sim_EWA() simulates plays of a normal-form
game expected by an experienced weighted attraction (EWA) model.
sim_EWA(
game,
n_periods,
lambda = 1,
delta = 0.5,
rho = 0.5,
phi = 0.5,
A1_init = 0,
A2_init = 0,
N_init = 0
)An object of normal_form class defined by
normal_form().
A positive integer specifying how many times the game is played within each sample.
A positive real value representing the players' sensitivity to
attraction values of strategies. As lambda gets larger, the choice
will be dependent on attraction values more heavily. As lambda
gets close to 0, a strategy will tend to be chosen randomly.
A real number between 0 and 1. This parameter controls how fast
attraction values of strategies that are not chosen are updated. If
delta = 0, attraction is updated only for the strategy that is
selected at the given period (i.e., reinforcement learning is
implemented). If delta = 1, attraction is updated equally for all
strategies (i.e., belief-based learning model is applied).
A real value between 0 and 1. This parameter controls the learning
speed. rho = 0 for "reinforcement" leaning and "belief" based
learning.
A real value between 0 and 1. This parameter controls how much
attraction values at the current period are constrained by the past
attraction values. If phi = 0, the past attraction values are
ignored. phi = 1 for "reinforcement" leaning and "belief" based
learning.
An initial value of Player 1's attraction for each strategy.
An initial value of Player 2's attraction for each strategy.
An initial value of N.
Simulate plays of a normal-form game defined by
normal_form() in a way expected by an EWA model.