Network Dynamics of Social Behavior
University of Pennsylvania
课程信息
How do revolutions emerge without anyone expecting them? How did social norms about same sex marriage change more rapidly than anyone anticipated? Why do some social innovations take off with relative ease, while others struggle for years without spreading? More generally, what are the forces that control the process of social evolution –from the fashions that we wear, to our beliefs about religious tolerance, to our ideas about the process of scientific discovery and the best ways to manage complex research organizations?
The social world is complex and full of surprises. Our experiences and intuitions about the social world as individuals are often quite different from the behaviors that we observe emerging in large societies. Even minor changes to the structure of a social network - changes that are unobservable to individuals within those networks - can lead to radical shifts in the spread of new ideas and behaviors through a population. These “invisible” mathematical properties of social networks have powerful implications for the ways that teams solve problems, the social norms that are likely to emerge, and even the very future of our society.
This course condenses the last decade of cutting-edge research on these topics into six modules. Each module provides an in-depth look at a particular research puzzle -with a focus on agent-based models and network theories of social change -and provides an interactive computational model for you try out and to use for making your own explorations!
Learning objectives - after this course, students will be able to...
- explain how computer models are used to study challenging social problems
- describe how networks are used to represent the structure of social relationships
- show how individual actions can lead to unintended collective behaviors
- provide concrete examples of how social networks can influence social change
- discuss how diffusion processes can explain the growth social movements, changes in cultural norms, and the success of team problem solving
Syllabus - What you will learn from this course
1
Section
Course Introduction and Schelling's Segregation Model
This week will introduce students to agent-based modeling and social network theory. We will present one of the earliest and most famous agent-based models, Thomas Schelling’s model of segregation, which shows how segregation can emerge in a population even when people individually prefer diversity. This week will demonstrate this model both conceptually and with NetLogo, and illustrate how agent-based models can be used to demonstrate sufficient conditions for the emergence of social phenomena.
7 videos (Total 20 min), 1 quiz
7 videos
1.1 The Substantive Problem: Micromotives and Macrobehavior1m
1.2 What are Agent-Based Models?2m
1.3 Formal Model of Segregation1m
1.4 Exploring Schelling's Segregation Model1m
1.5 How to Download and Use NetLogo2m
1.6 Using NetLogo: Schelling's Segregation Model4m
1 practice exercises
Week 116m
2
Section
Diffusion in Small Worlds
This week will introduce students to social network theory and the “small worlds” paradox. We will introduce contagion models of diffusion, and discuss how network structure can impact the speed with which information spreads through a population. This week includes both high level conceptual overviews of social network theory, explaining how networks are used to represent complex social relationships, as well as technical descriptions of two basic types of networks.
7 videos (Total 25 min), 1 quiz
7 videos
2.2 Introduction to Network Science5m
2.3 Types of Networks: Lattice Graph5m
2.4 Types of Networks: Random Graph4m
2.5 Using NetLogo: Properties of the Small World Network2m
2.6 Using NetLogo: Information Diffusion in Small World Networks3m
2.7 Conclusions: Life in a Small World2m
1 practice exercises
Week 218m
3
Section
Complex Contagions and the Weakness of Long Ties
This week will begin by discussing the limitations of simple disease-like models of social contagion, introducing the idea of “complex contagions” to model people’s frequent need for social reinforcement before spreading a piece of information or behavior. While simple contagions always spread faster as networks get smaller, this week will demonstrate the paradoxical nature of complex contagions, which can spread slower (or not at all!) in the smallest networks.
6 videos (Total 26 min), 1 quiz
6 videos
3.2 From Simple to Complex Contagions4m
3.3 How to Model Complex Contagions4m
3.4 Threshold Models in Networks9m
3.5 Using NetLogo: Complex Contagions in Small World Networks2m
3.6 Conclusion: The Spread of Behavior in a Complex World3m
1 practice exercises
Week 324m
4
Section
Emperor's Dilemma and the Spread of Unpopular Norms
How can behaviors become popular even when most people dislike them? This week will introduce a model based on the classic allegory by Hans Christian Anderson, “The Emperor’s New Clothes.” We will first provide a conceptual overview of the model, discussing the role of private versus public beliefs and the enforcement of social norms. We will then present this model in NetLogo, showing which conditions favor the spread of unpopular behaviors.
6 videos (Total 17 min), 1 quiz
6 videos
4.2 Components of a Norm: Compliance and Enforcement1m
4.3 Modeling Compliance and Enforcement2m
4.4 Using NetLogo: Explaining the Spread of Unpopular Norms7m
4.5 Falsification and Sufficiency in the Emperor's Dilemma1m
4.6 Self-Reinforcing Norms: A Cautionary Conclusion1m
1 practice exercises
Week 416m
5
Section
The Spontaneous Emergence of Conventions
This week will tackle another puzzle in social conventions: how can populations reach widely shared social conventions in the absence of any central organizing mechanism? We will begin by discussing classic explanations for the emergence of conventions, and why these explanations are insufficient to explain our social world. We will then discuss an agent-based model of conventions that builds on a model of local peer-to-peer coordination, and use NetLogo to show how local interactions can generate global convergence.
5 videos (Total 16 min), 1 reading, 1 quiz
5 videos
5.2 Conventions and the Challenge of Coordination2m
5.3 Modelling Pairwise Coordination in Networks3m
5.4 Using NetLogo: Coordination in Networks2m
5.5 How do norms emerge? Global Agreement from Peer-to-Peer Interation2m
1 readings
Optional Reading: Creating Critical Mass10m
1 practice exercises
Week 520m
6
Section
Problem Solving in Networks
How can you best organize a team to produce innovative solutions to complex problems? If people on the team can’t communicate, then they can’t share strategies, and won’t learn from each other’s success. But if they communicate too much, they’ll cluster around just a few ideas, and won’t explore the entire problem space. This week introduces an agent-based model of problem solving and shows how network structure can be used to navigate this classic exploration/exploitation trade-off.
7 videos (Total 31 min), 1 quiz
7 videos
6.2 Modeling Complex Problems7m
6.3 Modeling Problem-Solving in Teams2m
6.4 Thinking about Models: Problem-Solving in the Real World1m
6.5 Using NetLogo: Complex Problem Solving in Networks9m
6.6 Conclusions: The Two Pizza Rule1m
6.7 Course Conclusions: The Network Dynamics of Social Behavior4m
1 practice exercises
Week 618m
4.5
Top Reviews
By SC•Jan 21st 2018
A Crisp yet effective overview of some of the most critical works in the field of Networking. Anyone from the fields of Management, Sociology, Anthropology et al should try the MOOC.
By AE•Nov 21st 2017
Although the course was very short and the homework were so easy, I'm quite satisfied by the insight I got from Prof Centola
Instructor
Damon Centola
Associate ProfessorAbout University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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