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Course: Model Thinking @ Coursera
0001 Jun 1
2 minutes read

Course: Model Thinking @ Coursera

Why model ?

  • Intelligent Citizen of the World
  • Clearer Thinker
  • understand and use data
  • decide, strategize, design


Thinking More Clearly

Outcome

  • equilibrium
  • cycle
  • random
  • complex

Communicate

Using and Understanding Data

  • Understanding Patterns
  • Predict Points
  • Produce Bounds
  • Retrospective analysis
  • Predict Other
  • Informing data collections
  • Estimate hidden parameters
  • Calibrate

Using Models to Decide, Strategize, and Design

  • Decision Aids
  • Comparative Statics
  • Counterfacts
  • Identify and Rank Levers
  • Experimental Design
  • Institutional Design
  • Helping to Choose Among Policies and Institutions

Segregation and Peer Effects

Sorting and Peer Effects Introduction

Approaches
    Schelling's Segregation Model                           
    Granovetter
        The model consists of N individuals, each of whom has a threshold for a certain behavior. If there are enough 'extremists' in this model with very low thresholds, collective action may occur despite the presence of other individuals with very high thresholds.
    Standing Ovation
    Identification
Models
    Equation Based Model
    Agent Based Model (In agent-based modeling, we model a system that is a collection of autonomous, decision-making individuals called agents. These agents make decisions on the basis of a particular set of rules. We then look at these decisions in the aggregate to see what types of macro-level behaviors or patterns emerge.)            
        Individuals
        Behaviors
        Outcomes

Schelling’s Segregation Model

        Threshold for Decision
        Micromotives ≠ Macrobehaviour
        Tipping
            Exodus
            Genesis

Measuring Segregation

Index of Dissimilarity
|b/B - y/Y| / 2
 dividing by 2 is a tool to change the scale of Index of Dissimilarity so that our upper limit will be 1 rather than 2. 

Peer Effects

N Persons
Tj — a Threshold for Joining

The Standing Ovation Model

Threshold to Stand: T
Quality: Q
Signal: S = Q + E
    E = Error, Diversity
Initial Rule
    Stand, if S > T
Subsequent Rule
    Stand, if more than X % stand
Claims
    Higher Q — More People Stand
    Lower T — More People Stand
    Lower X — More Ovations
    If Q < T, More Variation in E — More Stand

Identification Problem

Static Data Couldn't Give a Clue, Why Segregation Appears
We Need the Dynamic Data to Know, Why Objects Are Segregated
    Move = Sorting
        The basic idea here is that when agents in the model - people - are choosing to surround themselves with others who are similar, we consider it sorting.
    Change = Peer Effect
        peer effects, in which agents are influenced by others who are around them.

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