Atnaujinkite slapukų nuostatas

El. knyga: Reciprocity, Evolution, and Decision Games in Network and Data Science

(Tsinghua University, Beijing), , (University of Maryland, College Park), (University of Science and Technology of China)
  • Formatas: EPUB+DRM
  • Išleidimo metai: 22-Jul-2021
  • Leidėjas: Cambridge University Press
  • Kalba: eng
  • ISBN-13: 9781108849036
Kitos knygos pagal šią temą:
Reciprocity, Evolution, and Decision Games in Network and Data ScienceDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 22-Jul-2021
  • Leidėjas: Cambridge University Press
  • Kalba: eng
  • ISBN-13: 9781108849036
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

"Learn how to analyze and manage evolutionary and sequential user behaviors 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 behavior 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"--

Recenzijos

'This advanced text is a great start for more in-depth explorations into complex system-modeling areas. Recommended.' J. Brzezinski, Choice Connect

Daugiau informacijos

A unique treatment of evolutionary games, indirect reciprocity, sequential decision making, and application to wireless and social networks.
Preface xiii
1 Basic Game Theory
1(6)
1.1 Strategic-Form Games and Nash Equilibrium
1(1)
1.2 Extensive-Form Games and Subgame-Perfect Nash Equilibrium
2(2)
1.3 Incomplete Information: Signal and Bayesian Equilibrium
4(1)
1.4 Repeated Games and Stochastic Games
5(2)
Part I Indirect Reciprocity
7(100)
2 Indirect Reciprocity Game in Cognitive Networks
9(19)
2.1 Introduction
9(2)
2.2 The System Model
11(2)
2.2.1 Social Norms
12(1)
2.2.2 Action Rules
13(1)
2.3 Optimal Action Rule
13(5)
2.3.1 Reputation Updating Policy
13(2)
2.3.2 Stationary Reputation Distribution
15(1)
2.3.3 Payoff Function
16(1)
2.3.4 Optimal Action Using an Alternative Algorithm
17(1)
2.4 Action Spreading Due to Natural Selection
18(2)
2.4.1 Action Spreading Algorithm Using the Wright-Fisher Model
19(1)
2.4.2 Action Spreading Algorithm Using the Replicator Dynamic Equation
19(1)
2.5 Evolutionarily Stable Strategy and Simulations
20(5)
2.5.1 Binary Reputation Scenario
21(1)
2.5.2 Multilevel Reputation Scenario
22(3)
2.6 Conclusion
25(3)
References
26(2)
3 Indirect Reciprocity Game for Dynamic Channel Access
28(27)
3.1 Introduction
28(2)
3.2 System Model
30(3)
3.2.1 Action
31(1)
3.2.2 Social Norm: How to Assign Reputation
31(1)
3.2.3 Power Level and Relay Power
32(1)
3.2.4 Channel Quality Distribution
33(1)
3.3 Theoretical Analysis
33(12)
3.3.1 Reputation Updating Policy
33(2)
3.3.2 Power Detection and Power Detection Transition Matrix
35(2)
3.3.3 Stationary Reputation Distribution
37(1)
3.3.4 Payoff Function and Equilibrium of the Indirect Reciprocity Game
38(3)
3.3.5 Stability of the Optimal Action Rule
41(4)
3.4 Simulation
45(7)
3.4.1 Evolutionary Stability of Optimal Action flj
45(2)
3.4.2 System Performance
47(2)
3.4.3 Different Social Norms
49(3)
3.5 Conclusion
52(3)
References
52(3)
4 Multiuser Indirect Reciprocity Game for Cooperative Communications
55(25)
4.1 Introduction
55(2)
4.2 System Model
57(5)
4.2.1 Physical Layer Model with Relay Selection
57(2)
4.2.2 Incentive Schemes Based on the Indirect Reciprocity Game
59(1)
4.2.3 Overheads of the Scheme
60(1)
4.2.4 Payoff Functions
61(1)
4.3 Steady-State Analysis Using Markov Decision Processes
62(7)
4.3.1 Stationary Reputation Distribution
62(1)
4.3.2 Long-Term Expected Payoffs at Steady States
63(2)
4.3.3 Equilibrium Steady State
65(4)
4.4 Evolutionary Modeling of the Indirect Reciprocity Game
69(1)
4.4.1 Evolutionary Dynamics of the Indirect Reciprocity Game
69(1)
4.4.2 Evolutionarily Stable Strategy
70(1)
4.5 Energy Detection
70(2)
4.6 Simulation Results
