À propos de ce cours

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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

Résultats de carrière des étudiants

20%

ont commencé une nouvelle carrière après avoir terminé ce cours

11%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours

17%

a obtenu une augmentation de salaire ou une promotion

Certificat partageable

Obtenez un Certificat lorsque vous terminez

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau débutant

Approx. 4 heures pour terminer

Anglais

Sous-titres : Anglais, Chinois (simplifié)

Résultats de carrière des étudiants

20%

ont commencé une nouvelle carrière après avoir terminé ce cours

11%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours

17%

a obtenu une augmentation de salaire ou une promotion

Certificat partageable

Obtenez un Certificat lorsque vous terminez

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau débutant

Approx. 4 heures pour terminer

Anglais

Sous-titres : Anglais, Chinois (simplifié)

Programme du cours : ce que vous apprendrez dans ce cours

Semaine

1

Semaine 1

1 heure pour terminer

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.

1 heure pour terminer

7 vidéos (Total 20 min)

7 vidéos

1.0 Course Introduction and Objectives6 min

1.1 The Substantive Problem: Micromotives and Macrobehavior1 min

1.2 What are Agent-Based Models?2 min

1.3 Formal Model of Segregation1 min

1.4 Exploring Schelling's Segregation Model1 min

1.5 How to Download and Use NetLogo2 min

1.6 Using NetLogo: Schelling's Segregation Model4 min

1 exercice pour s'entraîner

Week 116 min

Semaine

2

Semaine 2

1 heure pour terminer

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.

1 heure pour terminer

7 vidéos (Total 25 min)

7 vidéos

2.1 Network Science: Mapping a Connected World1 min

2.2 Introduction to Network Science5 min

2.3 Types of Networks: Lattice Graph5 min

2.4 Types of Networks: Random Graph4 min

2.5 Using NetLogo: Properties of the Small World Network2 min

2.6 Using NetLogo: Information Diffusion in Small World Networks3 min

2.7 Conclusions: Life in a Small World2 min

1 exercice pour s'entraîner

Week 218 min

Semaine

3

Semaine 3

1 heure pour terminer

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.

1 heure pour terminer

6 vidéos (Total 26 min)

6 vidéos

3.1 Social Contagions: Beyond Information Diffusion1 min

3.2 From Simple to Complex Contagions4 min

3.3 How to Model Complex Contagions4 min

3.4 Threshold Models in Networks9 min

3.5 Using NetLogo: Complex Contagions in Small World Networks2 min

3.6 Conclusion: The Spread of Behavior in a Complex World3 min

1 exercice pour s'entraîner

Week 324 min

Semaine

4

Semaine 4

1 heure pour terminer

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.

1 heure pour terminer

6 vidéos (Total 17 min)

6 vidéos

4.1 The Emperor's Dilemma: Explaining Unpopular Norms2 min

4.2 Components of a Norm: Compliance and Enforcement1 min

4.3 Modeling Compliance and Enforcement2 min

4.4 Using NetLogo: Explaining the Spread of Unpopular Norms7 min

4.5 Falsification and Sufficiency in the Emperor's Dilemma1 min

4.6 Self-Reinforcing Norms: A Cautionary Conclusion1 min

1 exercice pour s'entraîner

Week 416 min

Semaine

5

Semaine 5

1 heure pour terminer

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.

1 heure pour terminer

5 vidéos (Total 16 min), 1 lecture, 1 quiz

5 vidéos

5.1 A Startup Problem: The Emergence of Norms5 min

5.2 Conventions and the Challenge of Coordination2 min

5.3 Modelling Pairwise Coordination in Networks3 min

5.4 Using NetLogo: Coordination in Networks2 min

5.5 How do norms emerge? Global Agreement from Peer-to-Peer Interation2 min

1 lecture

Optional Reading: Creating Critical Mass10 min

1 exercice pour s'entraîner

Week 520 min

Semaine

6

Semaine 6

1 heure pour terminer

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.

1 heure pour terminer

7 vidéos (Total 31 min)

7 vidéos

6.1 Exploration and Exploitation: Networks of Innovation2 min

6.2 Modeling Complex Problems7 min

6.3 Modeling Problem-Solving in Teams2 min

6.4 Thinking about Models: Problem-Solving in the Real World1 min

6.5 Using NetLogo: Complex Problem Solving in Networks9 min

6.6 Conclusions: The Two Pizza Rule1 min

6.7 Course Conclusions: The Network Dynamics of Social Behavior4 min

1 exercice pour s'entraîner

Week 618 min

Foire Aux Questions

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Network Dynamics of Social Behavior