Acerca de este curso: Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences. You can find a more detailed syllabus here: http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf You can find a short introductory videao here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4
Para quién es esta clase: The course is aimed at people interested in researching social and economic networks, but should be accessible to advanced undergraduates and other people who have some prerequisites in mathematics and statistics. For example, it will be assumed that students are comfortable with basic concepts from linear algebra (e.g., matrix multiplication), probability theory (e.g., probability distributions, expected values, Bayes' rule), and statistics (e.g., hypothesis testing). Beyond those concepts, the course is self-contained.
Enseñado por: Matthew O. Jackson, Professor
Economics
| Nivel | Avanzado |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
- Reading: Syllabus
- Vídeo: 1.1: Introduction
- Vídeo: 1.2: Examples and Challenges
- Vídeo: 1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)
- Vídeo: 1.3: Definitions and Notation
- Vídeo: 1.4: Diameter
- Vídeo: 1.5: Diameter and Trees
- Vídeo: 1.6: Diameters of Random Graphs (Optional/Advanced 11:12)
- Vídeo: 1.7: Diameters in the World
- Vídeo: 1.8: Degree Distributions
- Vídeo: 1.9: Clustering
- Vídeo: 1.10: Week 1 Wrap
- Practice Quiz: Quiz Week 1
- Practice Quiz: Optional: Empirical Analysis of Network Data using Gephi or Pajek
- Reading: Slides from Lecture 1, with References
- Reading: OPTIONAL - Advanced Problem Set 1
- Vídeo: 2.2: Dynamics and Tie Strength
- Vídeo: 2.3: Centrality Measures
- Vídeo: 2.4: Centrality – Eigenvector Measures
- Vídeo: 2.5a: Application - Centrality Measures
- Vídeo: 2.5b: Application – Diffusion Centrality
- Vídeo: 2.6: Random Networks
- Vídeo: 2.7: Random Networks - Thresholds and Phase Transitions
- Vídeo: 2.8: A Threshold Theorem (optional/advanced 13:00)
- Vídeo: 2.9: A Small World Model
- Vídeo: 2.10 Week 2 Wrap
- Practice Quiz: Quiz Week 2
- Practice Quiz: Optional: Empirical Analysis of Network Data
- Reading: Slides from Lecture 2, with references
- Reading: OPTIONAL - Advanced Problem Set 2
- Reading: OPTIONAL - Solutions to Advanced PS 1
- Vídeo: 3.2: Mean Field Approximations
- Vídeo: 3.3: Preferential Attachment
- Vídeo: 3.4: Hybrid Models
- Vídeo: 3.5: Fitting Hybrid Models
- Vídeo: 3.6: Block Models
- Vídeo: 3.7: ERGMs
- Vídeo: 3.8: Estimating ERGMs
- Vídeo: 3.9: SERGMs
- Vídeo: 3.10: SUGMs
- Vídeo: 3.11: Estimating SUGMs (Optional/Advanced 21:03)
- Vídeo: 3.12: Week 3 Wrap
- Practice Quiz: Quiz Week 3
- Practice Quiz: Optional: Empirical Analysis of Network Data
- Practice Quiz: Optional: Using Statnet in R to Estimate an ERGM
- Reading: Slides from Lecture 3, with references
- Reading: OPTIONAL - Advanced Problem Set 3
- Reading: OPTIONAL - Solutions to Advanced PS 2
- Vídeo: 4.2: Pairwise Stability and Efficiency
- Vídeo: 4.3: Connections Model
- Vídeo: 4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)
- Vídeo: 4.5: Pairwise Stability in the Connections Model
- Vídeo: 4.6: Externalities and the Coauthor Model
- Vídeo: 4.7: Network Formation and Transfers
- Vídeo: 4.8: Heterogeneity in Strategic Models
- Vídeo: 4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)
- Vídeo: 4.10: Pairwise Nash Stability (Optional/Advanced 11:34)
- Vídeo: 4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)
- Vídeo: 4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)
- Vídeo: 4.13: Directed Network Formation (Optional/Advanced 16:38)
- Vídeo: 4.14: Application Structural Model (Optional/Advanced 35:06)
- Vídeo: 4.15: Week 4 Wrap
- Practice Quiz: Quiz Week 4
- Reading: Slides from Lecture 4, with references
- Reading: OPTIONAL - Advanced Problem Set 4
- Reading: OPTIONAL - Solutions to Advanced PS 3
- Vídeo: 5.2: Bass Model
- Vídeo: 5.3: Diffusion on Random Networks
- Vídeo: 5.4: Giant Component Poisson Case
- Vídeo: 5.5: SIS Model
- Vídeo: 5.6: Solving the SIS Model
- Vídeo: 5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)
- Vídeo: 5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)
- Vídeo: 5.8b: Application: Financial Contagions (Optional/Advanced 12:47)
- Vídeo: 5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)
- Vídeo: 5.9: Diffusion Summary
- Vídeo: 5.10: Week 5 Wrap
- Practice Quiz: Quiz Week 5
- Practice Quiz: Optional: Empirical Analysis of Network Data
- Reading: OPTIONAL - Advanced Problem Set 5
- Reading: OPTIONAL - Solutions to Advanced PS 4
- Reading: Slides from Lecture 5, with references
- Vídeo: 6.2: DeGroot Model
- Vídeo: 6.3: Convergence in DeGroot Model
- Vídeo: 6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)
- Vídeo: 6.5: Influence
- Vídeo: 6.6: Examples of Influence
- Vídeo: 6.7: Information Aggregation
- Vídeo: 6.8: Learning Summary
- Vídeo: 6.9: Week 6 Wrap
- Practice Quiz: Quiz Week 6
- Reading: Slides from Lecture 6, with references
- Reading: OPTIONAL - Advanced Problem Set 6
- Reading: OPTIONAL - Solutions to Advanced PS 5
- Vídeo: 7.2: Complements and Substitutes
- Vídeo: 7.3: Properties of Equilibria
- Vídeo: 7.4: Multiple Equilibria
- Vídeo: 7.5: An Application
- Vídeo: 7.6: Beyond 0-1 Choices
- Vídeo: 7.7: A Linear Quadratic Model
- Vídeo: 7.8: RepeatedGames and Networks
- Vídeo: 7.9: Week 7 Wrap
- Vídeo: 7.9b: Course Wrap
- Practice Quiz: Quiz Week 7
- Reading: Slides from Lecture 7, with references
- Reading: OPTIONAL - Advanced Problem Set 7
- Reading: OPTIONAL - Solutions to Advanced PS 6
- Reading: OPTIONAL - Solutions to Advanced PS 7
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The topic is quite interesting and Professor explains the concepts and theories in a quite understandable way. It is easy to follow the contents and offers me with a basic idea of the modeling of network effect. A very help course and highly recommend!
The chemistry disciplinary knowledge cautions the utilization of the idea of diffusion because diffusion in chemistry is more of systematic random process then the idea of diffusion in this lecture. If you could enhance and clarify the Week 4 lecture of the Praeto Efficiency, Utility, and Pairwise in additional examples the brevity of the lecture could build the idea into a few slides to sharpen the idea earlier. Think about adding more examples of the Centrality examples, I thought the Centrality was interesting.
Greatly insightful and resourceful content for future research. As a recent university graduate interested in graduate school I found the course challenging meaning determination and consistency contributed to the successful completion of the course. Rewatching lectures and seeking external support helped me comprehend concepts through application.
pn
needs more practical exercises