Pattern in Strategy Space of Supermodularity Games
When Does Learning in Games Generate Convergence to Nash Equilibria?
The Role of Supermodularity in an Experimental Setting
By YAN CHEN AND ROBERT GAZZALE
CHEN & GAZZALE (2004):
Phase Plane Method
Result from time tracing
The Three Learning Models for Competition
The models we examine are
stochastic fictitious play with discounting (hereafter shortened as sFP)
(Yin-Wong Cheung and Daniel Friedman, 1997; Fudenberg and Levine, 1998),
functional EWA (fEWA) (Teck-Hua Ho et al., 2001) and the payoff assessment
learning model (PA) (Rajiv Sarin and Farshid Vahid, 1999).
We now give a brief overview of each model. Interested readers are referred to the originals for complete descriptions.
We then compare the simulated paths with the experimental data to find those parameters that minimize the mean-squared deviation (MSD) scores.
Result from Field Method
(Need to add title for each figure in Matlab, also label of both axis, red-price and blue-price)
|Experimental Result||Theoretical Result|
The Evolutionary Game Theory Figure Result