Getting started#

Mesa is a modular framework for building, analyzing and visualizing agent-based models.

Agent-based models are computer simulations involving multiple entities (the agents) acting and interacting with one another based on their programmed behavior. Agents can be used to represent living cells, animals, individual humans, even entire organizations or abstract entities. Sometimes, we may have an understanding of how the individual components of a system behave, and want to see what system-level behaviors and effects emerge from their interaction. Other times, we may have a good idea of how the system overall behaves, and want to figure out what individual behaviors explain it. Or we may want to see how to get agents to cooperate or compete most effectively. Or we may just want to build a cool toy with colorful little dots moving around.

Tutorials#

If you want to get a quick start on how to build agent based models with MESA, check the tutorials:

Examples#

Mesa ships with a collection of example models. These are classic ABMs, so if you are familiar with ABMs and want to get a quick sense of how MESA works, these examples are great place to start. You can find them here.

Overview of the MESA library#

Mesa is modular, meaning that its modeling, analysis and visualization components are kept separate but intended to work together. The modules are grouped into three categories:

  1. Modeling: Classes used to build the models themselves: a model and agent classes, space for them to move around in, and built-in functionality for managing agents.

  2. Analysis: Tools to collect data generated from your model, or to run it multiple times with different parameter values.

  3. Visualization: Classes to create and launch an interactive model visualization, using a browser-based interface.

Modeling modules#

Most models consist of one class to represent the model itself and one or more classes for agents. Mesa provides built-in functionality for managing agents and their interactions. These are implemented in Mesa’s modeling modules:

The skeleton of a model might look like this:

import mesa

class MyAgent(mesa.Agent):
    def __init__(self, model, age):
        super().__init__(model)
        self.age = age

    def step(self):
        self.age += 1
        print(f"Agent {self.unique_id} now is {self.age} years old")
        # Whatever else the agent does when activated

class MyModel(mesa.Model):
    def __init__(self, n_agents):
        super().__init__()
        self.grid = mesa.space.MultiGrid(10, 10, torus=True)
        for _ in range(n_agents):
            initial_age = self.random.randint(0, 80)
            a = MyAgent(self, initial_age)
            coords = (self.random.randrange(0, 10), self.random.randrange(0, 10))
            self.grid.place_agent(a, coords)

    def step(self):
        self.agents.shuffle_do("step")

If you instantiate a model and run it for one step, like so:

model = MyModel(5)
model.step()

You should see agents 1-5, activated in random order. See the tutorial or API documentation for more detail on how to add model functionality.

To bootstrap a new model install mesa and run mesa startproject

AgentSet and model.agents#

Mesa 3.0 makes model.agents and the AgentSet class central in managing and activating agents.

model.agents#

model.agents is an AgentSet containing all agents in the model. It’s automatically updated when agents are added or removed:

# Get total number of agents
num_agents = len(model.agents)

# Iterate over all agents
for agent in model.agents:
    print(agent.unique_id)

AgentSet Functionality#

AgentSet offers several methods for efficient agent management:

  1. Selecting: Filter agents based on criteria.

    high_energy_agents = model.agents.select(lambda a: a.energy > 50)

  2. Shuffling and Sorting: Randomize or order agents.

    shuffled_agents = model.agents.shuffle()
sorted_agents = model.agents.sort(key="energy", ascending=False)

  3. Applying methods: Execute methods on all agents.

    model.agents.do("step")
model.agents.shuffle_do("move")  # Shuffle then apply method

  4. Aggregating: Compute aggregate values across agents.

    avg_energy = model.agents.agg("energy", func=np.mean)

  5. Grouping: Group agents by attributes.

    grouped_agents = model.agents.groupby("species")

for _, agent_group in grouped_agents:
   agent_group.shuffle_do()
species_counts = grouped_agents.count()
mean_age_by_group = grouped_agents.agg("age", np.mean)

model.agents can also be accessed within a model instance using self.agents.

These are just some examples of using the AgentSet, there are many more possibilities, see the AgentSet API docs.

Analysis modules#

If you’re using modeling for research, you’ll want a way to collect the data each model run generates. You’ll probably also want to run the model multiple times, to see how some output changes with different parameters. Data collection and batch running are implemented in the appropriately-named analysis modules:

You’d add a data collector to the model like this:

import mesa
import numpy as np

# ...

class MyModel(mesa.Model):
    def __init__(self, n_agents):
        super().__init__()
        # ... (model initialization code)
        self.datacollector = mesa.DataCollector(
            model_reporters={"mean_age": lambda m: m.agents.agg("age", np.mean)},
            agent_reporters={"age": "age"}
        )

    def step(self):
        self.agents.shuffle_do("step")
        self.datacollector.collect(self)

The data collector will collect the specified model- and agent-level data at each step of the model. After you’re done running it, you can extract the data as a pandas DataFrame:

model = MyModel(5)
for t in range(10):
    model.step()
model_df = model.datacollector.get_model_vars_dataframe()
agent_df = model.datacollector.get_agent_vars_dataframe()

To batch-run the model while varying, for example, the n_agents parameter, you’d use the batch_run function:

import mesa

parameters = {"n_agents": range(1, 6)}
results = mesa.batch_run(
    MyModel,
    parameters,
    iterations=5,
    max_steps=100,
    data_collection_period=1,
)

The results are returned as a list of dictionaries, which can be easily converted to a pandas DataFrame for further analysis.

Visualization#

Mesa now uses a new browser-based visualization system called SolaraViz. This allows for interactive, customizable visualizations of your models.

Note: SolaraViz is experimental and still in active development for Mesa 3.0. While we attempt to minimize them, there might be API breaking changes between Mesa 3.0 and 3.1. There won’t be breaking changes between Mesa 3.0.x patch releases.

The core functionality for building your own visualizations resides in the mesa.visualization namespace

Here’s a basic example of how to set up a visualization:

from mesa.visualization import SolaraViz, make_space_component, make_plot_component


def agent_portrayal(agent):
    return {"color": "blue", "size": 50}


model_params = {
    "N": {
        "type": "SliderInt",
        "value": 50,
        "label": "Number of agents:",
        "min": 10,
        "max": 100,
        "step": 1,
   }
}

page = SolaraViz(
    MyModel,
    [
        make_space_component(agent_portrayal),
        make_plot_component("mean_age")
    ],
    model_params=model_params
)
page

This will create an interactive visualization of your model, including:

  • A grid visualization of agents

  • A plot of a model metric over time

  • A slider to adjust the number of agents

You can also create custom visualization components using Matplotlib. For more advanced usage and customization options, please refer to the visualization tutorial.

Further resources#

To further explore Mesa and its features, we have the following resources available:

Best practices#

API documentation#

Repository of models built using MESA#

Migration guide#

  • Mesa 3.0 Migration guide: If you’re upgrading from an earlier version of Mesa, this guide will help you navigate the changes in Mesa 3.0.

Source Ccode and development#

  • Mesa GitHub repository: Access the full source code of Mesa, contribute to its development, or report issues.

  • Mesa release notes: View the detailed changelog of Mesa, including all past releases and their features.

Community and support#

Enjoy modelling with Mesa, and feel free to reach out!