sim_learning | R Documentation |
sim_learning()
simulates learning dynamics in a
normal-form game expected by an experienced weighted attraction (EWA)
model.
sim_learning(
game,
n_samples,
n_periods,
type = "EWA",
lambda = 1,
delta = 0.5,
rho = 0.5,
phi = 0.5,
A1_init = 0,
A2_init = 0,
N_init = 0,
plot_range_y = NULL
)
game |
An object of |
n_samples |
A positive integer specifying the number of samples to be simulated. |
n_periods |
A positive integer specifying how many times the game is played within each sample. |
type |
A character string to tell which learning models should be
simulated. The available options are |
lambda |
A positive real value representing the players' sensitivity to
attraction values of strategies. As |
delta |
A real number between 0 and 1. This parameter controls how fast
attraction values of strategies that are not chosen are updated. If
|
rho |
A real value between 0 and 1. This parameter controls the learning
speed. |
phi |
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 |
A1_init |
An initial value of Player 1's attraction for each strategy. |
A2_init |
An initial value of Player 2's attraction for each strategy. |
N_init |
An initial value of N. |
plot_range_y |
Choose the range of vertical axis for plots. Available
choices are |
Simulate plays of a normal-form game defined by
normal_form()
in a way expected by an EWA model.
A list containing (1) a list of data frames of strategies chosen by each player, (2) a single long data frame of (1)'s data frames combined, (3) a list of each player's attraction values for each strategy (data frames), (4) a list of probability of each strategy being chosen (data frames), and (5) a plot of the simulation result (ggplot object).
Yoshio Kamijo and Yuki Yanai yanai.yuki@kochi-tech.ac.jp
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