Free Online Course offered by Google on Model Thinking

Coursera Platform is with Google offering a free online course with the theme of "Model Thinking".

Course background:

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. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. In this class, I present a starter kit of models: I start with models of tipping points.

Course duration:

Approx. 25 hours to complete.

Course topics:

• 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 better decide, strategize, and design.

• 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 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.

• 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 diffusion. The second part adds recovery.

• 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 some new concepts like "rugged landscapes" and "local optima".

• 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 many systems that go to equilibrium. In addition, they enable us to put bounds on how quickly the equilibrium will be attained.

• 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.

• 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, the ideas that market prices contain all relevant information so that what's left is randomness.

• 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 ways to produce cooperation.

• 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.

• Week 12: Final Exam.

Course costs:

You can either:

• Purchase course for $49 USD:

Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

• Or full course for free, no certificate:

You will still have access to all course materials for this course.

See more available competition and awards on For9a 

See more available opportunities in Turkey on For9a