Can I add customized arguments to an agent's "step" method?

[philip928lin]

Hi,
Can I add customized arguments to an agent's "step" method?
I tried it with the following code, in which I add an "a" argument to the step method.
What do you think the best way is to address a need like this?
Simply assign new data by updating agents' attributes before running "step()"?

Thank you very much.

`import mesa
class MoneyAgent(mesa.Agent):
"""An agent with fixed initial wealth."""

def __init__(self, unique_id, model):
    # Pass the parameters to the parent class.
    super().__init__(unique_id, model)

    # Create the agent's attribute and set the initial values.
    self.wealth = 1
    self.d = {"sds": {}}

def step(self, a):
    # The agent's step will go here.
    # For demonstration purposes we will print the agent's unique_id
    print(f"Hi, I am an agent, you can call me {str(self.unique_id)}. {a}")

def compute_gini(model):
agent_wealths = [agent.wealth for agent in model.schedule.agents]
x = sorted(agent_wealths)
N = model.num_agents
B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
return 1 + (1 / N) - 2 * B

class MoneyModel(mesa.Model):
"""A model with some number of agents."""

def __init__(self, N):
    self.num_agents = N
    # Create scheduler and assign it to the model
    self.schedule = mesa.time.RandomActivation(self)

    # Create agents
    for i in range(self.num_agents):
        a = MoneyAgent(i, self)
        # Add the agent to the scheduler
        self.schedule.add(a)

    self.datacollector = mesa.DataCollector(
        model_reporters={"Gini": compute_gini},
        agent_reporters={"Wealth": "wealth",
                         "d": "d"}
    )

def step(self):
    """Advance the model by one step."""

    # The model's step will go here for now this will call the step method of each agent and print the agent's unique_id
    self.datacollector.collect(self)
    self.schedule.step(a="Philip")

model = MoneyModel(10)
for i in range(100):
model.step()`

[rht]

From the snippet, it seems to me that a's value doesn't vary across agents. As such, specifying it as a model attribute that is read by the agents should be sufficient. The agents can access the value via self.model.a.