Model

The model class for Mesa framework.

Core Objects: Model

class Model(*args: Any, seed: float | None = None, rng: Generator | BitGenerator | int | integer | Sequence[int] | SeedSequence | None = None, **kwargs: Any)

Base class for models in the Mesa ABM library.

This class serves as a foundational structure for creating agent-based models. It includes the basic attributes and methods necessary for initializing and running a simulation model.

running

A boolean indicating if the model should continue running.

steps

the number of times model.step() has been called.

random

a seeded python.random number generator.

rng

a seeded numpy.random.Generator

Notes

Model.agents returns the AgentSet containing all agents registered with the model. Changing the content of the AgentSet directly can result in strange behavior. If you want change the composition of this AgentSet, ensure you operate on a copy.

Create a new model.

Overload this method with the actual code to initialize the model. Always start with super().__init__() to initialize the model object properly.

Parameters:

• args – arguments to pass onto super

• seed – the seed for the random number generator

• rng – Pseudorandom number generator state. When rng is None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator are passed to numpy.random.default_rng to instantiate a Generator.

• kwargs – keyword arguments to pass onto super

Notes

you have to pass either seed or rng, but not both.

property agents: AgentSet

Provides an AgentSet of all agents in the model, combining agents from all types.

property agent_types: list[type]

Return a list of all unique agent types registered with the model.

property agents_by_type: dict[type[Agent], AgentSet]

A dictionary where the keys are agent types and the values are the corresponding AgentSets.

register_agent(agent)

Register the agent with the model.

Parameters:

agent – The agent to register.

Notes

This method is called automatically by Agent.__init__, so there is no need to use this if you are subclassing Agent and calling its super in the __init__ method.

deregister_agent(agent)

Deregister the agent with the model.

Parameters:

agent – The agent to deregister.

Notes

This method is called automatically by Agent.remove

run_model() → None

Run the model until the end condition is reached.

Overload as needed.

step() → None

A single step. Fill in here.

reset_randomizer(seed: int | None = None) → None

Reset the model random number generator.

Parameters:

seed – A new seed for the RNG; if None, reset using the current seed

reset_rng(rng: Generator | BitGenerator | int | integer | Sequence[int] | SeedSequence | None = None) → None

Reset the model random number generator.

Parameters:

rng – A new seed for the RNG; if None, reset using the current seed

remove_all_agents()

Remove all agents from the model.

Notes

This method calls agent.remove for all agents in the model. If you need to remove agents from e.g., a SingleGrid, you can either explicitly implement your own agent.remove method or clean this up near where you are calling this method.