72(5)
4.7 Discussion and Conclusion
77(3)
References
78(2)
5 Indirect Reciprocity Data Fusion Game and Application to Cooperative Spectrum Sensing
80(27)
5.1 Introduction
80(2)
5.2 Indirect Reciprocity Data Fusion Game
82(7)
5.2.1 System Model
82(1)
5.2.2 Action and Action Rule
83(1)
5.2.3 Social Norm: How to Assign Reputation
84(1)
5.2.4 Decision Consistency Matrix
84(1)
5.2.5 Reputation Updating Policy
85(2)
5.2.6 Payoff Function
87(2)
5.2.7 Equilibrium of the Indirect Reciprocity Data Fusion Game
89(1)
5.3 Application to Cooperative Spectrum Sensing
89(10)
5.3.1 System Model
90(1)
5.3.2 Fusion Game for the Single-Channel {K = 1) and Hard Fusion Case
91(4)
5.3.3 Fusion Game for the Single-Channel (K = 1) and Soft Fusion Case
95(2)
5.3.4 Fusion Game for the Multichannel (K > 1) Case
97(2)
5.4 Simulation
99(5)
5.4.1 The Optimal Action Rule and Its Evolutionary Stability
99(2)
5.4.2 System Performance
101(2)
5.4.3 Anticheating
103(1)
5.5 Conclusion
104(3)
References
105(2)
Part II Evolutionary Games
107(140)
6 Evolutionary Game for Cooperative Peer-to-Peer Streaming
109(22)
6.1 Introduction
109(2)
6.2 The System Model and Utility Functions
111(3)
6.2.1 System Model
111(1)
6.2.2 Utility Functions
112(2)
6.3 Agent Selection within a Homogeneous Group
114(6)
6.3.1 Centralized Agent Selection
114(1)
6.3.2 Distributed Agent Selection
114(1)
6.3.3 Evolutionary Cooperative Streaming Game
115(1)
6.3.4 Analysis of the Cooperative Streaming Game
116(4)
6.4 Agent Selection within a Heterogeneous Group
120(2)
6.4.1 Two-Player Game
120(2)
6.4.2 Multiplayer Game
122(1)
6.5 A Distributed Learning Algorithm for an ESS
122(1)
6.6 Simulation Results
123(5)
6.7 Conclusion
128(3)
References
128(3)
7 Evolutionary Game for Spectrum Sensing and Access in Cognitive Networks
131(27)
7.1 Introduction
131(2)
7.2 System Model
133(3)
7.2.1 Network Entity
133(1)
7.2.2 Spectrum Sensing Model
134(1)
7.2.3 Synchronous and Asynchronous Scenarios
135(1)
7.3 Evolutionary Game Formulation for the Synchronous Scenario
136(7)
7.3.1 Evolutionary Game
136(2)
7.3.2 Replicator Dynamics of Spectrum Sensing
138(1)
7.3.3 Replicator Dynamics of Spectrum Access
138(1)
7.3.4 Analysis of the ESS
139(4)
7.4 Evolutionary Game Formulation for the Asynchronous Scenario
143(5)
7.4.1 ON--OFF Primary Channel Model
143(1)
7.4.2 Analysis of SUs' Access Time Ta
144(2)
7.4.3 Analysis of the ESS
146(2)
7.5 A Distributed Learning Algorithm for the ESSs
148(3)
7.6 Simulation Results
151(4)
7.6.1 ESSs of the Synchronous and Asynchronous Scenarios
151(2)
7.6.2 Stability of the ESSs
153(1)
7.6.3 Performance Evaluation
154(1)
7.7 Conclusion
155(3)
References
155(3)
8 Graphical Evolutionary Game for Distributed Adaptive Networks
158(28)
8.1 Introduction
158(1)
8.2 Related Works
159(3)
8.3 Graphical Evolutionary Game Formulation
162(7)
8.3.1 Introduction to the Graphical Evolutionary Game
162(2)
8.3.2 Graphical Evolutionary Game Formulation
164(2)
8.3.3 Relationship to Existing Distributed Adaptive Filtering Algorithms
166(1)
8.3.4 Error-Aware Distributed Adaptive Filtering Algorithm
167(2)
8.4 Diffusion Analysis
169(7)
8.4.1 Strategies and Utility Matrix
170(2)
8.4.2 Dynamics of pm and qm\m
172(2)
8.4.3 Diffusion Probability Analysis
174(2)
8.5 Evolutionarily Stable Strategy
176(2)
8.5.1 ESS in Complete Graphs
177(1)
8.5.2 ESS in Incomplete Graphs
177(1)
8.6 Simulation Results
178(5)
8.6.1 Mean-Square Performance
179(3)
8.6.2 Diffusion Probability
182(1)
8.6.3 Evolutionarily Stable Strategy
182(1)
8.7 Conclusion
183(3)
References
183(3)
9 Graphical Evolutionary Game for Information Diffusion in Social Networks
186(30)
9.1 Introduction
186(3)
9.2 Diffusion Dynamics over Complete Networks
189(4)
