Learning In The Presence Of Strategic Agents: Dynamics, Equilibria, And Convergence

          @misc{ scivideos_19621,
            doi = {},
            url = {https://simons.berkeley.edu/talks/learning-presence-strategic-agents-dynamics-equilibria-and-convergence},
            author = {},
            keywords = {},
            language = {en},
            title = {Learning In The Presence Of Strategic Agents: Dynamics, Equilibria, And Convergence},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {feb},
            note = {19621 see, \url{https://scivideos.org/Simons-Institute/19621}}
          }
          

Abstract

The ability to learn from data and make decisions in real-time has led to the rapid deployment of machine learning algorithms across many aspects of everyday life. While this has enabled new services and technologies, the fact that algorithms are increasingly interacting with people and other algorithms marks a distinct shift away from the traditional machine learning paradigm. Indeed, little is known about how these algorithms--- that were designed to operate in isolation--- behave when confronted with strategic behaviors on the part of people, and the extent to which strategic agents can game the algorithms to achieve better outcomes. In this talk, I will give an overview of my work on learning games and in the presence of strategic agents and multi-agent reinforcement learning.