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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/networkdynamics
课程评论：没有评论
课程名称：社会行为的网络动态 课程概述：本课程探讨了社会变革及其背后动态的奥秘，例如革命如何在意想不到的时刻爆发，社会对同性婚姻的规范为何迅速变化，以及为何某些社会创新能够迅速传播而其他则停滞不前。更一般地说，课程将分析掌控社会进化过程的力量，包括影响时尚、宗教宽容、科学发现及复杂研究组织管理等各种因素。 课程将复杂的社会世界及其突发性进行剖析，揭示个体经验与大社会行为之间的差异。网络结构的微小变化可能导致新观念和行为在群体中的剧烈扩展，这些在社交网络中的“不可见”数学属性对团队解决问题的方式、社会规范的形成，乃至社会未来具有深远影响。 本课程将过去十年的前沿研究浓缩为六个模块，每个模块深入探讨一个特定的研究难题，重点关注基于智能体的模型和社会变迁的网络理论，同时提供互动计算模型供学生进行探索。 学习目标：完成本课程后，学生将能够… - 解释计算机模型如何用于研究复杂的社会问题 - 描述网络如何代表社会关系的结构 - 展示个体行为如何导致意想不到的集体行为 - 提供社交网络如何影响社会变迁的具体例子 - 讨论扩散过程如何解释社会运动的增长、文化规范的变化以及团队问题解决的成功 课程大纲： 1. 课程介绍与谢林的隔离模型：介绍基于智能体建模和社会网络理论，分析谢林的隔离模型及其社会现象的条件。 2. 小世界中的传播：探讨网络结构对信息传播速度的影响，介绍社会网络理论和传播模型。 3. 复杂传播与长关系的弱点：讨论单纯传播模型的局限，引入“复杂传播”概念，分析社交增强的需求。 4. 皇帝的困境与不受欢迎规范的传播：探讨为何一些行为即使不受欢迎却仍能流行，分析私信与公共信念对社会规范的影响。 5. 社会约定的自发性出现：研究在缺乏中央组织机制的情况下，如何形成广泛共享的社会约定。 6. 网络中的问题解决：探讨如何最佳地组织团队以应对复杂问题，并解析通信与探索之间的权衡。 该课程适合任何对社会行为的网络动力学、社交网络和社会变迁感兴趣的学习者。
Name:Course Introduction and Schelling's Segregation Model
Description: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.
Name:Diffusion in Small Worlds
Description: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.
Name:Complex Contagions and the Weakness of Long Ties
Description: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.
Name:Emperor's Dilemma and the Spread of Unpopular Norms
Description: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.
Name:The Spontaneous Emergence of Conventions
Description: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.
Name:Problem Solving in Networks
Description: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.
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
