Tutorial agent based modelling in python

agent-based modeling (abm) is a computational modeling approach that simulates the interactions of autonomous agents to assess their effects on the system as a whole. this tutorial will guide you through the basics of agent-based modeling using python, specifically using the `mesa` library, which is a popular framework for abm in python.

### tutorial: agent-based modeling with mesa in python

#### step 1: install mesa

first, ensure that you have mesa installed. you can install it via pip:

#### step 2: create a simple model

in this example, we will create a simple model of a population of agents that move randomly on a grid. each agent will have a state (e.g., healthy or infected) and will change state based on interactions with other agents.

#### step 3: set up the model structure

here's how to structure your abm using mesa:

1. **define the agent**: create a class for your agents.
2. **define the model**: create a class for your model, which contains the environment and agents.
3. **define the scheduler**: manage when each agent updates.
4. **define the data collector**: track data for analysis and visualization.
5. **run the model**: set up a way to run and visualize your model.

### example code

below is a complete example of a simple agent-based model using the mesa framework.

### explanation of code components

1. **person class**: this class represents the agents in our model. each agent can move to a random position and may infect neighboring agents.

2. **infectionmodel class**: the model class initializes the grid and agents. it uses a `randomactivation` scheduler to determine the order of agent updates and a `datacollector` to keep track of the number of infected agents.

3. **step method**: the `step` method in the model calls `collect` to gather data and executes each agent’s `step` method.

4. **running the model**: the model is instantiated and run for a specified number of steps. after the simulation, the data is collected and printed.

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