Week 11 – Zombie Automata by Dachi

Sketch:

Inspiration

To begin, I followed existing coding tutorials by The Coding Train on cellular automata to understand the basics and gather ideas for implementation. While working on the project, I drew inspiration from my high school IB Math Internal Assessment, where I explored the Susceptible-Infected-Recovered (SIR) model of disease spread (well technically I did SZR model). The concepts I learned there seemed to work well for current task.

Additionally, being a fan of zombie-themed shows and series, I thought that modeling a zombie outbreak would add an engaging narrative to the project. Combining these elements, I designed a simulation that not only explored cellular automata but also offered a creative and interactive way to visualize infection dynamics.

Process

The development process started with studying cellular automata and experimenting with simple rulesets to understand how basic principles could lead to complex behavior. After following coding tutorials to build a foundational understanding, I modified and expanded on these ideas to create a zombie outbreak simulation. The automata were structured to include four states, empty, human, zombie, and dead, each with defined transition rules.

I implemented the grid and the rules governing state transitions. I experimented with parameters such as infection and recovery rates, as well as grid sizes and cell dimensions, to observe how these changes affected the visual patterns. To ensure interactivity, I developed a user interface with sliders and buttons, allowing users to adjust parameters and directly interact with the simulation in real time.

How It Works

The simulation is based on a grid where each cell represents a specific state:

Humans: Are susceptible to infection if neighboring zombies are present. The probability of infection is determined by the user-adjustable infection rate.
Zombies: Persist unless a recovery rate is enabled, which allows them to turn back into humans.
Dead Cells: Represent the aftermath of human-zombie interactions and remain static.
Empty Cells: Simply occupy space with no active behavior.

At the start of the simulation, a few cells are randomly assigned as zombies to initiate the outbreak, and users can also click on any cell to manually spawn zombies or toggle states between humans and zombies.

Users can interact with the simulation by toggling the state of cells (e.g., turning humans into zombies) or by adjusting sliders to modify parameters such as infection rate, recovery rate, and cell size. The real-time interactivity encourages exploration of how these factors influence the patterns and dynamics.

Code I’m Proud Of

A part of the project that I am particularly proud of is the implementation of probabilistic infection dynamics

if (state === HUMAN) {
  let neighbors = countNeighbors(i, j, ZOMBIE);
  if (neighbors > 0) {
    if (random() < 1 - pow(1 - infectionRate, neighbors)) {
      nextGrid[i][j] = ZOMBIE;
    } else {
      nextGrid[i][j] = HUMAN;
    }
  } else {
    nextGrid[i][j] = HUMAN;
  }
}

This code not only introduces a realistic element of risk-based infection but also produces visually interesting outcomes as the patterns evolve. Watching the outbreak spread dynamically based on these probabilities was quite fun.

Challenges

One of the main challenges was balancing the simulation’s performance and functionality. With many cells updating at each frame, the program occasionally slowed down, especially with smaller cell sizes. I also tried adding some features (cure) which I later removed due to lack of visual engagement (other structures might suite it better), of course such simulation in itself is oversimplification so you have to be mindful when adding parameters.

Reflection and Future Considerations

This project was a good opportunity to deepen my understanding of cellular automata and their potential for creating dynamic patterns. The combination of technical programming and creative design made the process both educational and enjoyable. I’m particularly pleased with how the interactivity turned the simulation into a fun engaging experience.

Looking ahead, I would like to enhance the simulation by introducing additional rulesets or elements, such as safe zones or zombie types with varying behaviors. Adding a graph to track population changes over time would also provide users with a clearer understanding of the dynamics at play. These improvements would further expand the educational and aesthetic appeal of the project. Furthermore, I could switch from grid cell to other structures similar to real life scenarios.