Although valued for its ability to allow teams to collaborate
and foster coalitional behaviors among the participants, game
theory s application to networking systems is not without
challenges. Distributed Strategic Learning for Wireless Engineers
illuminates the promise of learning in dynamic games as a tool for
analyzing network evolution and underlines the potential pitfalls
and difficulties likely to be encountered.
Establishing the link between several theories, this book
demonstrates what is needed to learn strategic interaction in
wireless networks under uncertainty, randomness, and time delays.
It addresses questions such as:
- How much information is enough for effective distributed
decision making?
- Is having more information always useful in terms of system
performance?
- What are the individual learning performance bounds under
outdated and imperfect measurement?
- What are the possible dynamics and outcomes if the players
adopt different learning patterns?
- If convergence occurs, what is the convergence time of
heterogeneous learning?
- What are the issues of hybrid learning?
- How can one develop fast and efficient learning schemes in
scenarios where some players have more information than the
others?
- What is the impact of risk-sensitivity in strategic learning
systems?
- How can one construct learning schemes in a dynamic environment
in which one of the players do not observe a numerical value of its
own-payoffs but only a signal of it?
- How can one learn "unstable" equilibria and global optima in a
fully distributed manner?
The book provides an explicit description of how players attempt
to learn over time about the game and about the behavior of others.
It focuses on finite and infinite systems, where the interplay
among the individual adjustments undertaken by the different
players generates different learning dynamics, heterogeneous
learning, risk-sensitive learning, and hybrid dynamics.
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