Model Thinking

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Model Thinking

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Description

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About this course: 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 fore…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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!

Created by:  University of Michigan
  • Taught by:  Scott E. Page, Professor of Complex Systems, Political Science, and Economics

    Center for the Study of Complex Systems
Commitment 4-8 hours/week Language English, Subtitles: Portuguese (Brazilian), Turkish, Ukrainian, Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. User Ratings 4.7 stars Average User Rating 4.7See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of Michigan 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.

Syllabus


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 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 videos, 6 readings expand


  1. Reading: Welcome
  2. Reading: Grading Policy
  3. Reading: Course FAQ
  4. Reading: Syllabus
  5. Reading: Help us learn more about you!
  6. Video: Thanks and Welcome
  7. Video: Why Model?
  8. Video: Intelligent Citizens of the World
  9. Video: Thinking Clearly
  10. Video: Using and Understanding Data
  11. Video: Using Models to Decide, Strategize, and Design
  12. Reading: Segregation and Peer Effects
  13. Video: Sorting and Peer Effects Introduction
  14. Video: Schelling's Segregation Model
  15. Video: Measuring Segregation
  16. Video: Peer Effects
  17. Video: The Standing Ovation Model
  18. Video: The Identification Problem

Graded: Why Model? & Segregation and Peer Effects

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 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, 1 reading expand


  1. Video: Aggregation
  2. Video: Central Limit Theorem
  3. Video: Six Sigma
  4. Video: Game of Life
  5. Video: Cellular Automata
  6. Video: Preference Aggregation
  7. Reading: Decision Models
  8. Video: Introduction to Decision Making
  9. Video: Multi-Criterion Decision Making
  10. Video: Spatial Choice Models
  11. Video: Probability: The Basics
  12. Video: Decision Trees
  13. Video: Value of Information

Graded: Aggregation & Decision Models

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. 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, 1 reading expand


  1. Video: Thinking Electrons: Modeling People
  2. Video: Rational Actor Models
  3. Video: Behavioral Models
  4. Video: Rule Based Models
  5. Video: When Does Behavior Matter?
  6. Reading: Categorical and Linear Models
  7. Video: Introduction to Linear Models
  8. Video: Categorical Models
  9. Video: Linear Models
  10. Video: Fitting Lines to Data
  11. Video: Reading Regression Output
  12. Video: From Linear to Nonlinear
  13. Video: The Big Coefficient vs The New Reality

Graded: Modules Thinking Electrons: Modeling People & Categorical and Linear Models

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 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, 1 reading expand


  1. Video: Tipping Points
  2. Video: Percolation Models
  3. Video: Contagion Models 1: Diffusion
  4. Video: Contagion Models 2: SIS Model
  5. Video: Classifying Tipping Points
  6. Video: Measuring Tips
  7. Reading: Economic Growth
  8. Video: Introduction To Growth
  9. Video: Exponential Growth
  10. Video: Basic Growth Model
  11. Video: Solow Growth Model
  12. Video: Will China Continue to Grow?
  13. Video: Why Do Some Countries Not Grow?
  14. Video: Piketty's Capital: The Power of Simple Model

Graded: Modules Tipping Points & Economic Growth

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 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, 1 reading expand


  1. Video: Problem Solving and Innovation
  2. Video: Perspectives and Innovation
  3. Video: Heuristics
  4. Video: Teams and Problem Solving
  5. Video: Recombination
  6. Reading: Markov Processes
  7. Video: Markov Models
  8. Video: A Simple Markov Model
  9. Video: Markov Model of Democratization
  10. Video: Markov Convergence Theorem
  11. Video: Exapting the Markov Model

Graded: Diversity and Innovation & Markov Processes

WEEK 6


Midterm Exam





    Graded: Modules 1-10

    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 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, 1 reading expand


    1. Video: Lyapunov Functions
    2. Video: The Organization of Cities
    3. Video: Exchange Economies and Externalities
    4. Video: Time to Convergence and Optimality
    5. Video: Lyapunov: Fun and Deep
    6. Video: Lyapunov or Markov
    7. Reading: Coordination and Culture
    8. Video: Coordination and Culture
    9. Video: What Is Culture And Why Do We Care?
    10. Video: Pure Coordination Game
    11. Video: Emergence of Culture
    12. Video: Coordination and Consistency

    Graded: Lyapunov Functions & Coordination and Culture

    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 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, 1 reading expand


    1. Video: Path Dependence
    2. Video: Urn Models
    3. Video: Mathematics on Urn Models
    4. Video: Path Dependence and Chaos
    5. Video: Path Dependence and Increasing Returns
    6. Video: Path Dependent or Tipping Point
    7. Reading: Networks
    8. Video: Networks
    9. Video: The Structure of Networks
    10. Video: The Logic of Network Formation
    11. Video: Network Function

    Graded: Path Dependence & Networks

    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, 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, 1 reading expand


    1. Video: Randomness and Random Walk Models
    2. Video: Sources of Randomness
    3. Video: Skill and Luck
    4. Video: Random Walks
    5. Video: Random Walks and Wall Street
    6. Video: Finite Memory Random Walks
    7. Reading: Colonel Blotto
    8. Video: Colonel Blotto Game
    9. Video: Blotto: No Best Strategy
    10. Video: Applications of Colonel Blotto
    11. Video: Blotto: Troop Advantages
    12. Video: Blotto and Competition

    Graded: Randomness and Random Walks & Colonel Blotto

    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 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, 1 reading expand


    1. Video: Intro: The Prisoners' Dilemma and Collective Action
    2. Video: The Prisoners' Dilemma Game
    3. Video: Seven Ways To Cooperation
    4. Video: Collective Action and Common Pool Resource Problems
    5. Video: No Panacea
    6. Reading: Mechanism Design
    7. Video: Mechanism Design
    8. Video: Hidden Action and Hidden Information
    9. Video: Auctions
    10. Video: Public Projects

    Graded: Prisoners' Dilemma and Collective Action & Mechanism Design

    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 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, 1 reading expand


    1. Video: Replicator Dynamics
    2. Video: The Replicator Equation
    3. Video: Fisher's Theorem
    4. Video: Variation or Six Sigma
    5. Reading: Prediction and The Many Model Thinker
    6. Video: Prediction
    7. Video: Linear Models
    8. Video: Diversity Prediction Theorem
    9. Video: The Many Model Thinker

    Graded: Learning Models: Replicator Dynamics & Prediction and the Many Model Thinker

    WEEK 12


    Final Exam
    The description goes here


    1 reading expand


    1. Reading: Post-course Survey

    Graded: Modules 12-21
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