1 Teck H. Ho April 8, 2004 Outline  In-Class Experiment and Motivation  Adaptive Experience-Weighted Attraction (EWA) Learning in Games: Camerer and.

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Presentation transcript:

1 Teck H. Ho April 8, 2004 Outline  In-Class Experiment and Motivation  Adaptive Experience-Weighted Attraction (EWA) Learning in Games: Camerer and Ho (Econometrica, 1999)  Sophisticated EWA Learning and Strategic Teaching: Camerer, Ho, and Chong (JET, 2002)  Self-tuning EWA Learning (EWA Lite): Ho, Camerer, and Chong (2004)

2 Teck H. Ho April 8, 2004 Actual versus Belief-Based Model Frequencies: pBC (inexperienced subjects)

3 Teck H. Ho April 8, 2004 Actual versus Reinforcement Model Frequencies: pBC (inexperienced subjects)

4 Teck H. Ho April 8, 2004 Actual versus EWA Model Frequencies: pBC (inexperienced subjects)

5 Teck H. Ho April 8, 2004 Actual versus Belief-Based Model Frequencies: pBC (experienced subjects)

6 Teck H. Ho April 8, 2004 Actual versus Reinforcement Model Frequencies: pBC (experienced subjects)

7 Teck H. Ho April 8, 2004 Actual versus EWA Model Frequencies: pBC (experienced subjects)

8 Teck H. Ho April 8, 2004 Three User Complaints of EWA 1.0 u Experience matters. u EWA 1.0 prediction is not sensitive to the structure of the learning setting (e.g., matching protocol). u EWA 1.0 model does not use opponents’ payoff matrix to predict behavior.

9 Teck H. Ho April 8, 2004 Sophisticated EWA Learning (EWA 2.0) u The population consists of both adaptive and sophisticated players.  The proportion of sophisticated players is denoted by . Each sophisticated player however believes the proportion of sophisticated players to be  ’  u Use latent class to estimate parameters.

10 Teck H. Ho April 8, 2004 The EWA 2.0 Model: Adaptive players  Adaptive ( ) + sophisticated ( )  Adaptive players

11 Teck H. Ho April 8, 2004 The EWA 2.0 Model: Sophisticated Players  Adaptive ( ) + sophisticated ( )  Sophisticated players believe ( )proportion of the players are adaptive and best respond based on that belief:  Better-than-others ( ); false consensus ( )

12 Teck H. Ho April 8, 2004 Well-known Special Cases  Nash equilibrium:  ’ = 1 and  = infinity  Quanta response equilibrium:  ’ = 1  Rational expectation model:  ’  Better-than-others model:  ’

13 Teck H. Ho April 8, 2004 Results

14 Teck H. Ho April 8, 2004 MLE Estimates

15 Teck H. Ho April 8, 2004 Strategic Teaching u So far, all players are myopic. They only care about immediate payoffs. u Sophisticated players would want to “control” or “manipulate” the learning paths of the adaptive players if they are non- myopic (i.e., care beyond immediate payoffs). u In evaluating the attractiveness of a strategy, a strategic teacher compute its NPV, taking into account the potential of the strategy in influencing adaptive players to evolve into desirable outcomes in the future.

16 Teck H. Ho April 8, 2004

17 Teck H. Ho April 8, 2004 Out-of-Sample Prediction

18 Teck H. Ho April 8, 2004

19 Teck H. Ho April 8, 2004 In-Sample Calibration

20 Teck H. Ho April 8, 2004