Social and Economic Networks: Models and Analysis
Social networks pervade our social and economic lives. They play a central role in the transmission of information about job opportunities and are critical to the trade of many goods and services. They are important in determining which products we buy, which languages we speak, how we vote, as well as whether or not we decide to become criminals, how much education we obtain, and our likelihood of succeeding professionally. The countless ways in which network structures affect our well-being make it critical to understand how social network structures impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. This course provides an overview and synthesis of research on social and economic networks, drawing on studies by sociologists, economists, computer scientists, physicists, and mathematicians.
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.
Week 1: Introduction, Empirical Background and Definitions
- Examples of Social Networks and their Impact, Definitions, Measures and Properties: Degrees, Diameters, Small Worlds, Weak and Strong Ties, Degree Distributions
Week 2: Background, Definitions, and Measures Continued
- Homophily, Dynamics, Centrality Measures: Degree, Betweenness, Closeness, Eigenvector, and Katz-Bonacich. Erdos and Renyi Random Networks: Thresholds and Phase Transitions
Week 3: Random Networks
- Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation
Week 4: Strategic Network Formation
- Game Theoretic Modeling of Network Formation, The Connections Model, The Conflict between Incentives and Efficiency, Dynamics, Directed Networks, Hybrid Models of Choice and Chance
Week 5: Diffusion on Networks
- Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data
Week 6: Learning on Networks.
- Bayesian Learning on Networks, The DeGroot Model of Learning on a Network, Convergence of Beliefs, The Wisdom of Crowds, How Influence depends on Network Position.
- Network Games, Peer Influences: Strategic Complements and Substitutes, the Relation between Network Structure and Behavior, A Linear Quadratic Game, Repeated Interactions and Network Structures
The course has some basic 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), and some light calculus (e.g., differentiation and integration). Beyond those concepts, the course will be self-contained.
The course is self-contained, so that all the definitions and concepts you need to solve the problem sets and final are contained in the video lectures. Much of the material for the course is covered in a text: Matthew O. Jackson Social and Economic Networks, Princeton University Press (Here are Princeton University Pressand Amazon pages for the book). The text is optional and not required for the course. Additional background readings, including research articles and several surveys on some of the topics covered in the course can be found on Professor Jackson's web page.
Matthew O. Jackson, Eberle Professor of Economics
The course will run for eight weeks, seven weeks of content plus one for the final exam. Each week there will be video lectures available, as well as a standalone problem set and some occasional data exercises, and there will be a final exam at the end of the course for those who wish to earn a course certificate.
Statement of Accomplishment
Students who successfully complete the class (above 70 percent correct on the problem sets and final exam) will receive a Statement of Accomplishment signed by the instructor - and those earning above 90 percent credit on the problem sets and final will earn one with distinction.
Coursera Course Certificate Available for a Fee
You can earn a Course Certificate for this Coursera course. A Course Certificate is proof that you completed and passed the course.
In order to get a Course Certificate, you must:
- Complete ID Verification, which includes submitting a photo and verified ID
- Pass all required assignments in the course
- Pay the Course Certificate fee, or apply and be approved for Coursera Financial Aid
This course may not currently be available to learners in some states and territories.