model thinking
section 2: segregation and peer effects
4. peer effects
Subtopic
model
avg value - how likely that everyone will do it
each one has a threshold (how many people you need to join them)
N individuals
3. measuring segregation
1. rich/poor
for perfectly segregaged - 2; for perfectly mixed 0
total index: 6*1/45 + 6*1/9 + 12* 1/15= 72/45 (total index)
for blue - 1/15; yellow - 1/9; green - 1/45
| b/B - y/Y | - if equals 0, totally not segregated - index of similarity
b=rich in block; B - rich total; y/Y - poor
24 blocks: 12 rich, 6 50-50, 6 poor
2. schelling segregation
3. checkers model
3/7 good; 2/7 bad
how many people around you are rich
2. racial - "ghettos"
1. financial - by income (model of NY)
1. sorting and peer effects
peer effect: peers start acting like surrounding
sorting effect: people tend to communicate with similars (detroit - black and white or smokers)
section 1: why model
1.5 using models to analyse
7. help choose among institutions (democratics or communism?)
6. institutional design
5. experimental design (models instead of real experiments)
4. identifying leverages (if greece fails, all europe fails)
3. conterfactuals (with or without recovery plan - what would happen?)
2. comparative statics (as-ad curves)
1. rral time desicion aid (monty hall paradox)
1.4 analysing data
8. calibrate - make the model closer to real world - forest fires.
7. estimate hidden parameters (number of sick people and sickness progress)
6. inform data collection - which data do we need?
5. predict sth else (planet with big mass)
4. retrodict - test models by running from past
3. predict boundaries ( infl 0..3%)
2. predict points (for ex., price of the house basing on metrage)
1. see patterns (horizontal, rising, falling, cyclical...)
