def main(): storing_costs = 0.10, 0.20, 0.24 u = 1 parameters = { "t_max": 500, "agent_parameters": { "alpha": 0.1, "temp": 0.1, "gamma": 0.1, "q_values": np.ones((6, 2)) }, "repartition_of_roles": np.array([50, 50, 50]), "storing_costs": storing_costs, "u": u, "agent_model": RL2StepsAgent, } e = Economy(**parameters) backup = e.run() # backup["last_strategies"] = [agent.strategies for agent in e.agents] # save(backup) represent_results(backup=backup, parameters=parameters)
def main(): storing_costs = 0.01, 0.04, 0.09 # [0.1, 0.20, 0.24] u = 1 beta = 0.9 agent_parameters = { "acceptance_memory_span": 1000, "encounter_memory_span": 1000, "temp": 0.1, } parameters = { "t_max": 500, "agent_parameters": agent_parameters, "repartition_of_roles": np.array([50, 50, 50]), "storing_costs": storing_costs, "u": u, "beta": beta, "agent_model": FrequentistAgent } expected_equilibrium = compute_equilibrium(storing_costs=storing_costs, beta=beta, u=u) print("Expected equilibrium is: {}".format(expected_equilibrium)) e = Economy(**parameters) backup = e.run() represent_results(backup=backup, parameters=parameters)
def main(): parameters = { "t_max": 500, "u": 100, "b11": 0.025, "b12": 0.025, "b21": 0.25, "b22": 0.25, "initial_strength": 0, "repartition_of_roles": np.array([50, 50, 50]), "storing_costs": np.array([0.1, 1., 20.]), } e = MarimonEconomy(**parameters) backup = e.run() parameters["agent_parameters"] = { "u": parameters["u"], "b11": parameters["b11"], "b12": parameters["b12"], "b21": parameters["b21"], "b22": parameters["b22"], "initial_strength": parameters["initial_strength"] } parameters["agent_model"] = type("", (object, ), {"name": "Marimon"})() represent_results(backup=backup, parameters=parameters)
def main(): parameters = { "t_max": 500, "agent_parameters": {"alpha_plus": 0.4, "alpha_minus": 0.05, "temp": 0.01}, "role_repartition": np.array([500, 500, 500]), "storing_costs": np.array([0.1, 0.25, 0.4]), "agent_model": RL2Agent, } backup = \ launch( **parameters ) represent_results(backup=backup, parameters=parameters)
def main(): parameters = { "t_max": 500, "agent_parameters": {"beta": 0.9, "u": 0.2}, "repartition_of_roles": np.array([500, 500, 500]), "storing_costs": np.array([0.01, 0.04, 0.09]), "agent_model": StupidAgent, } backup = \ launch( **parameters ) represent_results(backup=backup, parameters=parameters)
def main(): parameters = { "t_max": 500, "beta": 0.9, "u": 100, "repartition_of_roles": [500, 500, 500], "storing_costs": [1, 4, 9], "agent_model": DuffyAgent, } backup = \ launch( **parameters ) represent_results(backup=backup, parameters=parameters)
def main(): parameters = { "t_max": 500, "agent_parameters": { "u": 500, "b11": 0.025, "b12": 0.025, "b21": 0.25, "b22": 0.25, "initial_strength": 0 }, "repartition_of_roles": np.array([50, 50, 50]), "storing_costs": np.array([0.1, 1., 20.]), "agent_model": MarimonAgent, } backup = \ launch( **parameters ) represent_results(backup=backup, parameters=parameters)