关于此课程： 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!
制作方： 密歇根大学
教学方： Scott E. Page, Professor of Complex Systems, Political Science, and Economics
Center for the Study of Complex Systems
| 承诺学习时间 | 4-8小时/周 |
| 语言 | English, 字幕： Portuguese (Brazilian), Turkish, Ukrainian, Chinese (Simplified) |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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 better decide, strategize, and design. There are two readings for this section. These should be read either after the first video or at the completion of all of the videos.We now jump directly into some models. We contrast two types of models that explain a single phenomenon, namely that people tend to live and interact with people who look, think, and act like themselves. After an introductory lecture, we cover famous models by Schelling and Granovetter that cover these phenomena. We follows those with a fun model about standing ovations that I wrote with my friend John Miller.
12 视频, 6 阅读材料
- Leyendo: Welcome
- Leyendo: Grading Policy
- Leyendo: Course FAQ
- Leyendo: Syllabus
- Leyendo: Help us learn more about you!
- 视频: Thanks and Welcome
- 视频: Why Model?
- 视频: Intelligent Citizens of the World
- 视频: Thinking More Clearly
- 视频: Using and Understanding Data
- 视频: Using Models to Decide, Strategize, and Design
- Leyendo: Segregation and Peer Effects
- 视频: Sorting and Peer Effects Introduction
- 视频: Schelling's Segregation Model
- 视频: Measuring Segregation
- 视频: Peer Effects
- 视频: The Standing Ovation Model
- 视频: The Identification Problem
已评分: Why Model? & Segregation and Peer Effects
第 2 周
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 automata models. Both models show how simple rules can combine to produce interesting phenomena. Last, we consider aggregating preferences. Here we see how individual preferences can be rational, but the aggregates need not be.There exist many great places on the web to read more about the Central Limit Theorem, the Binomial Distribution, Six Sigma, The Game of LIfe, and so on. I've included some links to get you started. The readings for cellular automata and for diverse preferences are short excerpts from my books Complex Adaptive Social Systems and The Difference Respectively.
12 视频, 1 阅读材料
- 视频: Aggregation
- 视频: Central Limit Theorem
- 视频: Six Sigma
- 视频: Game of Life
- 视频: Cellular Automata
- 视频: Preference Aggregation
- Leyendo: Decision Models
- 视频: Introduction to Decision Making
- 视频: Multi-Criterion Decision Making
- 视频: Spatial Choice Models
- 视频: Probability: The Basics
- 视频: Decision Trees
- 视频: Value of Information
已评分: Aggregation & Decision Models
第 3 周
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. There's no specific reading for these lectures though I mention several books on behavioral economics that you may want to consider. Also, if you find the race to the bottom game interesting just type "Rosemary Nagel Race to the Bottom" into a search engine and you'll get several good links. You can also find good introductions to "Zero Intelligence Traders" by typing that in as well.
12 视频, 1 阅读材料
- 视频: Thinking Electrons: Modeling People
- 视频: Rational Actor Models
- 视频: Behavioral Models
- 视频: Rule Based Models
- 视频: When Does Behavior Matter?
- Leyendo: Categorical and Linear Models
- 视频: Introduction to Linear Models
- 视频: Categorical Models
- 视频: Linear Models
- 视频: Fitting Lines to Data
- 视频: Reading Regression Output
- 视频: From Linear to Nonlinear
- 视频: The Big Coefficient vs The New Reality
已评分: Modules Thinking Electrons: Modeling People & Categorical and Linear Models
第 4 周
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 diffusion. The second part adds recovery. The readings for this section consist of two excerpts from the book I'm writing on models. One covers diffusion. The other covers tips. There is also a technical paper on tipping points that I've included in a link. I wrote it with PJ Lamberson and it will be published in the Quarterly Journal of Political Science. I've included this to provide you a glimpse of what technical social science papers look like. You don't need to read it in full, but I strongly recommend the introduction. It also contains a wonderful reference list.
13 视频, 1 阅读材料
- 视频: Tipping Points
- 视频: Percolation Models
- 视频: Contagion Models 1: Diffusion
- 视频: Contagion Models 2: SIS Model
- 视频: Classifying Tipping Points
- 视频: Measuring Tips
- Leyendo: Economic Growth
- 视频: Introduction To Growth
- 视频: Exponential Growth
- 视频: Basic Growth Model
- 视频: Solow Growth Model
- 视频: Will China Continue to Grow?
- 视频: Why Do Some Countries Not Grow?
- 视频: Piketty's Capital: The Power of Simple Model
已评分: Modules Tipping Points & Economic Growth
第 5 周
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 some new concepts like "rugged landscapes" and "local optima". In the last lecture, we'll see the awesome power of recombination and how it contributes to growth. The readings for this chapters consist on an excerpt from my book The Difference courtesy of Princeton University Press.
