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# Model Thinking

### Overview

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### Syllabus

• 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.
• 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.
• 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.
• 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.
• 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.
• Midterm Exam
• 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.
• 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
• 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.
• 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.
• 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.
• Final Exam

Scott E. Page

## Reviews

4.7 rating, based on 65 Class Central reviews

4.8 rating at Coursera based on 2213 ratings

Start your review of Model Thinking

• This was a great interdisciplinary course, relevant for all thinking people. I have a background in economics and social science (two major applications of model thinking) and thus I was familiar with some of these concepts (e.g. prisoner's dilemmaâ€¦
• Anonymous
"Model Thinking" was the first, and still one of the very best, courses I've taken via www.coursera.org.

I'm an engineer with a strong background in quantitative analysis. I've taught Operations Research at the MBA level, and so was skeptical that I would learn anything new or significant from this class. I was wrong. Almost every week yielded a new revelation. It was eye-opening. Thanks to Professor Page I now have a whole new stack of analytical tools in my toolkit. I highly recommend this class, regardless of what you think you know or don't know about modeling social, business, or physical systems.
• Anonymous
Wonderful material, a rare opportunity to get a thorough overview of the topic.

It is not an easy topic, but Pr Page is a talented enough teacher to make it affordable in an enjoyable manner.

You can breeze through this course in a couple of hours, but the topic, in my opinion, requires a lot more work than advertise to really benefit from the learning.

A great course, a great opportunity that you do not want to miss to have a wide and thorough overview of most of the model families currently in use by economists, physicists, anthropologists, sociologists, and your beloved government in the control of the masses.

There is no other place to find all this information in gathered one source.
• Anonymous
I'm currently taking this class and really enjoying it. It wasn't what I expected at all -- I thought it would be more based on physical science and math rather than social sciences -- but that is my fault for not reading the explanatory materials more carefully. Nevertheless, I have to say I have had some real insights from the lectures. I enjoy how ideas from many different fields are stitched together.

Dr. Page presents with clarity and a high energy level. The materials a high qualtiy. Really enjoying this.
• Anonymous
This was my first MOOC and it hooked me in a big way. Professor Page keeps it fresh, interesting, and challanging throught the course. I found out that I was using models for most of my life without knowing it. He absolutly made me better at it (the more models the better, I got it Professor).

I never anticipated that the experience would be so rewarding.
• Anonymous
This was a great course and I learned a great deal out of it. I would recommend it to others interested in learning a wide range of models that they can apply to a ton of thought experiments or real-life situations.
• Anonymous
A broad introduction to a diverse collection of useful models for thinking about the world. You will need a bit of algebra, but the course is not math-intensive. Great lectures. Over and over, I found myself saying "That's fascinating!" One of my favorites
• Peter H
Very good course on model thinking. The instructor breaks down the content to an easily understandable level. Highly recommended for people working with data to at least know some of the heuristics and models that are taught in this class.
• Astrid
Great introduction to the world of scientific modelling without ever becoming boring. Great for all subjects. Gifted lecturer, great mix of tools (videos, experiments, examples). One of lasting impact.
• Anonymous
I took the class last year. It's one of the best courses that I have done so far. Prof. Scott covers political, economical, business , and many other models. It's amazing how different fields are connected.
• Anonymous
Very well taught by an enthusiastic and entertaining teacher. I learned a lot and would be excited about any available follow up.
• Sardar Hussain Salarzai
i learn more about the systematic thinking after completing this course. and would be able in future to understand any matter in model thinking.
• Anonymous
Best MOOC I've completed out of 35

Definitely changed my way of viewing the world
• Rui Rodrigues
Very interesting but in my case just didn't had much time to do it. But i recommended anyway. I will try again in a further session.
• Anonymous
high quality material with real life application.

Dr Page presentation is highly enjoyable.

recommended for all levels.
• Santosh Goteti
A very nice beginner level course with a lot of useful concepts which have a very wide range of applications.
• Bart
The way the course touches each subject lightly is fine for me. The wide variety of models described, the examples given and the way the theory is explained is well executed. All in all the subject matter is quite easy.

What I don't like is that sometimes the speed is brought to a halt because long lists of examples or numbers are summed up from the sheets that do not really help understanding the subject. This probably is just a personal annoyance.

The quizzes seem to test if I can remember what the teacher said, instead of making me think and apply the knowledge I just acquired.

Very easy.
• Enzo Altamiranda
Great course. It opened my mind to many new ideas that I didn't knew existed. The topics are so varied that unfortunately the contents don't have much depth, but Dr. Page teaches you just enough for you to start learning on your own about whatever topic picked your interest. Highly recommended!
• John OConnor
Models rule the world. We need models to make sense of the ocean of data that is being produced. This is a very gentle and interesting introduction to a very important topic.
• Eric Gehlhaar
The professor has a distinctive communication style combining approachability with rigour. It sneaks in some maths. Good access given to supplementary research papers

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