Prediction and The Many Model Thinker

This set of lectures cover:

1. Predictions
2. Diversity Prediction Theorem 
3. The Many Model Thinker

1. Predictions

We use categories to make sense of the world i.e. we "lump to live".

Categories for Predictions

We create categories.
Categories reduce variation (as measured by R-Squared).
The lower the variation, the better we get at predicting.
Different people create different categories.

Linear models for Predictions

Linear models assume a linear combination of components make up the whole.
The property of the whole can be predicted by knowing the property of the components.

2. Diversity Prediction Theorem

Relates the wisdom of the crowd to the wisdom of the individuals.
Crowd's accuracy depends on individual accuracy and crowd diversity.

Average individual errors are higher than average crowd error.
Diversity is the variation in prediction.

Diversity is the square of individual predictions from crowd average.

The Diversity Prediction Theorem states:

  Crowd's Error = Average Error - Diversity

  (c-v)^2 = 1/n * Sum(s[i] - v)^2 - 1/n * Sum(s[i] - c)^2

  where:
      v is true value
      s[i] is individual prediction
      c is crowd prediction
 The above is always true.

Read Wisdom of Crowds by Jim Surowiecki.

Wisdom of crowds come from reasonably smart people who are diverse.
Madness of crowds come from like-minded people who are all wrong.

3. The Many Model Thinker

Reason #1: Intelligent Citizen of the World

• Growth model - investing in capital to grow
• Solow Growth model - innovation matters
• Colonel Blotto Game - How adding new dimensions

Reason #2: Clearer Thinker

• Markov Models - history doesn't matter, some interventions don't help
• Tipping Points - difference between tipping points 
• Develop intuition of how things pan out out over time - different types of curves 
• Things aggregate differently - More is Different

Reason #3: Understand and Use Data

• Category Models & Linear Models
• Growth Models

Reason #4: Decide, Strategize, and Design

• Game Theory Models 
• Mechanism Design - design institutions and incentive structures
• Concept of Incentive Compatibility

 Understanding of how people behave

• Rational behaviour
• Psychological behaviour
• Rule-based behaviour