Random Values


Procedural generation systems often employ random values as their main source of variety. Understanding how to generate, bias, map, and control random values is key to controlling the aesthetics of these systems.


p5.js + p5.dom

Random Values

We use the word random to mean an hodgepodge of related ideas. Depending on context we might mean unplanned, unexpected, unpatterned, uncontrolled, or unpredictable. Random values are a staple of procedural generation systems, often used as the main source of variety.

Artists began working with randomness and chance long before the invention of computers. Then, as now, artists had to decide what aspects to control within a piece, and what aspects to leave to chance.

The 18th-century composition Instructions for the composition of as many waltzes as one desires with two dice, without understanding anything about music or composition, which may have been written by Mozart, uses chance to select and sequence pre-composed musical phrases. By placing rules on selection and ordering of the phrases, the system ensures that each variation makes musical sense.

Consider the works below:

  • Which aspects of each work were influenced by chance?
  • Which aspects were controlled by the artist?

The Methodical Application of Chance

Using random values in your procedural system doesn’t mean your results must be haphazard, uncontrolled, or unorganized. While individual random values are unpredictable, you can use these values to create a coherent cumulative effect.

2d6 vs 1d12 Chart

Compare the outcomes of rolling 2 6-sided dice to rolling 1 12-sided die.

Chart One

Roll two six-sided dice 50 times.
Plot the sums.

Chart Two

Roll a twelve-sided die 50 times.
Plot the values.


Generating Random Numbers

Plain Javascript provides Math.random() to generate a random number.

The Math.random() function returns a floating-point, pseudo-random number in the range [0, 1); that is, from 0 (inclusive) up to but not including 1 (exclusive), which you can then scale to your desired range. The implementation selects the initial seed to the random number generation algorithm; it cannot be chosen or reset by the user.


Specifying a Range

Math.random() always gives you a value between 0 and 1. If you want a different range, you can scale and offset the value yourself:

random() * range + start;

// even distribution between [10 and 15)
random() * 5 + 10;

Random Integers

The Math.random() function returns floating point values, but sometimes you want integers. The Math.floor() function will round a number down, chopping off the decimal part.

// roll a standard die
Math.floor(Math.random() * 6) + 1;

Be careful when generating integers: it is easy to get results that are slightly off. Math.floor() rounds down to the nearest integer, so you need to have values that go above the highest integer you want returned.

Using Math.round() instead of Math.floor() can lead to unevenly distributed results. In the example below, 1 will get picked half as often as it should, and 7 will sometimes get picked though it should not.

// roll a standard die
// this won't quite work. why?
Math.floor(Math.random() * 7);

// this also doesn't quite work. why?
Math.round(Math.random() * 6) + 1;

p5 random()

Processing provides the random() function for generating random numbers. Without any parameters, random() works very much like Math.random() producing numbers between [0 and 1).

console.log("random()"); // random()
console.log(random()); // 0.45...
console.log(random()); // 0.12...
console.log(random()); // 0.37...

P5’s random() function accepts optional parameters to control the range of the number, so you don’t have to do it yourself.

console.log("random(10)");      // random(10) -> range [0, 10)
console.log(random(10));        // 4.89...
console.log(random(10));        // 1.20...
console.log(random(10));        // 6.99...

console.log("random(20, 30)");  // random(20, 30) -> range [20, 30)
console.log(random(20, 30));    // 21.96...
console.log(random(20, 30));    // 20.56...
console.log(random(20, 30));    // 22.36...

P5 provides floor() which you can use to generate random integers.

// roll a standard die
floor(random(0, 6)) + 1;
// or
floor(random(1, 7));
// this won't quite work. why?
floor(random(1, 6));

Biased Distribution

The examples above will produce results evenly distributed across their range.

Often even distribution isn’t what you really want. Often you want to bias the results towards the low end, high end, or middle. Simple averaging and the min() and max() functions can help with this.

Even Distribution

random(1, 11);

even distribution

Low Bias Distribution

Taking the lowest of two or more random numbers will bias the result toward the low end.

min(random(10), random(10));

even distribution

The more random numbers you use, the stronger the bias.

min(random(10), random(10), random(10), random(10));

even distribution

High Bias Distribution

Taking the highest of two or more random numbers will bias the result toward the high end.

max(random(10), random(10));

even distribution

Middle Bias Distribution

Averaging two or more random numbers will bias the result toward the middle.

(random(1, 11) + random(1, 11)) / 2;

even distribution

Normal Distribution

If you generate several random numbers and average them, the results get close to normal distribution. Normal distribution, or Gaussian distribution, is the “bell curve” distribution which is often found in natural systems.

(random(1, 11) + random(1, 11) + random(1, 11)) / 3;

even distribution

Note: P5 also provides the randomGaussian() function for generating numbers with a true normal distribution. With randomGaussian() the possible values are not clamped to a range; extreme outliers are just really rare.

More Info

Anydice Dice Calculator

Anydice: Three Basic Distributions

Redblob: Damage Rolls

Dice vs. Decks

When you roll a die, you get random values. You might get the same value more than once, and it might take a long time to get a particular value.

If you roll a normal die six times, it is unlikely—about a 1.5% chance—that you’ll get all six values without repeats. You have a pretty good chance—about 33%–of not rolling any 1s. You can be pretty sure—98.5% sure—that at least one number won’t have appeared after six rolls.

A deck of cards works differently. When you pull cards from a deck, you don’t get random values. You get values in a random order. You avoid duplicates, and you know you will have toured all the values when you reach the end of the deck.

