Stochastic Games on Directed Networks

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Page 3 of 4

Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.

Explores algebraic graph theory applied to networked control systems and consensus algorithms.

Covers stochastic models for communications and the estimation of correlation functions.

Covers learning dynamics in finite action games and explores various types of equilibria, including correlated and coarse correlated equilibria.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Explores multistage games, focusing on extensive form and feedback strategies, including Nash equilibria and backward induction methods.

Delves into learning dynamics in games, exploring chaos, limit cycles, and the interplay between convergence and chaos.

Covers the review of random variables, probability density functions, variance, and Gaussian processes.

Covers vector spaces in R2 and R3, including planes and lines.

Explains the front door criterion in causal inference and its sufficient conditions for variables blocking paths effectively.