|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/model-thinking
课程评论：没有评论
课程名称：模型思维 课程概述：在一个复杂的世界中，各种个体、企业和政府的行为汇聚成意想不到的现象，如政治动荡、市场崩溃和不断变化的社会趋势。我们如何理解这一切？答案是：模型。研究表明，使用模型进行思考的人表现得更好，而使用多种模型的人则优于只使用单一模型的人。模型能够帮助我们更好地组织信息，从而明晰互联网海量数据。它们提高了我们的预测能力，改善了我们决策和战略制定的能力，并可以帮助我们设计更有效的制度和程序。本课程提供了一套模型入门包，涵盖了 tipping points、群体智慧、国家贫富差异及企业与政治家的战略决策等模型。这些模型为未来的社会科学课程打下基础，掌握这些内容将使您在高级课程中更具优势，同时也对生活中的思考大有裨益。 课程大纲： 1. 为什么要建模与隔离/同行效应 - 讲述建模课程的目的，包括成为明智的公民、清晰思考、理解数据以及改进决策。 2. 聚合与决策模型 - 探讨数字聚合及其背后的规则，包括生命游戏和一维元胞自动机模型，以及偏好的聚合问题。 3. 模拟人类及分类和线性模型 - 研究社科领域中如何建模人类，比较理性行为者、行为模型和基于规则的模型。 4. 转折点与经济增长 - 关注转折点模型，涵盖疾病传播模型及其扩散和恢复过程。 5. 多样性与创新及马尔可夫过程 - 研究多样性在创新中的角色，介绍“崎岖景观”和“局部最优”的概念。 6. 期中考试 7. Lyapunov 函数与协调与文化 - 学习如何利用 Lyapunov 函数确定系统产出的特性，如均衡、周期性等。 8. 路径依赖与网络 - 通过简单的尿罐模型探讨路径依赖及其与回报递增和转折点的关系。 9. 随机性与随机游走及 Colonel Blotto - 讨论随机性的来源及其对表现的影响，学习随机游走模型。 10. 囚徒困境、集体行动与机制设计 - 讨论囚徒困境及合作产生的策略，并探讨集体行动与公共资源问题。 11. 学习模型：复制者动态与预测 - 介绍复制者动态和 Fisher 的基本定理，以及与六西格玛和变异性减少的关系。 12. 期末考试 本课程的设计旨在提供易于消化的概述及深入的技术细节，完成所有测验并参加期末考试的学生将获得课程证书。若只是想跟随入门课程获取知识，也欢迎随意参与，全部内容免费，旨在帮助您成为更好的思考者！
Name:Why Model & Segregation/Peer Effects
Description: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.
Name:Aggregation & Decision Models
Description: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.
Name:Thinking Electrons: Modeling People & Categorical and Linear Models
Description: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.
Name:Tipping Points & Economic Growth
Description: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.
Name:Diversity and Innovation & Markov Processes
Description: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.
Name:Midterm Exam
Description:
Name:Lyapunov Functions & Coordination and Culture
Description: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.
Name:Path Dependence & Networks
Description: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
Name:Randomness and Random Walks & Colonel Blotto
Description: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.
Name:Prisoners' Dilemma and Collective Action & Mechanism Design
Description: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.
Name:Learning Models: Replicator Dynamics & Prediction and the Many Model Thinker
Description: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.
Name:Final Exam
Description:
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!
