University of Michigan

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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!

Comienza de inmediato y aprende a tu propio ritmo.

Sugerido: 4-8 hours/week

Subtítulos: English, Portuguese (Brazilian), Turkish, Ukrainian, Chinese (Simplified)

EconomicsDecision-MakingDecision AnalysisStrategic ThinkingManagement

Comienza de inmediato y aprende a tu propio ritmo.

Sugerido: 4-8 hours/week

Subtítulos: English, Portuguese (Brazilian), Turkish, Ukrainian, Chinese (Simplified)

Section

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.
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12 videos (Total 124 min), 6 readings, 1 quiz

Why Model?8m

Intelligent Citizens of the World11m

Thinking More Clearly10m

Using and Understanding Data10m

Using Models to Decide, Strategize, and Design15m

Sorting and Peer Effects Introduction5m

Schelling's Segregation Model11m

Measuring Segregation11m

Peer Effects6m

The Standing Ovation Model18m

The Identification Problem10m

Welcome10m

Grading Policy10m

Course FAQ10m

Syllabus10m

Help us learn more about you!10m

Segregation and Peer Effects10m

Why Model? & Segregation and Peer Effects12m

Section

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 videos (Total 138 min), 1 reading, 1 quiz

Aggregation10m

Central Limit Theorem18m

Six Sigma5m

Game of Life14m

Cellular Automata18m

Preference Aggregation12m

Introduction to Decision Making5m

Multi-Criterion Decision Making8m

Spatial Choice Models11m

Probability: The Basics10m

Decision Trees14m

Value of Information8m

Decision Models10m

Aggregation & Decision Models16m

Section

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 videos (Total 130 min), 1 reading, 1 quiz

Rational Actor Models16m

Behavioral Models12m

Rule Based Models12m

When Does Behavior Matter?12m

Introduction to Linear Models4m

Categorical Models15m

Linear Models8m

Fitting Lines to Data11m

Reading Regression Output11m

From Linear to Nonlinear6m

The Big Coefficient vs The New Reality11m

Categorical and Linear Models10m

Modules Thinking Electrons: Modeling People & Categorical and Linear Models20m

Section

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 videos (Total 132 min), 1 reading, 1 quiz

Percolation Models11m

Contagion Models 1: Diffusion7m

Contagion Models 2: SIS Model9m

Classifying Tipping Points8m

Measuring Tips13m

Introduction To Growth6m

Exponential Growth10m

Basic Growth Model13m

Solow Growth Model11m

Will China Continue to Grow?11m

Why Do Some Countries Not Grow?11m

Piketty's Capital: The Power of Simple Model8m

Economic Growth10m

Modules Tipping Points & Economic Growth18m

Section

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 videos (Total 99 min), 1 reading, 1 quiz

Perspectives and Innovation16m

Heuristics9m

Teams and Problem Solving11m

Recombination11m

Markov Models4m

A Simple Markov Model11m

Markov Model of Democratization8m

Markov Convergence Theorem10m

Exapting the Markov Model10m

Markov Processes10m

Diversity and Innovation & Markov Processes12m

Section

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1 quiz

Modules 1-1028m

Section

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 videos (Total 116 min), 1 reading, 1 quiz

The Organization of Cities12m

Exchange Economies and Externalities9m

Time to Convergence and Optimality8m

Lyapunov: Fun and Deep8m

Lyapunov or Markov7m

Coordination and Culture3m

What Is Culture And Why Do We Care?15m

Pure Coordination Game13m

Emergence of Culture11m

Coordination and Consistency 17m

Coordination and Culture10m

Lyapunov Functions & Coordination and Culture20m

Section

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 videos (Total 122 min), 1 reading, 1 quiz

Urn Models16m

Mathematics on Urn Models14m

Path Dependence and Chaos11m

Path Dependence and Increasing Returns12m

Path Dependent or Tipping Point9m

Networks7m

The Structure of Networks19m

The Logic of Network Formation10m

Network Function13m

Networks10m

Path Dependence & Networks20m

Section

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 videos (Total 79 min), 1 reading, 1 quiz

Sources of Randomness5m

Skill and Luck8m

Random Walks12m

Random Walks and Wall Street7m

Finite Memory Random Walks8m

Colonel Blotto Game1m

Blotto: No Best Strategy7m

Applications of Colonel Blotto7m

Blotto: Troop Advantages6m

Blotto and Competition10m

Colonel Blotto10m

Randomness and Random Walks & Colonel Blotto16m

Section

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 videos (Total 92 min), 1 reading, 1 quiz

The Prisoners' Dilemma Game13m

Seven Ways To Cooperation15m

Collective Action and Common Pool Resource Problems7m

No Panacea6m

Mechanism Design4m

Hidden Action and Hidden Information9m

Auctions19m

Public Projects12m

Mechanism Design10m

Prisoners' Dilemma and Collective Action & Mechanism Design18m

Section

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 videos (Total 62 min), 1 reading, 1 quiz

The Replicator Equation13m

Fisher's Theorem11m

Variation or Six Sigma5m

Prediction2m

Linear Models5m

Diversity Prediction Theorem11m

The Many Model Thinker7m

Prediction and The Many Model Thinker10m

Learning Models: Replicator Dynamics & Prediction and the Many Model Thinker12m

Section

The description goes here...

1 reading, 1 quiz

Post-course Survey10m

Modules 12-2126m

4.8

started a new career after completing these courses

got a tangible career benefit from this course

By YK•Apr 7th 2018

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.

By GK•Feb 25th 2017

Great content and lectures, that possibly provides new dimensions to look/explain the situation in context, I guess I will comeback for references to continue with this journey in to 'Model Thinking'

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....

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