Midterm Study Guide
List of Topics for Midterm Review:
Schelling's Segregation Model - Big "takeaways" and relationship to Tipping Points [See Section 2.2, "Schelling's Segregation Model"]
Standing Ovation Model - overview, relationship between variance, show quality, likelihood of standing ovation, etc. [See Section 2.5, "The Standing Ovation Model"]
Sorting and Peer Effects - definitions, differences (Identification Problem), and example of when to apply them [See 2.6, "Identification Problem"]
Six Sigma - calculating six sigma, calculating standard deviation [See 3.3, "Six Sigma"]
Thinking Electrons - when would you use a rational model vs. when would you use a behavioral or rule-based model, examples of each [See 5.2, "Rational Actor Models"] and Section 5 in general.
Linear Models - calculate R-squared and know what it means. Know when an equation or situation is "linear" [See 6.3, "Linear Models"]
Peaked Landscapes - what are they, what is their relationship to linear models, also know dancing landscapes [See 6.3]
Tipping Points - define, calculate diversity index, know the difference between direct and contextual tips (lecture 7.5) and also between exodus and genesis tips, and "within class" and "between class" tips [You may want to review section 7 in general, and pay special attention to 7.5, "Classifying Tipping Points"]
Economic Growth - relationship between investment, depreciation, innovation, and equilibrium [See 8.3, "Economic Growth"]
Markov - be able to set up a markov matrix, and use it to calculate percentages in different categories at equilibrium. Know, in a general sense, in what situations it applies [See 10.2, "Markov Equilibrium"]
Heuristics - No Free Lunch Theorem (lessons we learn), what are heuristics, what makes heuristics diverse [See 9.4, "Teams and Problem Solving"]
Decision Trees - be able to draw one, calculate expected value and make decisions using them given some data [See 4.5, "Decision Trees"]
Spatial Choice - know what it is, know when it applies. Given some information, can we use spatial choice? [See 4.3, "Spatial Choice Models"]
2013-11-07 (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/class/index;
Vocabulary learning:
Midterm Study Guide List of Topics for Midterm Review:
Schelling's Segregation Model - Big "takeaways" and relationship to Tipping Points [See Section 2.2, "Schelling's Segregation Model"]
Standing Ovation Model - overview, relationship between variance, show quality, likelihood of standing ovation, etc. [See Section 2.5, "The Standing Ovation Model"]
Sorting and Peer Effects - definitions, differences (Identification Problem), and example of when to apply them [See 2.6, "Identification Problem"]
Six Sigma - calculating six sigma, calculating standard deviation [See 3.3, "Six Sigma"]
Thinking Electrons - when would you use a rational model vs. when would you use a behavioral or rule-based model, examples of each [See 5.2, "Rational Actor Models"] and Section 5 in general.
Linear Models - calculate R-squared and know what it means. Know when an equation or situation is "linear" [See 6.3, "Linear Models"]
Peaked Landscapes - what are they, what is their relationship to linear models, also know dancing landscapes [See 6.3]
Tipping Points - define, calculate diversity index, know the difference between direct and contextual tips (lecture 7.5) and also between exodus and genesis tips, and "within class" and "between class" tips [You may want to review section 7 in general, and pay special attention to 7.5, "Classifying Tipping Points"]
Economic Growth - relationship between investment, depreciation, innovation, and equilibrium [See 8.3, "Economic Growth"]
Markov - be able to set up a markov matrix, and use it to calculate percentages in different categories at equilibrium. Know, in a general sense, in what situations it applies [See 10.2, "Markov Equilibrium"]
Heuristics - No Free Lunch Theorem (lessons we learn), what are heuristics, what makes heuristics diverse [See 9.4, "Teams and Problem Solving"]
Decision Trees - be able to draw one, calculate expected value and make decisions using them given some data [See 4.5, "Decision Trees"]
Spatial Choice - know what it is, know when it applies. Given some information, can we use spatial choice? [See 4.3, "Spatial Choice Models"]
Classes of outcomes - There are 4.