Midas Oracle .ORG » Jon Kleinberg http://www.midasoracle.org Prediction Markets, etc. Mon, 13 Feb 2012 22:24:30 +0000 en hourly 1 http://wordpress.org/?v=3.3.1 Networks, Crowds, and Markets – Reasoning About A Highly Connected World – by David Easley and Jon Kleinberg http://www.midasoracle.org/2009/10/22/networks-crowds-and-markets/ http://www.midasoracle.org/2009/10/22/networks-crowds-and-markets/#comments Thu, 22 Oct 2009 07:20:07 +0000 Chris F. Masse http://www.midasoracle.org/?p=18390 Continue reading →]]> Networks, Crowds, and Markets – Reasoning About A Highly Connected World – by David Easley and Jon Kleinberg

http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf
-
Contents

  • Chapter 1. Overview
    • 1.1 Aspects of Networks
    • 1.2 Central Themes and Topics

Part I Graph Theory and Social Networks

  • Chapter 2. Graphs
    • 2.1 Basic Definitions
    • 2.2 Paths and Connectivity
    • 2.3 Distance and Breadth-First Search
    • 2.4 Network Datasets: An Overview
  • Chapter 3. Strong and Weak Ties
    • 3.1 Triadic Closure
    • 3.2 The Strength of Weak Ties
    • 3.3 Tie Strength and Network Structure in Large-Scale Data
    • 3.4 Tie Strength, Social Media, and Passive Engagement
    • 3.5 Closure, Structural Holes, and Social Capital
    • 3.6 Advanced Material: Betweenness Measures and Graph Partitioning
  • Chapter 4. Networks in Their Surrounding Contexts
    • 4.1 Homophily
    • 4.2 Mechanisms Underlying Homophily: Selection and Social Influence
    • 4.3 Affiliation
    • 4.4 Tracking Link Formation in On-Line Data
    • 4.5 A Spatial Model of Segregation
  • Chapter 5. Positive and Negative Relationships
    • 5.1 Structural Balance
    • 5.2 Balanced Networks and the Cartwright-Harary Theorem
    • 5.3 Applications of Structural Balance
    • 5.4 A Weaker Form of Structural Balance
    • 5.5 Advanced Material: Generalizing the Definition of Structural Balance

Part II Game Theory

  • Chapter 6. Games
    • 6.1 What is a Game?
    • 6.2 Reasoning about Behavior in a Game
    • 6.3 Best Responses and Dominant Strategies
    • 6.4 Nash Equilibrium
    • 6.5 Multiple Equilibria: Coordination Games
    • 6.6 Multiple Equilibria: The Hawk-Dove Game
    • 6.7 Mixed Strategies
    • 6.8 Mixed Strategies: Examples and Empirical Analysis
    • 6.9 Pareto-Optimality and Social Optimality
    • 6.10 Advanced Material: Dominated Strategies and Dynamic Games
  • Chapter 7. Evolutionary Game Theory
    • 7.1 Fitness as a Result of Interaction
    • 7.2 Evolutionarily Stable Strategies
    • 7.3 A General Description of Evolutionarily Stable Strategies
    • 7.4 Relationship Between Evolutionary and Nash Equilibria
    • 7.5 Evolutionarily Stable Mixed Strategies
  • Chapter 8. Modeling Network Traffic using Game Theory
    • 8.1 Traffic at Equilibrium
    • 8.2 Braess’s Paradox
    • 8.3 Advanced Material: The Social Cost of Traffic at Equilibrium
  • Chapter 9. Auctions
    • 9.1 Types of Auctions
    • 9.2 When are Auctions Appropriate?
    • 9.3 Relationships between Different Auction Formats
    • 9.4 Second-Price Auctions
    • 9.5 First-Price Auctions and Other Formats
    • 9.6 Common Values and The Winner’s Curse
    • 9.7 Advanced Material: Bidding Strategies in First-Price and All-Pay Auctions

Part III Markets and Strategic Interaction in Networks

  • Chapter 10. Matching Markets
    • 10.1 Bipartite Graphs and Perfect Matchings
    • 10.2 Valuations and Optimal Assignments
    • 10.3 Prices and the Market-Clearing Property
    • 10.4 Constructing a Set of Market-Clearing Prices
    • 10.5 How Does this Relate to Single-Item Auctions?
    • 10.6 Advanced Material: A Proof of the Matching Theorem
  • Chapter 11. Network Models of Markets with Intermediaries
    • 11.1 Price-Setting in Markets
    • 11.2 A Model of Trade on Networks
    • 11.3 Equilibria in Trading Networks
    • 11.4 Further Equilibrium Phenomena: Auctions and Ripple Effects
    • 11.5 Social Welfare in Trading Networks
    • 11.6 Trader Profits
    • 11.7 Reflections on Trade with Intermediaries
  • Chapter 12. Bargaining and Power in Networks
    • 12.1 Power in Social Networks
    • 12.2 Experimental Studies of Power and Exchange
    • 12.3 Results of Network Exchange Experiments
    • 12.4 A Connection to Buyer-Seller Networks
    • 12.5 Modeling Two-Person Interaction: The Nash Bargaining Solution
    • 12.6 Modeling Two-Person Interaction: The Ultimatum Game
    • 12.7 Modeling Network Exchange: Stable Outcomes
    • 12.8 Modeling Network Exchange: Balanced Outcomes
    • 12.9 Advanced Material: A Game-Theoretic Approach to Bargaining

