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)