Model Thinking

Go to Course: https://www.coursera.org/learn/model-thinking

Introduction

### Course Review: Model Thinking **Platform**: Coursera **Course Title**: Model Thinking **Instructor**: [Instructor's Name] (Please check the course page for details) **Duration**: Approximately [insert duration] **Level**: Beginner to Intermediate #### Overview In today's fast-paced and complex world, understanding the behaviors of various actors—ranging from individuals and firms to governments—is crucial. The course "Model Thinking" aims to equip you with the skills needed to navigate this intricate landscape through the lens of modeling. By learning to think in models, you can enhance your ability to comprehend and predict a wide array of phenomena, from political uprisings to market dynamics and social trends. The course is structured to emphasize why models are essential tools for intelligent reasoning. Reflecting research suggesting that those who utilize multiple models tend to outperform those who rely on a single perspective, "Model Thinking" offers a robust framework for developing critical thinking skills. #### Course Content The syllabus is divided into multiple sections, each addressing different aspects and applications of models in social sciences: 1. **Why Model & Segregation/Peer Effects**: This introductory section outlines the importance of models in various contexts, fostering better decision-making and strategic thinking through readings and discussions on the works of Schelling and Granovetter. 2. **Aggregation & Decision Models**: Dive into the concept of aggregation, exploring how individual behaviors combine to influence broader outcomes. This section emphasizes the Central Limit Theorem and includes engaging examples like the Game of Life. 3. **Thinking Electrons: Modeling People & Categorical and Linear Models**: Focus on different approaches to modeling behavior, applying rational actor and behavioral models to understand decision-making processes. 4. **Tipping Points & Economic Growth**: Investigate models related to tipping points in social phenomena, such as the spread of diseases, with insightful readings and comparisons. 5. **Diversity and Innovation & Markov Processes**: Learn how diversity enhances problem-solving capabilities and how models can illustrate the effects of varying perspectives. 6. **Lyapunov Functions & Coordination and Culture**: Understand system outcomes using Lyapunov Functions, exploring how they help identify equilibria and the dynamics within cultural systems. 7. **Path Dependence & Networks**: Examine path dependence through simple models, learning how historical processes can shape outcomes and relate to economic principles. 8. **Randomness and Random Walks & Colonel Blotto**: Discover randomness in models and its implications for market behavior, distinguishing skill from luck. 9. **Prisoners' Dilemma and Collective Action & Mechanism Design**: Delve into collective action problems, learning how individual incentives can steer societal outcomes. 10. **Learning Models: Replicator Dynamics & Prediction and the Many Model Thinker**: Explore how replicator dynamics contribute to understanding evolution and learning, incorporating relevant theories. 11. **Final Assessment**: The course wraps up with a comprehensive final exam to assess your understanding of the material. #### Pros and Cons **Pros:** - **Diverse Topics**: The course covers a wide array of models, helping learners develop a versatile toolkit for analyzing complex systems. - **Engaging Lectures**: The lectures are dynamic and accessible, making complex theories easy to grasp. - **Real-World Application**: Each section ties concepts back to real-world phenomena, demonstrating the practical utility of models. - **Recommended Readings**: Supplementary material offers deeper insights and encourages further exploration of key concepts. **Cons:** - **Complexity**: Some sections may become technically dense, requiring additional effort in comprehension. - **Self-Paced**: As with many online courses, the effectiveness is partly dependent on your self-discipline to manage your learning pace. #### Recommendation I highly recommend **Model Thinking** for anyone interested in enhancing their analytical skills and understanding of complex social phenomena. Whether you are a student, a professional in a decision-making role, or an individual keen on making sense of the intricate dynamics around you, this course will offer valuable insights. With its comprehensive curriculum and emphasis on practical modeling applications, you'll not only learn how to think more clearly but also how to utilize a variety of models to tackle real-world issues. The tools you gain from this course will empower you to navigate and interpret the complexities of modern society with confidence and sophistication. Consider enrolling now to expand your mental toolkit and improve your critical thinking capabilities!

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.

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.

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.

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.

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.

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.

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

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.

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.

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.