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



CHEN & GAZZALE (2004):

Phase Plane Method


Experiment Design

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.


Dynamics Function



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