Reciprocity, evolution, and decision games in network and data science:
Learn how to analyse and manage evolutionary and sequential user behaviours in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision making. Understand the latest theory without the need to go through the details of tradit...
Gespeichert in:
| Beteilige Person: | |
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| Weitere beteiligte Personen: | , , |
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2021
|
| Links: | https://doi.org/10.1017/9781108859783 |
| Zusammenfassung: | Learn how to analyse and manage evolutionary and sequential user behaviours in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behaviour, and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing. |
| Umfang: | 1 Online-Ressource (xv, 457 Seiten) |
| ISBN: | 9781108859783 |
Internformat
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| 100 | 1 | |a Chen, Yan | |
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| 700 | 1 | |a Jiang, Chunxiao |d 1987- | |
| 700 | 1 | |a Liu, K. J. Ray |d 1961- | |
| 700 | 1 | |a Wang, Chih-Yu |d 1984- | |
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| spelling | Chen, Yan Reciprocity, evolution, and decision games in network and data science Yan Chen, University of Science and Technology of China, Chih-Yu Wang, Academia Sinica, Chunxiao Jiang, Tsinghua University, K.J. Ray Liu, University of Maryland, College Park Cambridge Cambridge University Press 2021 1 Online-Ressource (xv, 457 Seiten) txt c cr Learn how to analyse and manage evolutionary and sequential user behaviours in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behaviour, and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing. Jiang, Chunxiao 1987- Liu, K. J. Ray 1961- Wang, Chih-Yu 1984- Erscheint auch als Druck-Ausgabe 9781108494748 |
| spellingShingle | Chen, Yan Reciprocity, evolution, and decision games in network and data science |
| title | Reciprocity, evolution, and decision games in network and data science |
| title_auth | Reciprocity, evolution, and decision games in network and data science |
| title_exact_search | Reciprocity, evolution, and decision games in network and data science |
| title_full | Reciprocity, evolution, and decision games in network and data science Yan Chen, University of Science and Technology of China, Chih-Yu Wang, Academia Sinica, Chunxiao Jiang, Tsinghua University, K.J. Ray Liu, University of Maryland, College Park |
| title_fullStr | Reciprocity, evolution, and decision games in network and data science Yan Chen, University of Science and Technology of China, Chih-Yu Wang, Academia Sinica, Chunxiao Jiang, Tsinghua University, K.J. Ray Liu, University of Maryland, College Park |
| title_full_unstemmed | Reciprocity, evolution, and decision games in network and data science Yan Chen, University of Science and Technology of China, Chih-Yu Wang, Academia Sinica, Chunxiao Jiang, Tsinghua University, K.J. Ray Liu, University of Maryland, College Park |
| title_short | Reciprocity, evolution, and decision games in network and data science |
| title_sort | reciprocity evolution and decision games in network and data science |
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