9.2.1 Basic Concepts of Evolutionary Game Theory
189(1)
9.2.2 Evolutionary Game Formulation
190(1)
9.2.3 Information Diffusion Dynamics over a Complete Network
191(2)
9.3 Diffusion Dynamics over Uniform-Degree Networks
193(9)
9.3.1 Basic Concepts of Graphical EGT
193(1)
9.3.2 Graphical Evolutionary Game Formulation
194(1)
9.3.3 Diffusion Dynamics over Uniform-Degree Networks
195(7)
9.4 Diffusion Dynamics over Nonuniform-Degree Networks
202(3)
9.4.1 General Case
202(2)
9.4.2 Two Special Cases
204(1)
9.5 Experiments
205(8)
9.5.1 Synthetic Networks and a Real-World Network
205(3)
9.5.2 Twitter Hashtag Data Set Evaluation
208(5)
9.6 Conclusion
213(3)
References
213(3)
10 Graphical Evolutionary Game for Information Diffusion in Heterogeneous Social Networks
216(31)
10.1 Introduction
216(2)
10.2 Heterogeneous System Model
218(4)
10.2.1 Basics of Evolutionary Game Theory
218(2)
10.2.2 Unknown User-Type Model
220(1)
10.2.3 Known User-Type Model
221(1)
10.3 Theoretical Analysis for the Unknown User-Type Model
222(5)
10.4 Theoretical Analysis for the Known User-Type Model
227(5)
10.5 Experiments
232(10)
10.5.1 Synthetic Data Experiments
232(6)
10.5.2 Real Data Experiments
238(4)
10.6 Discussion and Conclusion
242(5)
References
244(3)
Part III Sequential Decision-Making
247(205)
11 Introduction to Sequential Decision-Making
249(4)
11.1 Decision-Making in Networks
249(1)
11.2 Social Learning
250(1)
11.3 Multiarmed Bandit
251(1)
11.4 Reinforcement Learning
251(2)
12 Chinese Restaurant Game: Sequential Decision-Making in Static Systems
253(28)
12.1 Introduction
253(3)
12.2 System Model
256(2)
12.3 Equilibrium Grouping and Advantage in Decision Order
258(6)
12.3.1 Equilibrium Grouping
258(2)
12.3.2 Subgame-Perfect Nash Equilibrium
260(4)
12.4 Signals: Learning Unknown States
264(3)
12.4.1 Best Response of Customers
265(1)
12.4.2 Recursive Form of the Best Response
265(2)
12.5 Simulation Results and Analysis
267(6)
12.5.1 Advantage of Playing Positions vs. Signal Quality
268(1)
12.5.2 Price of Anarchy
269(1)
12.5.3 Case Study: Resource Pool and Availability Scenarios
270(3)
12.6 Application: Cooperative Spectrum Access in Cognitive Radio Networks
273(5)
12.6.1 System Model
273(2)
12.6.2 Simulation Results
275(3)
12.7 Conclusion
278(3)
References
279(2)
13 Dynamic Chinese Restaurant Game: Sequential Decision-Making in Dynamic Systems
281(29)
13.1 Introduction
281(2)
13.2 System Model
283(4)
13.2.1 Bayesian Learning for the Restaurant State
284(3)
13.3 Multidimensional MDP-based Table Selection
287(5)
13.4 Application to Cognitive Radio Networks
292(11)
13.4.1 System Model
293(1)
13.4.2 Bayesian Channel Sensing
294(3)
13.4.3 Belief State Transition Probability
297(1)
13.4.4 Channel Access: Two Primary Channels Case
298(3)
13.4.5 Channel Access: Multiple Primary Channels Case
301(1)
13.4.6 Analysis of Interference to the PU
302(1)
13.5 Simulation Results
303(4)
13.5.1 Bayesian Channel Sensing
303(1)
13.5.2 Channel Access in the Two Primary Channels Case
304(2)
13.5.3 Fast Algorithm for Multichannel Access
306(1)
13.5.4 Interference Performance
307(1)
13.6 Conclusion
307(3)
References
308(2)
14 Indian Buffet Game for Multiple Choices
310(31)
14.1 Introduction
310(2)
14.2 System Model
312(4)
14.2.1 Indian Buffet Game Formulation
312(2)
14.2.2 Time Slot Structure of the Indian Buffet Game
314(2)
14.3 Indian Buffet Game without Budget Constraints
316(6)
14.3.1 Recursive Best Response Algorithm
318(1)
14.3.2 Subgame-Perfect Nash Equilibrium
319(1)
14.3.3 Homogeneous Case
320(2)
14.4 Indian Buffet Game with Budget Constraints
322(5)
14.4.1 Recursive Best Response Algorithm
322(3)
14.4.2 Subgame-Perfect Nash Equilibrium
325(1)
14.4.3 Homogeneous Case
325(2)
14.5 Non-Bayesian Social Learning
