**Social and Economic Networks**

**Mohammad Irfan**

**What's it about?** This course will examine the social and economic aspects of today's connected world from a multitude of perspectives; namely, network science, sociology, economics, and computer science. The fundamental questions to be addressed are: What is a network? What does a realworld network look like? What are its effects on various social and behavioral phenomena, such as smoking, obesity, or even videos going viral? This course will then study the network structure of the Internet, how companies like Google search it, and how they make money doing so. Further economic implications of networks, including networked economies and markets, will also be addressed.

**What?** The focus of this course is networks. Graph theory as a mathematical tool for studying networks has been around for over two centuries. However, modern computational power has enabled the study of networks that not only change dynamically but also scales to a previously unimaginable size. Examples of such networks are today's social and economic networks. This course connects a series of interconnected questions within the central theme of social and economic networks.

**How?** A variety of interdisciplinary tools and techniques will be used to address these questions. For example, students will study network formation using the classical ErdosRenyi random graph model as well as the modern preferential-attachment model. Tools from mathematical sociology as well as computer science will be used to study diffusion in networks. Students will be introduced to computational game theory in order to address various interesting questions on the strategic aspects of networks.

Most importantly, elements of computational thinking will be prevalent throughout this course to answer many of the "how" questions. The reason is that in today's socio-economic context, nearly all real-world networks are very complex with many interdependent components. To answer any conceivable question within such complex settings, we need to think critically about devising a solution. We need to consider computational time and space. We need to combine a mathematical approach with an engineering approach. Computational thinking enables us to do the above in order to devise practical solutions to problems.

**Why?** Several critical questions will be addressed in this course. First, almost all real-world networks exhibit some common properties, such as a "giant component" and a certain type of "degree distribution." Why do the entities in the network connect in this fashion? Second, what are the effects of network connectivity on various social and behavioral phenomena, such as smoking, obesity, or even videos going viral? Finally, what roles do strategic interactions play in shaping the networks that we see today?