Part IV Information Networks and the World Wide Web

  • Chapter 13. The Structure of the Web
    • 13.1 The World Wide Web
    • 13.2 Information Networks, Hypertext, and Associative Memory
    • 13.3 The Web as a Directed Graph
    • 13.4 The Bow-Tie Structure of the Web
    • 13.5 The Emergence of Web 2.0
  • Chapter 14. Link Analysis and Web Search
    • 14.1 Searching the Web: The Problem of Ranking
    • 14.2 Link Analysis using Hubs and Authorities
    • 14.3 PageRank
    • 14.4 Applying Link Analysis in Modern Web Search
    • 14.5 Applications beyond the Web
    • 14.6 Advanced Material: Spectral Analysis, Random Walks, and Web Search
  • Chapter 15. Sponsored Search Markets
    • 15.1 Advertising Tied to Search Behavior
    • 15.2 Advertising as a Matching Market
    • 15.3 Encouraging Truthful Bidding in Matching Markets: The VCG Principle
    • 15.4 Analyzing the VCG Procedure: Truth-Telling as a Dominant Strategy
    • 15.5 The Generalized Second Price Auction
    • 15.6 Equilibria of the Generalized Second Price Auction
    • 15.7 Ad Quality
    • 15.8 Complex Queries and Interactions Among Keywords
    • 15.9 Advanced Material: VCG Prices and the Market-Clearing Property

Part V Network Dynamics: Population Models

  • Chapter 16. Information Cascades
    • 16.1 Following the Crowd
    • 16.2 A Simple Herding Experiment
    • 16.3 Bayes’ Rule: A Model of Decision-Making Under Uncertainty
    • 16.4 Bayes’ Rule in the Herding Experiment
    • 16.5 A Simple, General Cascade Model
    • 16.6 Sequential Decision-Making and Cascades
    • 16.7 Lessons from Cascades
  • Chapter 17. Network Effects
    • 17.1 The Economy Without Network Effects
    • 17.2 The Economy with Network Effects
    • 17.3 Stability, Instability, and Tipping Points
    • 17.4 A Dynamic View of the Market
    • 17.5 Industries with Network Goods
    • 17.6 Mixing Individual Effects with Population-Level Effects
    • 17.7 Advanced Material: Negative Externalities and The El Farol Bar Problem
  • Chapter 18. Power Laws and Rich-Get-Richer Phenomena
    • 18.1 Popularity as a Network Phenomenon
    • 18.2 Power Laws
    • 18.3 Rich-Get-Richer Models
    • 18.4 The Unpredictability of Rich-Get-Richer Effects
    • 18.5 The Long Tail
    • 18.6 The Effect of Search Tools and Recommendation Systems
    • 18.7 Advanced Material: Analysis of Rich-Get-Richer Processes

Part VI Network Dynamics: Structural Models

  • Chapter 19. Cascading Behavior in Networks
    • 19.1 Diffusion in Networks
    • 19.2 Modeling Diffusion through a Network
    • 19.3 Cascades and Clusters
    • 19.4 Diffusion, Thresholds, and the Role of Weak Ties
    • 19.5 Extensions of the Basic Cascade Model
    • 19.6 Knowledge, Thresholds, and Collective Action
    • 19.7 Advanced Material: The Cascade Capacity
  • Chapter 20. The Small-World Phenomenon
    • 20.1 Six Degrees of Separation
    • 20.2 Structure and Randomness
    • 20.3 Decentralized Search
    • 20.4 Empirical Analysis and Generalized Models
    • 20.5 Core-Periphery Structures and Difficulties in Decentralized Search
    • 20.6 Advanced Material: Analysis of Decentralized Search
  • Chapter 21. Epidemics
    • 21.1 Diseases and the Networks that Transmit Them
    • 21.2 Branching Processes
    • 21.3 The SIR Epidemic Model
    • 21.4 The SIS Epidemic Model
    • 21.5 Synchronization
    • 21.6 Transient Contacts and the Dangers of Concurrency
    • 21.7 Genealogy, Genetic Inheritance, and Mitochondrial Eve
    • 21.8 Advanced Material: Analysis of Branching and Coalescent Processes

Part VII Institutions and Aggregate Behavior

  • Chapter 22. Markets and Information
    • 22.1 Markets with Exogenous Events
    • 22.2 Horse Races, Betting, and Beliefs
    • 22.3 Aggregate Beliefs and the “Wisdom of Crowds”
    • 22.4 Prediction Markets and Stock Markets
    • 22.5 Markets with Endogenous Events
    • 22.6 The Market for Lemons
    • 22.7 Asymmetric Information in Other Markets
    • 22.8 Signaling Quality
    • 22.9 Quality Uncertainty On-Line: Reputation Systems and Other Mechanisms
    • 22.10 Advanced Material: Wealth Dynamics in Markets
  • Chapter 23. Voting
    • 23.1 Voting for Group Decision-Making
    • 23.2 Individual Preferences
    • 23.3 Voting Systems: Majority Rule
    • 23.4 Voting Systems: Positional Voting
    • 23.5 Arrow’s Impossibility Theorem
    • 23.6 Single-Peaked Preferences and the Median Voter Theorem
    • 23.7 Voting as a Form of Information Aggregation
    • 23.8 Insincere Voting for Information Aggregation
    • 23.9 Jury Decisions and the Unanimity Rule
    • 23.10 Sequential Voting and the Relation to Information Cascades
    • 23.11 Advanced Material: A Proof of Arrow’s Impossibility Theorem
  • Chapter 24. Property Rights
    • 24.1 Externalities and the Coase Theorem
    • 24.2 The Tragedy of the Commons
    • 24.3 Intellectual Property

Bibliography

]]> http://www.midasoracle.org/2009/10/22/networks-crowds-and-markets/feed/ 0