# How to Set Up Simulations for Multi-Agent Systems Using NetLogo

> Learn to set up multi-agent system simulations using NetLogo. Define agent behaviors and orchestrate emergent dynamics with this guide. Perfect for AI beginners.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: how-to-guide
- Published: 2026-08-08

---

**You can set up multi-agent system simulations by installing NetLogo, defining agent behaviors with breeds and parallel commands, and orchestrating emergent dynamics through Setup/Go cycles that implement local interaction rules.**

Setting up simulations for multi-agent systems requires understanding how autonomous entities interact within a shared environment. The microsoft/AI-For-Beginners curriculum demonstrates this process using NetLogo, a specialized modeling environment designed specifically for multi-agent systems research and education.

## Core Concepts of Multi-Agent Systems

In [`/lessons/6-Other/23-MultiagentSystems/README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main//lessons/6-Other/23-MultiagentSystems/README.md) (lines 31-90), multi-agent systems (MAS) are built around three fundamental components:

- **Agents** (turtles): The active entities that move and interact
- **Patches**: The spatial grid cells that constitute the environment  
- **Observer**: The global controller that orchestrates world initialization and simulation flow

These elements work together to create emergent behaviors from simple local rules executed by individual agents.

## Step-by-Step Simulation Setup

### Install NetLogo

Download the latest release from the official NetLogo website and unzip the archive to your local machine. The installation process is documented in [`/lessons/6-Other/23-MultiagentSystems/README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main//lessons/6-Other/23-MultiagentSystems/README.md) (lines 48-50).

### Explore the Models Library

Launch NetLogo and navigate to **File → Models Library** to examine pre-built reference implementations. The library contains canonical examples including *Flocking*, *Traffic*, and *Fireworks* that demonstrate common multi-agent interaction patterns (lines 52-60).

### Initialize the Interface

Select **File → New** to create a blank simulation. The NetLogo workspace is divided into two primary tabs: the **Interface** tab (containing buttons, sliders, and visual displays) and the **Code** tab where you define agent behaviors and world dynamics (lines 68-75).

## Defining Agent Types and Behaviors

### Creating Agents and Breeds

In [`/lessons/6-Other/23-MultiagentSystems/README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main//lessons/6-Other/23-MultiagentSystems/README.md) (lines 35-44), agents are created using `create-turtles` or custom breeds defined with the `breed` keyword. To establish a distinct agent type:

```netlogo
breed [cats cat]
create-cats 5 [
  set color orange
  set size 2
]

```

This declaration creates a "cats" breed with singular form "cat", allowing breed-specific commands like `ask cats [...]`.

### Parallel Execution with Ask Commands

Commands wrapped in `ask turtles [...]` execute **in parallel** for every agent, enabling simultaneous updates that produce system-wide emergent behavior. As implemented in the source (lines 86-90), this parallelism ensures all agents react to the same world state during each simulation tick.

```netlogo
create-turtles 10
ask turtles [
  forward 10
]

```

## Building the Simulation Control Loop

### Setup and Go Procedures

The standard multi-agent system interface requires two essential control buttons defined in [`/lessons/6-Other/23-MultiagentSystems/README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main//lessons/6-Other/23-MultiagentSystems/README.md) (lines 76-82):

- **Setup**: A procedure that initializes the world, clears existing agents, and creates initial populations
- **Go**: A procedure that advances the simulation, typically called repeatedly via a button or forever-loop

These procedures form the control cycle that drives the simulation forward tick-by-tick.

### Implementing Core Interaction Rules

For complex emergent behaviors like flocking, implement three fundamental rules that each agent follows based on nearby neighbors (lines 94-99):

- **Alignment**: Match the average heading of local agents
- **Cohesion**: Move toward the center of nearby agents
- **Separation**: Maintain minimum distance from others

The alignment rule can be implemented as:

```netlogo
to align
  ask turtles [
    let avg-heading mean [heading] of turtles in-cone 5 180
    set heading avg-heading
  ]
end

```

This code calculates the average heading of all turtles within a 5-unit, 180-degree cone and updates each agent's direction accordingly.

## Running and Parameterizing Simulations

Once implemented, modify parameters such as view-range, separation distance, and agent count through Interface tab sliders. As noted in the curriculum (lines 100-103), adjusting these variables allows you to observe how simple local rules give rise to complex global dynamics like swarming, traffic jams, or pattern formation.

## Summary

- Multi-agent systems require three core components: agents (turtles), patches (spatial grid), and an observer to orchestrate the world state.
- Install NetLogo and explore the Models Library to understand canonical patterns documented in [`/lessons/6-Other/23-MultiagentSystems/README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main//lessons/6-Other/23-MultiagentSystems/README.md).
- Define agent types using `breed` declarations and `create-turtles` commands that execute in parallel via `ask` blocks.
- Implement the standard Setup/Go cycle to initialize and advance your simulation tick-by-tick.
- Program local interaction rules (Alignment, Cohesion, Separation) to generate emergent global behaviors from simple agent-level instructions.

## Frequently Asked Questions

### What is NetLogo and why is it used for multi-agent simulations?

NetLogo is a specialized modeling environment designed specifically for multi-agent systems research. It provides built-in support for parallel agent execution, spatial grids (patches), and visualization tools that make it ideal for simulating emergent behaviors in complex systems without requiring low-level concurrent programming.

### How do you create custom agent types in NetLogo?

Use the `breed` keyword at the top of your Code tab to define custom agent categories. For example, `breed [cats cat]` creates a "cats" breed with singular form "cat", allowing you to write breed-specific code like `create-cats 10` or `ask cats [forward 1]` to control distinct populations with unique properties and behaviors.

### What is the difference between the Setup and Go procedures?

The **Setup** procedure runs once when clicked, clearing the world (`clear-all`), initializing agent positions, and setting global variables. The **Go** procedure runs continuously (or once per tick) to update agent states, typically containing a `tick` command to advance the simulation clock and often calling itself recursively or via a `forever` button flag.

### How does parallel execution work in NetLogo agent commands?

When you use `ask turtles [...]`, NetLogo executes the code block for all agents simultaneously during the same simulation tick rather than sequentially. This parallelism is crucial for multi-agent simulations because it ensures all agents react to the same world state, preventing update-order bias and enabling true emergent behavior from concurrent local interactions.