10 视频, 1 阅读材料
- 视频: Problem Solving and Innovation
- 视频: Perspectives and Innovation
- 视频: Heuristics
- 视频: Teams and Problem Solving
- 视频: Recombination
- Leyendo: Markov Processes
- 视频: Markov Models
- 视频: A Simple Markov Model
- 视频: Markov Model of Democratization
- 视频: Markov Convergence Theorem
- 视频: Exapting the Markov Model
已评分: Diversity and Innovation & Markov Processes
第 6 周
Midterm Exam
已评分: Modules 1-10
第 7 周
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 many systems that go to equilibrium. In addition, they enable us to put bounds on how quickly the equilibrium will be attained. In this set of lectures, we learn the formal definition of Lyapunov Functions and see how to apply them in a variety of settings. We also see where they don't apply and even study a problem where no one knows whether or not the system goes to equilibrium or not.
11 视频, 1 阅读材料
- 视频: Lyapunov Functions
- 视频: The Organization of Cities
- 视频: Exchange Economies and Externalities
- 视频: Time to Convergence and Optimality
- 视频: Lyapunov: Fun and Deep
- 视频: Lyapunov or Markov
- Leyendo: Coordination and Culture
- 视频: Coordination and Culture
- 视频: What Is Culture And Why Do We Care?
- 视频: Pure Coordination Game
- 视频: Emergence of Culture
- 视频: Coordination and Consistency
已评分: Lyapunov Functions & Coordination and Culture
第 8 周
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 relate path dependence to increasing returns and to tipping points. The reading for this lecture is a paper that I wrote that is published in the Quarterly Journal of Political Science
10 视频, 1 阅读材料
- 视频: Path Dependence
- 视频: Urn Models
- 视频: Mathematics on Urn Models
- 视频: Path Dependence and Chaos
- 视频: Path Dependence and Increasing Returns
- 视频: Path Dependent or Tipping Point
- Leyendo: Networks
- 视频: Networks
- 视频: The Structure of Networks
- 视频: The Logic of Network Formation
- 视频: Network Function
已评分: Path Dependence & Networks
第 9 周
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, the ideas that market prices contain all relevant information so that what's left is randomness. We conclude by discussing finite memory random walk model that can be used to model competition. The reading for this section is a paper on distinguishing skill from luck by Michael Mauboussin.
11 视频, 1 阅读材料
- 视频: Randomness and Random Walk Models
- 视频: Sources of Randomness
- 视频: Skill and Luck
- 视频: Random Walks
- 视频: Random Walks and Wall Street
- 视频: Finite Memory Random Walks
- Leyendo: Colonel Blotto
- 视频: Colonel Blotto Game
- 视频: Blotto: No Best Strategy
- 视频: Applications of Colonel Blotto
- 视频: Blotto: Troop Advantages
- 视频: Blotto and Competition
已评分: Randomness and Random Walks & Colonel Blotto
第 10 周
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 ways to produce cooperation. Five of these will be covered in the paper by Nowak and Sigmund listed below. We conclude by talking about collective action and common pool resource problems and how they require deep careful thinking to solve. There's a wonderful piece to read on this by the Nobel Prize winner Elinor Ostrom.
9 视频, 1 阅读材料
- 视频: Intro: The Prisoners' Dilemma and Collective Action
- 视频: The Prisoners' Dilemma Game
- 视频: Seven Ways To Cooperation
- 视频: Collective Action and Common Pool Resource Problems
- 视频: No Panacea
- Leyendo: Mechanism Design
- 视频: Mechanism Design
- 视频: Hidden Action and Hidden Information
- 视频: Auctions
- 视频: Public Projects
已评分: Prisoners' Dilemma and Collective Action & Mechanism Design
第 11 周
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 conclude by describing how to make sense of both Fisher's theorem and our results on six sigma and variation reduction. The readings for this section are very short. The second reading on Fisher's theorem is rather technical. Both are excerpts from Diversity and Complexity.
8 视频, 1 阅读材料
- 视频: Replicator Dynamics
- 视频: The Replicator Equation
- 视频: Fisher's Theorem
- 视频: Variation or Six Sigma
- Leyendo: Prediction and The Many Model Thinker
- 视频: Prediction
- 视频: Linear Models
- 视频: Diversity Prediction Theorem
- 视频: The Many Model Thinker
已评分: Learning Models: Replicator Dynamics & Prediction and the Many Model Thinker
第 12 周
Final Exam
The description goes here
1 阅读材料
- Leyendo: Post-course Survey
已评分: Modules 12-21
常见问题解答
运作方式
Trabajo del curso
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Certificados
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制作方
密歇根大学
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
评分和审阅
This is a great introduction to the types and uses of models. The lectures are clear, and the examples given show good applicability to real world problems. Thanks for making it available!
The course presents a multitude of models that enable us to analyze human and systems behavior and interactions. By making implicit assumptions explicit we can understand real world processes better.
Great professor and resources, relevant concepts about different Thinking Models, Markov, Lyapunov, Blotto, Linear, Categorial, Nash Equilibrium, Paretto Efficiency, etc.
Useful to apply on workplace and daily life as well...
MZ
Very interesting. Especially the part about the game of life and cellular automata models!