Cover Image

Social Learning Opinion Formation and Decision-Making over Graphs.

The ebook edition of this title is Open Access and freely available to read online. This book explores how agents in complex systems--like social networks, robotic swarms, or biological networks--interact and learn through information diffusion and decision-making over graphs.

Bibliographic Details
Main Author: Matta, Vincenzo
Corporate Author: Emerald Publishing Group
Other Authors: Bordignon, Virginia, Sayed, Ali H.
Format: eBook
Language:English
Published: Leeds : Now Publishers, 2025.
Subjects:
Social learning.
Apprentissage social.
Computers > Data Science > Machine Learning.
Machine learning.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Cover
  • SOCIAL LEARNING
  • EURASIP-Now Publishers Open Access Book Series on Information and Learning Sciences
  • Copyright
  • Dedication
  • Contents
  • Preface
  • Acknowledgments
  • Chapter 1 Introduction
  • Examples of Social Learning
  • Building Opinions
  • Book Organization
  • Notation, Symbols, and Conventions
  • Chapter 2 Bayesian Learning
  • The Bayesian Way
  • From Priors and Likelihoods to Beliefs
  • Properties of Bayes' Rule
  • Information-Theoretic Interpretations
  • Stochastic-Optimization Interpretation
  • Chapter 3 From Single-Agent to Social Learning
  • Bayesian versus Non-Bayesian Learning
  • Non-Bayesian Social Learning
  • Information-Theoretic Viewpoint
  • Geometric-Averaging Rule
  • Arithmetic-Averaging Rule
  • Behavioral Viewpoint
  • Geometric-Averaging Rule, Revisited
  • Arithmetic-Averaging Rule, Revisited
  • Unifying Framework
  • Chapter 4 Network Models
  • Network Graphs
  • Combination Matrices
  • Convergence of Matrix Powers
  • Strong and Primitive Graphs
  • Stochastic Combination Matrices
  • Weak Graphs
  • Convergent Matrices over Weak Graphs
  • Combination Policies
  • Left Stochastic Policies
  • Doubly Stochastic Policies
  • Chapter 5 Social Learning with Geometric Averaging
  • Belief Convergence
  • Learning over Connected Graphs
  • Objective Evidence
  • Subjective Evidence
  • Fake Evidence
  • Learning over Weak Graphs
  • Chapter 6 Error Probability Performance
  • Useful Statistical Descriptors
  • Log Likelihood Ratios
  • Log Belief Ratios
  • Error Probabilities
  • Normal Approximation for Large t
  • Large Deviations for Large t
  • Benefits of Cooperation
  • Chapter 7 Social Learning with Arithmetic Averaging
  • Modeling Assumptions
  • Belief Convergence
  • Chapter 8 Adaptive Social Learning
  • Stubbornness of Agents
  • Adaptive Update
  • Adaptive Update: First Approach
  • Adaptive Update: Second Approach.
  • More General Update Rules
  • Bayesian or Non-Bayesian?
  • Censored Beliefs
  • Learning the Social Graph
  • Appendices
  • Appendix A Convex Functions
  • Appendix B Entropy and KL Divergence
  • Appendix C Probabilistic Inequalities
  • Appendix D Stochastic Convergence
  • Types of Stochastic Convergence
  • Fundamental Asymptotic Results
  • Convergence of Sums and Recursions
  • Martingales
  • Appendix E Large Deviations
  • Empirical Averages
  • Fenchel-Legendre Transform
  • Generating Functions
  • Cramér's Theorem
  • Probability of Belonging to Arbitrary Sets
  • Large Deviation Principle
  • Appendix F Random Sums and Series
  • Convergent Random Series
  • Random Sums Relevant to Adaptive Social Learning
  • Vector Case for Network Behavior
  • Appendix G Rademacher Complexity
  • General Case
  • Multilayer Perceptrons
  • References
  • About the Authors.