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Multilevel Strategic Interaction Game Models for Complex Networks

ISBN-13: 9783030244545 / Angielski / Twarda / 2019 / 322 str.

Eitan Altman; Konstantin Avrachenkov; Francesco De Pellegrini
Multilevel Strategic Interaction Game Models for Complex Networks Eitan Altman Konstantin Avrachenkov Francesco D 9783030244545 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Multilevel Strategic Interaction Game Models for Complex Networks

ISBN-13: 9783030244545 / Angielski / Twarda / 2019 / 322 str.

Eitan Altman; Konstantin Avrachenkov; Francesco De Pellegrini
cena 443,82 zł
(netto: 422,69 VAT:  5%)

Najniższa cena z 30 dni: 424,07 zł
Termin realizacji zamówienia:
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inne wydania
Kategorie:
Nauka, Matematyka
Kategorie BISAC:
Mathematics > Matematyka stosowana
Science > System Theory
Technology & Engineering > Engineering (General)
Wydawca:
Springer
Seria wydawnicza:
Understanding Complex Systems
Język:
Angielski
ISBN-13:
9783030244545
Rok wydania:
2019
Wydanie:
2019
Numer serii:
000307910
Ilość stron:
322
Waga:
0.65 kg
Wymiary:
23.62 x 16.26 x 2.54
Oprawa:
Twarda
Wolumenów:
01
Dodatkowe informacje:
Wydanie ilustrowane

Preface

Part I Multi-Level Evolutionary Games

Chapter 1 Introduction

Chapter 2 Altruism in Groups
2.1 Classical Evolutionary Games and ESS; 2.2 New natural concept on evolutionary games; 2.3 Analysis of N-groups games with two strategies; 2.4 Some Examples; 2.5 Multiple Access Control; 2.6 Conclusions

Chapter 3 From Egoism to Altruism in Groups
3.1 System Model; 3.2 Utility Functions; 3.3 Computing the ESS; 3.4 Replicator Dynamics; 3.5 Optimization Issues; 3.6 Numerical Results; 3.7 Conclusion

Chapter 4 Evolutionary Games in Interacting Communities
4.1 Introduction; 4.2 Evolutionarily Stable Strategies; 4.3 Two-community two-strategy model; 4.4 Replicator Dynamics; 4.5 Application to Hawk- Dove game; 4.6 Interacting communities on random graphs; 4.7 Conclusion; 4.8 Proofs

Chapter 5 Random Time Delays in Evolutionary Game Dynamics
5.1 Evolutionary Games; 5.2 Replicator dynamics with continuous random delays; 5.3 Replicator dynamics with one delayed strategy; 5.4 Conclusions; 5.5 Proof of Theorem 8; 5.6 Proof of Theorem 10 

Chapter 6 Coupled State Policy Dynamics in Evolutionary Games
6.1 Standard Evolutionary Game Theory; 6.2 Individual State in EGT framework; 6.3 Two time-scales behavior; 6.4 Numerical Illustrations; 6.5 Conclusions and perspectives

Part II Epidemics/Information Diffusion

Chapter 7 Introduction

Chapter 8 Community Network with Equitable Partitions
8.1 Equitable Partitions; 8.2 Example; 8.3 Almost equitable partitions; 8.4 Conclusion 

Chapter 9 Epidemic Spreading on Interconnected Networks
9.1 Effect on the Epidemic Threshold; 9.2 Effect of the Spatial Constraints of Interconnected Networks

Chapter 10 Co-Evolution/ Adaptive Epidemics
10.1 Adaptive SIS model; 10.2 Comparison of the ASIS and AID model; 10.3 Conclusion 

Part III Networking Games

Chapter 11 Introduction

Chapter 12 “Beat your Rival” Routing Games
12.0.1 Solution concept; 12.1 Model and Game Theoretic Formulations; 12.2 Bottleneck Routing Games; 12.3 Additive Routing Games; 12.4 Conclusions

Chapter 13 Network Formation Games with Teams
13.1 Network Formation Games with Teams: Basic definitions; 13.2 Network Formation Games with Teams: A Motivating Example; 13.3 Stability of Networks Against Teams’ Coalitions; 13.4 Dynamic formation of networks under coalitions; 13.5 Simulation Results; 13.6 Conclusions

Chapter 14 Peering vs Transit: A game theoretical model for autonomous systems connectivity
14.1 Introduction; 14.2 Related Work; 14.3 Scenario; 14.4 General Model; 14.5 Minimal Complexity Model (MCM); 14.6 Simulations; 14.7 Conclusions; 14.8 Appendix

