Distributed Strategic Learning for Wireless Engineers (Hardcover)

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• What are the individual learning performance bounds under outdated and imperfect measurement?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.

儘管協作和促進參與者之間的聯合行為是博弈論在網絡系統中的應用所重視的，但也面臨著挑戰。《無線工程師的分散策略學習》闡明了動態博弈中學習的潛力作為分析網絡演化的工具，並強調了可能遇到的潛在困難和障礙。

本書建立了幾個理論之間的聯繫，展示了在不確定性、隨機性和時間延遲下學習無線網絡中的戰略互動所需的內容。它回答了以下問題：

- 有效的分散決策需要多少信息？
- 在系統性能方面，擁有更多信息是否總是有用的？
- 在過時和不完美的測量下，個體學習的性能界限是什麼？
- 如果玩家採用不同的學習模式，可能的動態和結果是什麼？
- 如果收斂發生，異質學習的收斂時間是多少？
- 混合學習的問題是什麼？
- 在某些玩家擁有更多信息的情況下，如何開發快速高效的學習方案？
- 戰略學習系統中風險敏感性的影響是什麼？
- 在動態環境中，如何構建學習方案，其中一個玩家只能觀察到自己收益的信號而不是數值？
- 如何以完全分散的方式學習“不穩定”的均衡和全局最優解？

本書詳細描述了玩家如何隨著時間的推移學習遊戲和他人的行為。它專注於有限和無限系統，其中不同玩家進行的個體調整相互作用產生不同的學習動態、異質學習、風險敏感學習和混合動態。