sim_EWA: Simulates an Experienced Weighted Attraction model
In yukiyanai/rgamer: rgamer: Learn Game Theory Using R

sim_EWA

R Documentation

Simulates an Experienced Weighted Attraction model

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
)

`game`	An object of `normal_form` class defined by `normal_form()`.
`n_periods`	A positive integer specifying how many times the game is played within each sample.
`lambda`	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.
`delta`	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).
`rho`	A real value between 0 and 1. This parameter controls the learning speed. `rho = 0` for "reinforcement" leaning and "belief" based learning.
`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 `phi = 0`, the past attraction values are ignored. `phi = 1` for "reinforcement" leaning and "belief" based learning.
`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.