A brief overview of game theory
|Lecturer:||Michael Muehlebach (MPI-IS)|
|Time:||– (Zurich time)|
|Notes:||Click here to download!|
|Recording:||Click here to view! (only for ETH members)|
Abstract:The lecture will summarize key ideas in game theory. Game theory provides a means for modelling interactions between machine learning algorithms and their environment. We will revisit zero-sum games and von Neumann’s minimax theorem and introduce the concept of Nash equilibria. We will then discuss repeated games and adaptive decision-making algorithms (follow the leader, follow a random leader, multiplicative weights).
- Karlin, A. R., & Peres, Y. (2017). Game theory, alive. American Mathematical Society. ISBN: 978-1-4704-1982-0. PDF version available online. [Chapter 2: Section 2.1–2.3; Chapter 18: Section 18.1–18.3]