simulate.py

import random
import time
import matplotlib.pyplot as plt
from World import World
from Board import RectangularGrid
from Game import PrisonersDilemma, Player, PrisonersDilemmaPlayer, Strategy
from SimulationStatistics import StrategyFractionsTimeSeries


def simulate(
    world, stats=None, time_max=30, iteration_max=100000, show_animation=False
):
    """simulates the evolution of strategies in the world, allowing for
    visualization and the recording of statistics
    """
    t = time.time()
    time_max = t + time_max
    iteration = 0

    if show_animation:
        world.board.draw()

    # record statistics
    if stats:
        stats.record_stats(world, iteration)

    while t < time_max and iteration < iteration_max:
        # perform one round of updates
        world.round()
        if show_animation:
            world.board.draw()

        # record statistics
        if stats:
            stats.record_stats(world, iteration)
            # stop simulation based on statistics
            if stats.end_simulation(world, iteration):
                break

        # update loop variables
        iteration += 1
        t = time.time()

    if show_animation:
        world.board.quit_animation()

    if stats:
        stats.print_results()


if __name__ == "__main__":
    """ Adjust simulation parameters here """

    # define the game played between two players during an interaction
    # choose a game type from Game.py
    T = 1.3
    R = 1
    P = 0.1
    S = 0
    game = PrisonersDilemma(T, R, P, S)

    # define a world topology ("board") - e.g. grid, network
    # choose a board type from Board.py
    grid_height = 50
    grid_width = 50
    board = RectangularGrid(grid_height, grid_width)

    # define the players in the world
    # choose a player type from Game.py
    num_players = grid_height * grid_width // 2
    p_cooperation = 0.5

    players = []
    for i in range(num_players):
        rand = random.random()
        if rand < p_cooperation:
            players.append(PrisonersDilemmaPlayer(Strategy.cooperate))
        else:
            players.append(PrisonersDilemmaPlayer(Strategy.defect))

    # define player update parameters
    r = 0.05  # probability that a player randomly resets its strategy
    q = 0.05  # conditional probability that a player resets to cooperate
    noise1 = True  # a boolean indicating whether Noise 1 is present
    noise2 = False  # a boolean indicating whether Noise 2 is present
    imitation = True  # a boolean indicating whether players perform imitation
    migration = True  # a boolean indicating whether players perform migration
    M = 5  # the range of the Moore neighborhood around each cell

    # simulation parameters
    time_max = 15
    iteration_max = 1500
    show_animation = True

    # define the statistics to record in the simulation
    # choose a simulations type from SimulationsStatistics.py
    stats = StrategyFractionsTimeSeries()

    # define the world to simulate evolution of strategies
    world = World(game, board, players, r, q, noise1,
                  noise2, imitation, migration, M)

    # perform simulation
    simulate(world, stats, time_max, iteration_max, show_animation)