def main():

    random_seed = np.random.randint(4294967295)

    parameters = {
        "random_seed": random_seed,
        "n_generations": 100,
        "n_periods_per_generation": 30,
        "n_goods": 5,
        "n_agents": 100,
        "p_mutation": 0.1,
        "mating_rate": 0.3
    }

    e = Economy(**parameters)

    e.run()
Exemple #2
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def compute(random_seed):

    parameters = {
        "mating_rate": 0.3,
        "max_production": 10,
        "n_agents": 300,
        "n_generations": 10**4,
        "n_goods": 3,
        "n_periods_per_generation": 5,
        "p_mutation": 0.1,
        "production_advantages": [4, 2, 0.5],
        "production_costs": [4, 2, 2],
        "random_seed": random_seed,
        "u": 10
    }

    e = Economy(**parameters)

    return parameters, e.run()
def fun(args):

    n_periods_per_generation = int(args[0])
    random_seed = np.random.randint(4294967295)
    # random_seed = 4043318547

    parameters = {
        "random_seed": random_seed,
        "n_generations": 50,
        "n_periods_per_generation": n_periods_per_generation,
        "n_goods": 3,
        "n_agents": 1000,
        "p_mutation": 0.05
    }

    e = Economy(**parameters)

    backup = e.run()

    return np.mean(backup["production_diversity"][-10:])
Exemple #4
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from economy import Economy
from graph import graph

from os import path

random_seed = 460741801

parameters = {
    "mating_rate": 0.3,
    "max_production": 10,
    "n_agents": 300,
    "n_generations": 10**4,
    "n_goods": 3,
    "n_periods_per_generation": 5,
    "p_mutation": 0.1,
    "production_advantages": [4, 2, 0.5],
    "production_costs": [4, 2, 2],
    "random_seed": random_seed,
    "u": 10
}

e = Economy(**parameters)

backup = e.run()
graph(results=backup,
      parameters=parameters,
      root_name="MB",
      root_folder=path.expanduser("~Desktop/MoneyBootstrapping"))