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.

Format:

Book

Author/Creator:

Chen, Yan (Authority on networks), author.

Wang, Chih-Yu, 1984- author.

Liu, K. J. Ray, 1961- author.

Jiang, Chunxiao, 1987- author.

Language:

English

Subjects (All):

Game theory.

System analysis.

Physical Description:

1 online resource (xv, 457 pages) : digital, PDF file(s).

Edition:

1st ed.

Place of Publication:

Cambridge : Cambridge University Press, 2021.

Summary:

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.

Contents:

Cover

Half-title

Title page

Copyright information

Contents

Preface

1 Basic Game Theory

1.1 Strategic-Form Games and Nash Equilibrium

1.2 Extensive-Form Games and Subgame-Perfect Nash Equilibrium

1.3 Incomplete Information: Signal and Bayesian Equilibrium

1.4 Repeated Games and Stochastic Games

Part I Indirect Reciprocity

2 Indirect Reciprocity Game in Cognitive Networks

2.1 Introduction

2.2 The System Model

2.2.1 Social Norms

2.2.2 Action Rules

2.3 Optimal Action Rule

2.3.1 Reputation Updating Policy

2.3.2 Stationary Reputation Distribution

2.3.3 Payoff Function

2.3.4 Optimal Action Using an Alternative Algorithm

2.4 Action Spreading Due to Natural Selection

2.4.1 Action Spreading Algorithm Using the Wright-Fisher Model

2.4.2 Action Spreading Algorithm Using the Replicator Dynamic Equation

2.5 Evolutionarily Stable Strategy and Simulations

2.5.1 Binary Reputation Scenario

2.5.2 Multilevel Reputation Scenario

2.6 Conclusion

References

3 Indirect Reciprocity Game for Dynamic Channel Access

3.1 Introduction

3.2 System Model

3.2.1 Action

3.2.2 Social Norm: How to Assign Reputation

3.2.3 Power Level and Relay Power

3.2.4 Channel Quality Distribution

3.3 Theoretical Analysis

3.3.1 Reputation Updating Policy

3.3.2 Power Detection and Power Detection Transition Matrix

3.3.3 Stationary Reputation Distribution

3.3.4 Payoff Function and Equilibrium of the Indirect Reciprocity Game

3.3.5 Stability of the Optimal Action Rule

3.4 Simulation

3.4.1 Evolutionary Stability of Optimal Action a[sub(2) sup(*)]

3.4.2 System Performance

3.4.3 Different Social Norms

3.5 Conclusion

4 Multiuser Indirect Reciprocity Game for Cooperative Communications

4.1 Introduction

4.2 System Model.

4.2.1 Physical Layer Model with Relay Selection

4.2.2 Incentive Schemes Based on the Indirect Reciprocity Game

4.2.3 Overheads of the Scheme

4.2.4 Payoff Functions

4.3 Steady-State Analysis Using Markov Decision Processes

4.3.1 Stationary Reputation Distribution

4.3.2 Long-Term Expected Payoffs at Steady States

4.3.3 Equilibrium Steady State

4.4 Evolutionary Modeling of the Indirect Reciprocity Game

4.4.1 Evolutionary Dynamics of the Indirect Reciprocity Game

4.4.2 Evolutionarily Stable Strategy

4.5 Energy Detection

4.6 Simulation Results

4.7 Discussion and Conclusion

5 Indirect Reciprocity Data Fusion Game and Application to Cooperative Spectrum Sensing

5.1 Introduction

5.2 Indirect Reciprocity Data Fusion Game

5.2.1 System Model

5.2.2 Action and Action Rule

5.2.3 Social Norm: How to Assign Reputation

5.2.4 Decision Consistency Matrix

5.2.5 Reputation Updating Policy

5.2.6 Payoff Function

5.2.7 Equilibrium of the Indirect Reciprocity Data Fusion Game

5.3 Application to Cooperative Spectrum Sensing

5.3.1 System Model

5.3.2 Fusion Game for the Single-Channel (K=1) and Hard Fusion Case

5.3.3 Fusion Game for the Single-Channel (K=1) and Soft Fusion Case

5.3.4 Fusion Game for the Multichannel (K&gt

1) Case

5.4 Simulation

5.4.1 The Optimal Action Rule and Its Evolutionary Stability

5.4.2 System Performance

5.4.3 Anticheating

5.5 Conclusion

Part II Evolutionary Games

6 Evolutionary Game for Cooperative Peer-to-Peer Streaming

6.1 Introduction

6.2 The System Model and Utility Functions

6.2.1 System Model

6.2.2 Utility Functions

6.3 Agent Selection within a Homogeneous Group

6.3.1 Centralized Agent Selection

6.3.2 Distributed Agent Selection

6.3.3 Evolutionary Cooperative Streaming Game.

6.3.4 Analysis of the Cooperative Streaming Game

6.4 Agent Selection within a Heterogeneous Group

6.4.1 Two-Player Game

6.4.2 Multiplayer Game

6.5 A Distributed Learning Algorithm for an ESS

6.6 Simulation Results

6.7 Conclusion

7 Evolutionary Game for Spectrum Sensing and Access in Cognitive Networks

7.1 Introduction

7.2 System Model

7.2.1 Network Entity

7.2.2 Spectrum Sensing Model

7.2.3 Synchronous and Asynchronous Scenarios

7.3 Evolutionary Game Formulation for the Synchronous Scenario

7.3.1 Evolutionary Game

7.3.2 Replicator Dynamics of Spectrum Sensing

7.3.3 Replicator Dynamics of Spectrum Access

7.3.4 Analysis of the ESS

7.4 Evolutionary Game Formulation for the Asynchronous Scenario

