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Authors@Google: Nate Silver
http://www.youtube.com/watch?v=mYIgSq-ZWE0&feature=em-uploademail

Probablistic Programming & Bayesian Methods for Hackers
http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

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Model Thinking Part 2 – Coarsera

Model thinking – Scoot E. Page – U. of Michigan

Modeling People

3 types of models: rational actor, behavioral, and rule based

  • Rational Actor: maximize some function, object, optimized
  • Behavior: observe what people do, neuroscience supports this view
  • Rule base _ e.g. Schelling model, people follow rule(s)

Rational Actor

  • An objective
  • Optimize the objective

Value is to establish benchmark to be used because: (also can determine how far people from rational choice)

  • Objective decision – reduced bias
  • Easy to mathematically solve for
  • People learn by repeating, hence closer to good result
  • Mistakes cancel out, eliminating bias

Behavior Model

Daniel Kahnemen says people have slow process (rational) and fast process (emotional) thinking

Bias examples

  • Prospect theory – bird in hard worth two in the bush
  • Hyperbolic – take immediate reward and discount future pain
  • Status Quo — stick with current situation
  • Bias Rate — first rate estimate will have a second rate estimate be make similar

Some people say above biases are WEIRD — essentially only happens in developed countries

Approach to take in modeling:  look at rational then introduce bias, then look at potential rules.

Rule Based

Follow a rule(s), e.g. Schelling. Rules can be easy to understand, capture main effort,  can be ad hoc, can be exploitable, may note be optimal

Two types in two contexts which are Decision and Game

Fixed — might be random, tit for tat, grime trigger

Adaptive — in Decision context might be gradient (take something that works and extend), in Decision might be Best Response, mimicry (copy other’s that work)

Impact of 3 models

  • Market — type of model does not matter much since market forces drive towards a mean. Zero Intelligence Agent will bid randomly lower (if buyer) and randomly higher (if seller) and than average to actual cleared price.
  • Race to Bottom (game) — type of model can matter as follows  — (1) Rational bid would b zero since rational person would always assume some number and quote a number 2/3’s from the mean (goal of the game).  He would continue to iterate this quoted number down since he would assume that everyone is rational and hence will be eventually  iterating down to. (2) Biased (behavior  model) would probably just pick 50. (3)  Rule  (best response) – will guess 50, some will then take 2/3 of 50 (33), then some will take 2/3 of 33 (22), then over the long run, down to zero — rule is a mix of rational and biased. If all people in game are rational, then new irrational person enters game, will cause rational people to be influence by what the rational person perceives the irrational person will do.

Categorical and Linear Models

  • Categorical Models — group sample and analyze sub-groups to see how well the grouping covers (reduces variance) the data — see r_squared.py for detailed explanation.  R-Squared indicates how much of the data variance was explained by the model — Correlation indicates a relationship between variables, BUT Causation means that one variable is actually dependent an another — Or X (independent) entails Y (dependent) check this last statement
  • Linear mode – draw a line through the data — Y depends on X, Y = F(X), Y is a function of X
  • Non-linear model – curve, some similarities to linear model
  • Big Coefficient – shows how important X is in Y = a1 x1 + a2 x2  — useing Big Coefficient as a guide only makes sense in world where there is lots of data