly prevalent in many online economic applications such as online auctions and automated trading.
no code implementations • 22 Oct 2021 • Yoav Kolumbus, Noam Nisan
We analyze a scenario in which software agents implemented as regret-minimizing algorithms engage in a repeated auction on behalf of their users.
no code implementations • 8 Nov 2019 • Yoav Kolumbus, Gali Noti
We show that if the available input is only of a short sequence of play, economic information about the game is important for predicting behavior of human agents.
no code implementations • 30 Nov 2016 • Gali Noti, Effi Levi, Yoav Kolumbus, Amit Daniely
A large body of work in behavioral fields attempts to develop models that describe the way people, as opposed to rational agents, make decisions.