7.4.1 ON-OFF Primary Channel Model

7.4.2 Analysis of SUs' Access Time T[sub(a)]

7.4.3 Analysis of the ESS

7.5 A Distributed Learning Algorithm for the ESSs

7.6 Simulation Results

7.6.1 ESSs of the Synchronous and Asynchronous Scenarios

7.6.2 Stability of the ESSs

7.6.3 Performance Evaluation

7.7 Conclusion

8 Graphical Evolutionary Game for Distributed Adaptive Networks

8.1 Introduction

8.2 Related Works

8.3 Graphical Evolutionary Game Formulation

8.3.1 Introduction to the Graphical Evolutionary Game

8.3.2 Graphical Evolutionary Game Formulation

8.3.3 Relationship to Existing Distributed Adaptive Filtering Algorithms

8.3.4 Error-Aware Distributed Adaptive Filtering Algorithm

8.4 Diffusion Analysis

8.4.1 Strategies and Utility Matrix

8.4.2 Dynamics of p[sub(m)] and q[sub(m|m)]

8.4.3 Diffusion Probability Analysis

8.5 Evolutionarily Stable Strategy

8.5.1 ESS in Complete Graphs

8.5.2 ESS in Incomplete Graphs

8.6 Simulation Results

8.6.1 Mean-Square Performance

8.6.2 Diffusion Probability.

8.6.3 Evolutionarily Stable Strategy

8.7 Conclusion

9 Graphical Evolutionary Game for Information Diffusion in Social Networks

9.1 Introduction

9.2 Diffusion Dynamics over Complete Networks

9.2.1 Basic Concepts of Evolutionary Game Theory

9.2.2 Evolutionary Game Formulation

9.2.3 Information Diffusion Dynamics over a Complete Network

9.3 Diffusion Dynamics over Uniform-Degree Networks

9.3.1 Basic Concepts of Graphical EGT

9.3.2 Graphical Evolutionary Game Formulation

9.3.3 Diffusion Dynamics over Uniform-Degree Networks

9.4 Diffusion Dynamics over Nonuniform-Degree Networks

9.4.1 General Case

9.4.2 Two Special Cases

9.5 Experiments

9.5.1 Synthetic Networks and a Real-World Network

9.5.2 Twitter Hashtag Data Set Evaluation

9.6 Conclusion

10 Graphical Evolutionary Game for Information Diffusion in Heterogeneous Social Networks

10.1 Introduction

10.2 Heterogeneous System Model

10.2.1 Basics of Evolutionary Game Theory

10.2.2 Unknown User-Type Model

10.2.3 Known User-Type Model

10.3 Theoretical Analysis for the Unknown User-Type Model

10.4 Theoretical Analysis for the Known User-Type Model

10.5 Experiments

10.5.1 Synthetic Data Experiments

10.5.2 Real Data Experiments

10.6 Discussion and Conclusion

Part III Sequential Decision-Making

11 Introduction to Sequential Decision-Making

11.1 Decision-Making in Networks

11.2 Social Learning

11.3 Multiarmed Bandit

11.4 Reinforcement Learning

12 Chinese Restaurant Game: Sequential Decision-Making in Static Systems

12.1 Introduction

12.2 System Model

12.3 Equilibrium Grouping and Advantage in Decision Order

12.3.1 Equilibrium Grouping

12.3.2 Subgame-Perfect Nash Equilibrium

12.4 Signals: Learning Unknown States

12.4.1 Best Response of Customers.

12.4.2 Recursive Form of the Best Response

12.5 Simulation Results and Analysis

12.5.1 Advantage of Playing Positions vs. Signal Quality

12.5.2 Price of Anarchy

12.5.3 Case Study: Resource Pool and Availability Scenarios

12.6 Application: Cooperative Spectrum Access in Cognitive Radio Networks

12.6.1 System Model

12.6.2 Simulation Results

12.7 Conclusion

13 Dynamic Chinese Restaurant Game: Sequential Decision-Making in Dynamic Systems

13.1 Introduction

13.2 System Model

13.2.1 Bayesian Learning for the Restaurant State

13.3 Multidimensional MDP-based Table Selection

13.4 Application to Cognitive Radio Networks

13.4.1 System Model

13.4.2 Bayesian Channel Sensing

13.4.3 Belief State Transition Probability

13.4.4 Channel Access: Two Primary Channels Case

13.4.5 Channel Access: Multiple Primary Channels Case

13.4.6 Analysis of Interference to the PU

13.5 Simulation Results

13.5.1 Bayesian Channel Sensing

13.5.2 Channel Access in the Two Primary Channels Case

13.5.3 Fast Algorithm for Multichannel Access

13.5.4 Interference Performance

13.6 Conclusion

14 Indian Buffet Game for Multiple Choices

14.1 Introduction

14.2 System Model

14.2.1 Indian Buffet Game Formulation

14.2.2 Time Slot Structure of the Indian Buffet Game

14.3 Indian Buffet Game without Budget Constraints

14.3.1 Recursive Best Response Algorithm

14.3.2 Subgame-Perfect Nash Equilibrium

14.3.3 Homogeneous Case

14.4 Indian Buffet Game with Budget Constraints

14.4.1 Recursive Best Response Algorithm

14.4.2 Subgame-Perfect Nash Equilibrium

14.4.3 Homogeneous Case

14.5 Non-Bayesian Social Learning

14.6 Simulation Results

14.6.1 Indian Buffet Game without Budget Constraints

14.6.2 Indian Buffet Game with Budget Constraints.

14.6.3 Non-Bayesian Social Learning Performance.

Notes:

Title from publisher's bibliographic system (viewed on 09 Jul 2021).

ISBN:

1-108-84903-2

1-108-85978-X

OCLC:

1249715358