Model Thinking

Model Thinking

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  • 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 bette...
  • 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 au...
  • 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....
  • 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...
  • 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...
  • Week 6 - Midterm Exam
  • 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...
  • 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 re...
  • 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,...
  • 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 way...
  • 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 con...
  • Week 12 - Final Exam
    The description goes here




Scott E. Page
Professor of Complex Systems, Political Science, and Economics
Center for the Study of Complex Systems


密歇根大学(UM,UMich 或简称密歇根)是一所公立研究型大学,位于美国密歇根州安阿伯市。该大学成立于 1817 年,是密歇根州历史最悠久、规模最大的大学。



Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

此内容评级为 4.6377/5
此内容评级为 4.6377/5