Chapter 15 Network Neutrality
15.1 Introduction; 15.2 Computing the equilibrium; 15.3 Paradox and Price of Anarchy; 15.4 Collusions and Conclusions

Part IV Intermittency in Complex Systems

Chapter 16 Introduction

17 Models with Adaptive Intermittent Behavior
17.1 Tangled Nature Model; 17.2 The Stochastic Replicator Model

Chapter 18 Outline of the Forecasting Procedure
18.1 Mean-Field Description; 18.2 Linear Stability Theory: Continuum Approximation; 18.3 TNM Mean Field Description; 18.4 SRM Mean Field Description

Chapter 19 Forecasting Procedure Based on Full Information
19.1 Procedure; 19.2 Results; 19.3 Analysis of the Stability Indicator Q(t); 19.4 Discussion 

Chapter 20 Procedures with Incomplete Information
20.1 Interactions with Error; 20.2 New Procedure; 20.3 Results; 20.4

Chapter 21 Conclusion

Konstantin Avrachenkov received Master degree in Control Theory from St. Petersburg State Polytechnic University (1996), Ph.D. degree in Mathematics from University of South Australia (2000) and Habilitation (Doctor of Science) from University of Nice Sophia Antipolis (2010). Currently, he is a Director of Research at Inria Sophia Antipolis, France. He is an associate editor of International Journal of Performance Evaluation, Stochastic Models, Probability in the Engineering and Informational Sciences and ACM TOMPECS . His main research interests are Markov processes, singular perturbation theory, queueing theory, mathematical programming, game theory and performance evaluation of communication networks.

Francesco De Pellegrini is full professor at the University of Avignon. He received the Ph.D. degree in 2004 in Telecommunication Engineering from the University of Padova, Italy. He has published more than 100 papers in conference and journals and received 2 best paper awards. He has been working on several subjects in the domain of computer science and networks. His interests are algorithms on graphs, epidemics and gossip processes, stochastic control of networks and game theory. He has promoted and chaired  several conferences in the field of networking and computer science. 


Huijuan Wang is currently a tenured assistant professor in the Multimedia Computing Group at Delft University of Technology. She received the M.Sc. degree (cum laude) and Ph.D. degree (cum laude) in Electrical Engineering from the Delft University of Technology in 2005 and 2009, respectively. She has been a visiting scientist in the Department of Physics at Boston University since 2011 and in the Department of Electrical Engineering at Stanford University for the period April-August 2015. Her main research areas are performance analysis of complex networks and systems ranging from technological networks, economical systems to brain networks, modelling, prediction and control of dynamic processes e.g. viral spreading, opinion formation and cascading failures on multi-layer interdependent networks.


Dr. Altman (IEEE fellow) has been a researcher at INRIA, France since 1990. He has been in editorial boards of many journals such as IEEE/ACM ToN, Wireless Networks, Computer Networks,  Computer Communications, SIAM J. of Control and Optimisation. He is a coauthor of 8 papers that received best paper or student paper awards. He received the Grand Prix  de France Telecom from the French Academy of Sciences (2009), the ISAACS Award from the Society of Dynamic Games (2014),  the Distinguished Technical Achievement Recognition Award by IEEE TC on Big Data (2017). Has over 20000 citations with 68 H number.


Rachid El-Azouzi  is a full professor at university of Avignon, France. He has been a visiting scientist in the Department of Computer Science in California University in Berekely  since 2012.  He currently serves  as an associate editor for IEEE Transactions on Networking since 2016. 
Rachid El-Azouzi is the co-recipient of 5 Best Paper Awards, and he has authored or co-authored over 70 journal articles and book chapters and over 170 conference publications, in the general areas of game theory, complex system, Evolutionary games, networking games, wireless networks, network economics, stochastic control and delay tolerant networks.

This book provides a state-of-the-art overview on the dynamics and coevolution in multi-level strategic interaction games. As such it summarizes the results of the European CONGAS project, which developed new mathematical models and tools for the analysis, prediction and control of dynamical processes in systems possessing a rich multi-level structure and a web of interwoven interactions among elements with autonomous decision-making capabilities.


The framework is built around game theoretical concepts, in particular evolutionary and multi-resolution games, and includes also techniques drawn from graph theory, statistical mechanics, control and optimization theory. Specific attention is devoted to systems that are prone to intermittency and catastrophic events due to the effect of collective dynamics.



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