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We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians. The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life. Here's how the course will work. For each model, I present a short, easily digestible overview lecture. Then, I'll dig deeper. I'll go into the technical details of the model. Those technical lectures won't require calculus but be prepared for some algebra. For all the lectures, I'll offer some questions and we'll have quizzes and even a final exam. If you decide to do the deep dive, and take all the quizzes and the exam, you'll receive a Course Certificate. If you just decide to follow along for the introductory lectures to gain some exposure that's fine too. It's all free. And it's all here to help make you a better thinker!
课程大纲
- Week 1 - Why Model & Segregation/Peer Effects
In these lectures, I describe some of the reasons why a person would want to take a modeling course. These reasons fall into four broad categories: 1)To be an intelligent citizen of the world 2) To be a clearer thinker 3) To understand and use data 4) To bette... - Week 2 - Aggregation & Decision Models
In this section, we explore the mysteries of aggregation, i.e. adding things up. We start by considering how numbers aggregate, focusing on the Central Limit Theorem. We then turn to adding up rules. We consider the Game of Life and one dimensional cellular au... - Week 3 - Thinking Electrons: Modeling People & Categorical and Linear Models
In this section, we study various ways that social scientists model people. We study and contrast three different models. The rational actor approach,behavioral models , and rule based models . These lectures provide context for many of the models that follow.... - Week 4 - Tipping Points & Economic Growth
In this section, we cover tipping points. We focus on two models. A percolation model from physics that we apply to banks and a model of the spread of diseases. The disease model is more complicated so I break that into two parts. The first part focuses on the... - Week 5 - Diversity and Innovation & Markov Processes
In this section, we cover some models of problem solving to show the role that diversity plays in innovation. We see how diverse perspectives (problem representations) and heuristics enable groups of problem solvers to outperform individuals. We also introduce... - Week 6 - Midterm Exam
- Week 7 - Lyapunov Functions & Coordination and Culture
Models can help us to determine the nature of outcomes produced by a system: will the system produce an equilibrium, a cycle, randomness, or complexity? In this set of lectures, we cover Lyapunov Functions. These are a technique that will enable us to identify... - Week 8 - Path Dependence & Networks
In this set of lectures, we cover path dependence. We do so using some very simple urn models. The most famous of which is the Polya Process. These models are very simple but they enable us to unpack the logic of what makes a process path dependent. We also re... - Week 9 - Randomness and Random Walks & Colonel Blotto
In this section, we first discuss randomness and its various sources. We then discuss how performance can depend on skill and luck, where luck is modeled as randomness. We then learn a basic random walk model, which we apply to the Efficient Market Hypothesis,... - Week 10 - Prisoners' Dilemma and Collective Action & Mechanism Design
In this section, we cover the Prisoners' Dilemma, Collective Action Problems and Common Pool Resource Problems. We begin by discussion the Prisoners' Dilemma and showing how individual incentives can produce undesirable social outcomes. We then cover seven way... - Week 11 - Learning Models: Replicator Dynamics & Prediction and the Many Model Thinker
In this section, we cover replicator dynamics and Fisher's fundamental theorem. Replicator dynamics have been used to explain learning as well as evolution. Fisher's theorem demonstrates how the rate of adaptation increases with the amount of variation. We con... - Week 12 - Final Exam
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教师
Scott E. Page
Professor of Complex Systems, Political Science, and Economics
Center for the Study of Complex Systems
内容设计师
平台
Coursera是一家数字公司，提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。
Coursera与顶尖大学和组织合作，在线提供一些课程，并提供许多科目的课程，包括：物理，工程，人文，医学，生物学，社会科学，数学，商业，计算机科学，数字营销，数据科学 和其他科目。
This class is just absolutely fantastic. I learned a ton and ended up getting a few of Scott Page's books, which go into even more detail (but still not at a level where someone like me, with a minimal math background, couldn't get it). Thank you for making this course. I'm a PhD student in Philosophy and this class (and Page's books) helped me work on an epistemology paper I would have been unable to do otherwise. Thank you!
This class is just absolutely fantastic. I learned a ton and ended up getting a few of Scott Page's books, which go into even more detail (but still not at a level where someone like me, with a minimal math background, couldn't get it). Thank you for making this course. I'm a PhD student in Philosophy and this class (and Page's books) helped me work on an epistemology paper I would have been unable to do otherwise. Thank you!
A really useful and interesting lesson. Learning through numbers of simple but meaningful models, providing me an introduction to model thinking.
I took this course at the University of Michigan and used the Coursera version as a study aid. This has been one of the most interesting and thought provoking classes I have taken in my academic career. Thank you Professor Page.
This is an amazing course for high school students and undergraduate freshmen, especially for students without STEM backgrounds.Know that it's a very basic introductory course, like an appetizer, it's up to you to research more into all those models (rigorous definition, mathematical formulae, technical papers, real-world application, etc.).
Simply, a great surprise! Content, videos, material, answers in the Forum, Coursera help online. It's been a rewarding journey! I must Excel the materials before I am taking a new one, and I need to wait for a period where the workload is letting me put the effort needed to succeed, but I'll repeat next summer again! Highly recommended!
