---
# System prepended metadata

title: Course Sylabus
tags: [Courses, Model Thinking]

---

Course Sylabus
===
###### tags: `Model Thinking` `Courses`

This course will consist of twenty sections. As the course proceeds, I will fill in the descriptions of the topics and put in readings. The readings are attached to their sections in this syllabus, and many can also be found on the same page as the video they correspond to.

## Section 1: Introduction: Why Model?
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
5. 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.

### Readings
-  [The Model Thinker](https://hackmd.io/daWVIkRTTsSRjQeiH8Yxvg#The-Model-Thinker) - Chapter 1
- [Why Model? by Joshua Epstein](https://drive.google.com/file/d/16RSLb_zqqYVYtzrRP6k-OrF1FWyREUck/view?usp=sharing)
- [Model Thinker - Chapter 1](https://drive.google.com/file/d/1Mwsfj1Sks95nmlnN9BD_5B0wcQPm6PXF/view?usp=sharing)

## Section 2: Sorting and Peer Effects

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 follow those with a fun model about standing ovations that I wrote with my friend John Miller.

In this second section, I show a computational version of Schelling's Segregation Model using NetLogo. Netlogo is free software authored by Uri Wilensky or Northwestern University. I will be using NetLogo several times during the course. It can be downloaded here:

[NetLogo](http://ccl.northwestern.edu/netlogo/)

The Schelling Model that I use can be found by clicking on the "File" tab, then going to "Models Library". In the Models Library directory, click on the arrow next to the Social Science folder and then scroll down and click on the model called Segregation.

### Readings:

The readings for this section include some brief notes on Schelling's model and then the academic papers of Granovetter and Miller and Page. I'm not expecting you to read those papers from start to end, but I strongly encourage you to peruse them so that you can see how social scientists frame and interpret models.

* [Notes on Schelling](https://drive.google.com/file/d/1tHMdgRe3dN-RQ74tXCLO-3srQFl1xnk5/view?usp=sharing)
* [Granovetter Model](https://drive.google.com/file/d/1XYnHPESKLIvZDwRlX1yzWhHN8uDpL8np/view?usp=sharing)
* [Miller and Page Model](https://drive.google.com/file/d/1XYnHPESKLIvZDwRlX1yzWhHN8uDpL8np/view?usp=sharing)

## Section 3: Aggregation

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 systems](https://hackmd.io/daWVIkRTTsSRjQeiH8Yxvg#Complex-Adaptive-Systems) and [The Difference](https://hackmd.io/daWVIkRTTsSRjQeiH8Yxvg#The-Difference) respectively.

### Readings

- [Central Limit Theorem](http://onlinestatbook.com/stat_sim/sampling_dist/)
- [Binomial Distribution](http://www.stat.yale.edu/Courses/1997-98/101/binom.htm)
- [Six Sigma](https://en.wikipedia.org/wiki/Six_Sigma)
- Cellular Automata [1](https://drive.google.com/file/d/1KNPoM5w0Sk1ffR7p7yZbUAfv9Zby6qzL/view?usp=sharing) and [2](https://drive.google.com/file/d/1V0gv2rzpRx9XJc7zmzk3Z7E-H0Bq0mdj/view?usp=sharing)
- [Diverse Preferences](https://drive.google.com/file/d/15oLB2ZhCO50CppJ75uUULNXggacrFbzQ/view?usp=sharing)

## Section 4: Decision Models

In this section, we study some models of how people make decisions. We start by considering multi criterion decision making. We then turn to spatial models of decision making and then decision trees. We conclude by looking at the value of information.

The reading for multi-criterion decision making will be my guide for the Michigan Civil Rights Initiative. It provides a case study for how to use this technique. For spatial voting and decision models, there exist many great power point presentations and papers on the web. The Decision Tree writings are from Arizona State University's Craig Kirkwood.

### Readings
* [Multi Criterion Decision Making Case Study](https://drive.google.com/file/d/1wGccVvRZfCtj6xBi6JsNgN2wmF4PjSs1/view?usp=sharing)
* [Spatial Models](https://drive.google.com/file/d/1AMYcmolP5bk7lwT6sgDgflwWgOZM-Pto/view?usp=sharing)
* [Decision Theory](https://drive.google.com/file/d/1TMIeYJl3pybWrxQYZI9CCLqLKUi7qHeO/view?usp=sharing)


{%hackmd G-uuuRi2RyKS_IyjBJS3Kw %}