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Network Dynamics of Social Behavior

Un vistazoProgramaPreguntas FrecuentesCreadoresCalificaciones y revisiones

InicioCiencias socialesGobernanza y Sociedad

Network Dynamics of Social Behavior

Universidad de Pensilvania

Acerca de este curso: 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

Para quién es esta clase: This course is aimed at people who are interested in understanding how computational models can be used to answer complex social questions. While experience with mathematical reasoning is helpful, there are no pre-requisites for this course. The material in each module is appropriate for students of any skill level. For college and graduate students who wish to engage with the material in greater depth, each module comes with a NetLogo model that you can download, explore, and build on.


Creada por:  Universidad de Pensilvania
Universidad de Pensilvania

  • Damon Centola

    Enseñado por:  Damon Centola, Associate Professor

    Annenberg School for Communication
NivelBeginner
Compromiso2-3 hours/ week
Idioma
English
Cómo aprobarAprueba todas las tareas calificadas para completar el curso.
Calificaciones del usuario
4.5 estrellas
Calificación promedio del usuario 4.5Ve los que los estudiantes dijeron
Programa
SEMANA 1
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
  1. Vídeo: 1.0 Course Introduction and Objectives
  2. Vídeo: 1.1 The Substantive Problem: Micromotives and Macrobehavior
  3. Vídeo: 1.2 What are Agent-Based Models?
  4. Vídeo: 1.3 Formal Model of Segregation
  5. Vídeo: 1.4 Exploring Schelling's Segregation Model
  6. Vídeo: 1.5 How to Download and Use NetLogo
  7. Vídeo: 1.6 Using NetLogo: Schelling's Segregation Model
Calificado: Week 1
SEMANA 2
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
  1. Vídeo: 2.1 Network Science: Mapping a Connected World
  2. Vídeo: 2.2 Introduction to Network Science
  3. Vídeo: 2.3 Types of Networks: Lattice Graph
  4. Vídeo: 2.4 Types of Networks: Random Graph
  5. Vídeo: 2.5 Using NetLogo: Properties of the Small World Network
  6. Vídeo: 2.6 Using NetLogo: Information Diffusion in Small World Networks
  7. Vídeo: 2.7 Conclusions: Life in a Small World
Calificado: Week 2
SEMANA 3
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
  1. Vídeo: 3.1 Social Contagions: Beyond Information Diffusion
  2. Vídeo: 3.2 From Simple to Complex Contagions
  3. Vídeo: 3.3 How to Model Complex Contagions
  4. Vídeo: 3.4 Threshold Models in Networks
  5. Vídeo: 3.5 Using NetLogo: Complex Contagions in Small World Networks
  6. Vídeo: 3.6 Conclusion: The Spread of Behavior in a Complex World
Calificado: Week 3
SEMANA 4
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
  1. Vídeo: 4.1 The Emperor's Dilemma: Explaining Unpopular Norms
  2. Vídeo: 4.2 Components of a Norm: Compliance and Enforcement
  3. Vídeo: 4.3 Modeling Compliance and Enforcement
  4. Vídeo: 4.4 Using NetLogo: Explaining the Spread of Unpopular Norms
  5. Vídeo: 4.5 Falsification and Sufficiency in the Emperor's Dilemma
  6. Vídeo: 4.6 Self-Reinforcing Norms: A Cautionary Conclusion
Calificado: Week 4
SEMANA 5
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
  1. Vídeo: 5.1 A Startup Problem: The Emergence of Norms
  2. Vídeo: 5.2 Conventions and the Challenge of Coordination
  3. Vídeo: 5.3 Modelling Pairwise Coordination in Networks
  4. Vídeo: 5.4 Using NetLogo: Coordination in Networks
  5. Vídeo: 5.5 How do norms emerge? Global Agreement from Peer-to-Peer Interation
Calificado: Week 5
SEMANA 6
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
  1. Vídeo: 6.1 Exploration and Exploitation: Networks of Innovation
  2. Vídeo: 6.2 Modeling Complex Problems
  3. Vídeo: 6.3 Modeling Problem-Solving in Teams
  4. Vídeo: 6.4 Thinking about Models: Problem-Solving in the Real World
  5. Vídeo: 6.5 Using NetLogo: Complex Problem Solving in Networks
  6. Vídeo: 6.6 Conclusions: The Two Pizza Rule
  7. Vídeo: 6.7 Course Conclusions: The Network Dynamics of Social Behavior
Calificado: Week 6

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Creadores
Universidad de Pensilvania
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.
Calificaciones y revisiones
Calificado 4.5 de 5 43 calificaciones
Christo Zonnev

very good overview. i'm missing the maths behind the theory which will enable me to apply the knowledge to own problems

Thomas Karl Alfred Woiczyk

The course gives a very good overview of the main insights and is enjoyable at the same time. Great course that would deserve a five-star rating if it would not be for the many typos, incomplete sentences in the tests, and errors in the videos.

김

i started this course after i read a book 'Linked' and became interested in network science. Although English is foreign language to me and Netlogo is new to me, i didn't have any difficulties understanding the course because it contains lots of examples and easy explanations. and i'm satisfied as i learned how social network works in our society. Thanks!

SY

Content is neatly laid out, not mathematically rigorous as I believe course is targeting a wider set of audience. Week 2 material can be improved with regard to clarity about the different types of networks.



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