327(4)
14.6 Simulation Results
331(7)
14.6.1 Indian Buffet Game without Budget Constraints
332(3)
14.6.2 Indian Buffet Game with Budget Constraints
335(1)
14.6.3 Non-Bayesian Social Learning Performance
336(1)
14.6.4 Application in Relay Selection of Cooperative Communication
336(2)
14.7 Conclusion
338(3)
References
339(2)
15 Hidden Chinese Restaurant Game: Learning from Actions
341(29)
15.1 Introduction
341(3)
15.2 System Models
344(3)
15.2.1 Customers: Naive and Rational
345(1)
15.2.2 Observable Information
346(1)
15.3 Hidden Chinese Restaurant Game
347(9)
15.3.1 System State Transition
350(1)
15.3.2 Grand Information Extraction
351(2)
15.3.3 Equilibrium Conditions
353(3)
15.4 Solutions
356(3)
15.4.1 Centralized Policy
356(1)
15.4.2 Nash Equilibrium
357(2)
15.5 Application: Channel Access in Cognitive Radio Networks
359(6)
15.5.1 Simulation Results
361(4)
15.6 Conclusion
365(1)
15.7 Literature Review
366(4)
References
368(2)
16 Wireless Network Access with Mechanism Design
370(27)
16.1 Introduction
370(2)
16.2 System Model and Problem Formulation
372(4)
16.2.1 System Model
372(3)
16.2.2 Expected Utility
375(1)
16.2.3 Best Response of Rational Users
376(1)
16.3 Modified Value Iteration Algorithm
376(2)
16.4 Threshold Structure of the Strategy Profile
378(2)
16.5 Truthful Mechanism Design
380(6)
16.5.1 Solution
384(2)
16.6 Numerical Simulation
386(6)
16.7 Discussion
392(1)
16.8 Conclusion
392(1)
16.9 Literature Review
393(4)
References
394(3)
17 Deal Selection on Social Media with Behavior Prediction
397(26)
17.1 Introduction
397(3)
17.2 Cross-Social Media Data Set
400(5)
17.3 System Model
405(2)
17.3.1 External Information from Social Media
406(1)
17.4 Stochastic DSG
407(7)
17.4.1 Multidimensional Markov Decision Process
407(1)
17.4.2 State Transition
408(3)
17.4.3 Expected Utility and Strategy Profile
411(1)
17.4.4 Nash Equilibrium and Value Iteration Algorithm
412(2)
17.5 Simulation Results
414(3)
17.5.1 Review Accuracy
416(1)
17.5.2 Arrival Rate
416(1)
17.6 Experiments: Are Customers Rational?
417(2)
17.7 Conclusion
419(1)
17.8 Literature Review
420(3)
References
421(2)
18 Social Computing: Answer vs. Vote
423(29)
18.1 Introduction
423(3)
18.2 System Model
426(4)
18.3 Equilibrium Analysis
430(7)
18.4 Extensions to Endogenous Effort
437(3)
18.5 Empirical Evaluations
440(3)
18.5.1 Data Set Description
440(1)
18.5.2 Observations and Validations
441(2)
18.6 Numerical Simulations
443(5)
18.6.1 Simulation Settings
443(1)
18.6.2 Simulation Results for Homogeneous Effort
444(3)
18.6.3 Simulation Results for Endogenous Effort
447(1)
18.7 Conclusion
448(1)
18.8 Literature Review
449(3)
References
450(2)
Index 452
Yan Chen is a Professor at School of Cyberspace Security, University of Science and Technology of China. Chih-Yu Wang is an Associate Research Fellow at the Research Center for Information Technology Innovation, Academia Sinica. Chunxiao Jiang is an associate professor in the School of Information Science and Technology at Tsinghua University. K.J. Ray Liu is Distinguished University Professor at the University of Maryland, College Park. A Highly Cited Researcher, he is a Fellow of the IEEE and the American Association for the Advancement of Science (AAAS), and National Academy of Inventors. He is the 2021 IEEE President Elect. He is a recipient of the IEEE Fourier Award for Signal Processing and the IEEE Leon K. Kirchmayer Graduate Teaching Award, the IEEE Signal Processing Society 2014 Society Award, and the IEEE Signal Processing Society 2009 Technical Achievement Award. He has also co-authored several books, including Wireless AI (Cambridge, 2019).
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97811088490366e.html
Raktažodžiai: