Skill and Luck - Given a scenario, be able to determine whether luck or skill is more influential. Know the equation for outcomes based on luck and skill. Understand the relationships between random walks and luck and skill. [See 15.3, "Skill and Luck"]
Urn Models - Know the different kinds; understand the mechanisms of how they work - how many "blue" balls come out when one "red" ball goes in, for example. [See 13.2, "Urn Models"]
Networks - Be able to calculate path length, degree, clustering coefficients. Understand each of these concepts well enough to know what a network looks like given path length, degree, and clustering coefficient. Given these numbers, be able to determine which of several networks is the best fit. [See 14.2, "The Structure of Networks"]
Path and Phat Dependance - Understand these concepts, their similarities and differences. Also understand the relationship between path dependance and tipping points, and understand the role of path dependance in public projects. [See 13.5, "Path Dependence and Increasing Returns"]
What are the four classes of outcomes?
And from sections 19 and 20:
Replicator Dynamics - know how to use the equations; know the relationship between weights, fitness levels, and proportions. [See 19.2, "The Replicator Equation"]
Fisher's Theorem - What is it? [See 19.3, "Fisher's Theorem"]
Diversity Prediction Theorem - Calculate all parts of the equation, including average prediction, crowd prediction, crowd error, crowd diversity, etc. [See 20.3, "Diversity Prediction Theorem"]
2013-12-22 (120min) Coursera open course: Model Thinking by Scott E. Page Start from 10.07.2013, will end on 12.16.2013 Link: https://class.coursera.org/modelthinking-005/lecture/index;
The topics for the final exam are as follows:
Skill and Luck - Given a scenario, be able to determine whether luck or skill is more influential. Know the equation for outcomes based on luck and skill. Understand the relationships between random walks and luck and skill. [See 15.3, "Skill and Luck"]
Urn Models - Know the different kinds; understand the mechanisms of how they work - how many "blue" balls come out when one "red" ball goes in, for example. [See 13.2, "Urn Models"]
Networks - Be able to calculate path length, degree, clustering coefficients. Understand each of these concepts well enough to know what a network looks like given path length, degree, and clustering coefficient. Given these numbers, be able to determine which of several networks is the best fit. [See 14.2, "The Structure of Networks"]
Path and Phat Dependance - Understand these concepts, their similarities and differences. Also understand the relationship between path dependance and tipping points, and understand the role of path dependance in public projects. [See 13.5, "Path Dependence and Increasing Returns"]
What are the four classes of outcomes?
And from sections 19 and 20:
Replicator Dynamics - know how to use the equations; know the relationship between weights, fitness levels, and proportions. [See 19.2, "The Replicator Equation"]
Fisher's Theorem - What is it? [See 19.3, "Fisher's Theorem"]
Diversity Prediction Theorem - Calculate all parts of the equation, including average prediction, crowd prediction, crowd error, crowd diversity, etc. [See 20.3, "Diversity Prediction Theorem"]