课程信息

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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

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初级

完成时间大约为5 小时

英语（English）

对员工进行热门技能培训能否为您的公司带来益处？

体验 Coursera 企业版

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

初级

完成时间大约为5 小时

英语（English）

对员工进行热门技能培训能否为您的公司带来益处？

体验 Coursera 企业版

授课教师

授课教师评分

4.64/5 （69 个评分）

Damon Centola

Professor of Communication, Sociology and Communication

11,437 个学生

1 门课程

授课大纲 - 您将从这门课程中学到什么

周

1

周 1

完成时间为 1 小时

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.

完成时间为 1 小时

7 个视频（总计 20 分钟）

周

2

周 2

完成时间为 1 小时

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.

完成时间为 1 小时

7 个视频（总计 25 分钟）

周

3

周 3

完成时间为 1 小时

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.

完成时间为 1 小时

6 个视频（总计 26 分钟）

周

4

周 4

完成时间为 1 小时

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.

完成时间为 1 小时

6 个视频（总计 17 分钟）

周

5

周 5

完成时间为 1 小时

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.

完成时间为 1 小时

5 个视频（总计 16 分钟）, 1 个阅读材料, 1 个测验

周

6

周 6

完成时间为 1 小时

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.

完成时间为 1 小时

7 个视频（总计 31 分钟）

7 个视频

6.1 Exploration and Exploitation: Networks of Innovation2分钟

6.2 Modeling Complex Problems7分钟

6.3 Modeling Problem-Solving in Teams2分钟

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

6.5 Using NetLogo: Complex Problem Solving in Networks9分钟

6.6 Conclusions: The Two Pizza Rule1分钟

6.7 Course Conclusions: The Network Dynamics of Social Behavior4分钟

1 个练习

Week 630分钟

审阅

4.6

90 条评论

5 stars
72.28%
4 stars
21.14%
3 stars
4.28%
2 stars
0.85%
1 star
1.42%

来自NETWORK DYNAMICS OF SOCIAL BEHAVIOR的热门评论

由 SC 提供Jan 20, 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.

由 MY 提供Apr 6, 2020

For me, from a non-mathematical background, it was an excellent introductory course. I wish it can paw the way for more in-depth exploration around the issue and advance steps.

由 BV 提供Jun 11, 2020

Great content and short/objective videos. One only comment is that, sometimes, the implementation of the models is a bit unclear.

由 AL 提供Mar 20, 2020

I have never learned agent model in social network courses in graduate school. This course brings me up to date about the aggregate effects of unintended individual behaviors

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Network Dynamics of Social Behavior

Network Dynamics of Social Behavior

课程信息