Dice Visualizer

Deck Visualizer

Modeling a Deck with an Array

p5 provides the shuffle() function to randomly reorder an array. Using shuffle we can simulate shuffling a deck, pulling values from it, and reshuffling when we run out.

// create an array to hold the possible values
var values = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9];

// create a variable to hold the current position in the deck
var position = 0;

function setup() {
  // shuffle the deck first
  values = shuffle(values);

  // pull as many values as we need
  for (let i = 0; i < 50; i++) {

function valueFromDeck() {
  // find the value at the current position in the deck
  var v = values[position];

  // change the position for next time

  // if we run out of "cards", shuffle and start over from the top
  if (position == values.length) {
    values = shuffle(values);
    position = 0;

  // return the value
  return v;

Random Choices

If you want your code to make a decision at random, you can combine random() with a conditional—if—statement.

// do something only half the time
if (random() < 0.5) {
  console.log("Optional Thing");

// do something only 10% of the time
if (random() < 0.1) {
  console.log("Rare Thing");

// another way to do something only 10% of the time
if (random(100) < 10) {
  console.log("Rare Thing");

With else you can pick between two optional things to do.

// do one thing, or the other. even odds.
if (random() < 0.5) {
  console.log("Option A");
} else {
  console.log("Option B");

With else if you can pick between multiple optional things to do.

var r = random(100);

if (r < 20) {
  // r is < 20
  console.log("Rare Thing 1 (20%)");
} else if (r < 40) {
  // r is >= 20 and < 40
  console.log("Rare Thing 2 (20%)");
} else {
  // r is >= 40
  console.log("Normal Thing (60%)");

A common mistake when following this pattern is calling random multiple times. If you are making one choice between several options, you only want to call random() once.

Remember: 1 choice, 1 random()

// this code doesn't work as expected
if (random(100) < 20) {
  // Actually a 20% chance
  console.log("Rare Thing 1 (20%)");
} else if (random(100) < 40) {
  // Actually a 32% chance
  console.log("Rare Thing 2 (20%)");
} else {
  // Actually a 48% chance
  console.log("Normal Thing (60%)");

Skyline Tactic Match

Study the example city skylines below. Each skyline was made using a different tactic for picking random values.
Match each skyline to its corresponding tactic.


Pure Random, Low Bias, Normal Bias, High Bias, Deck








  • Which tactic is the “best”?
  • When would you choose to use different types of bias?
  • Where is each type of bias found in the world?

Pseudo-random vs. Random

Computers are deterministic systems. When a computer is in a particular state and performs a specific instruction, the resulting state will always be the same. The results are never random.

So how can random() produce a random value? Technically, it can’t. It can produce values that appear to be random, called pseudorandom values. Pseudorandom values appear random—unless you look very closely—but are created by a deterministic process.

A common method to create pseudorandom values is a Linear Congruential Generator.

LCGs begin with an initial value called the seed, then use multiplication, addition, and modulus (remainder after division) to derive a new, seemingly random value. Below is a very basic implementation of an LCG so you can see how they work.

For our purposes, it is not important to understand exactly how the generator works.

It is important to understand that the sequence of random() numbers is perfectly predictable, if you know the seed.

Setting the Random Seed

P5 provides randomSeed() to set the seed used by random(). Once you have set the seed, the sequence of values produced by random will always be the same.

By setting a seed you can use random values in your code, but get the same results each time you run your program. This can be a useful feature in many programs and can help with debugging problems.

Be careful when relying on the seed to get random but repeatable results. There are at least two common ways for things to get messed up.

  • First, if your program accepts user input, and that input can influence how many times random() is called, your program can get off sequence.

  • Second, if you change your program and add or remove a call to random(), you will alter the sequence.

Javascript does not provide any way for you to set the seed used by Math.random(), so if you need to set the seed and are not using p5, you’ll need to find and use another Javascript library for generating random numbers.

Pencil + Paper LCG

Explore how an Linear Congruential Generator works by generating pseudo-random values by hand.


  1. Enter your assigned seed in the top box.
  2. Follow the arrows, perform the indicated operations, and put each result in the following box.


Study Examples

The following study examples demonstrate different ways to bias and map random values to get different looks and effects. Carefully study each example to understand how it works. Several of the examples offer varied approaches which can be commented in and out to compare their results.


Small Multiples


Brownian Motion

Wikipedia: Brownian Motion


In-class Challenge

Explore the study examples above by completing the following challenges in order.
Don’t skip any.

Time Comment
< 6 in 20 Minutes You need to put in some extra work to strengthen your understanding of these topics.
6 in 20 Minutes Good.
All 10 in 20 Minutes Great.

Modify the Small Multiples Example

  1. Change the background color to dark grey.
  2. Change the circle color to white.
  3. Draw each circle filled with a randomly-chosen color.

Modify the Grass Example

  1. Make the grass taller.
  2. Make the grass green.
  3. Make the grass messier.

Challenging Challenges

  1. Modify the small multiples example so that each row is a randomly-chosen color.
  2. Modify the small multiples example so that each column is a randomly-chosen color.
  3. Modify the grass example so that each blade of grass is a different, randomly-chosen shade of green.
  4. Modify the grass example so that the short grass is darker.



Experiment with procedurally generating images using random(). Explore each of the tactics discussed above. Post your results to the Sketchblog.

Individual Challenge: Master Study

Kasimir Malevich, Mark Rothko, Piet Modrian, and Anni Albers all worked with basic shapes, color, and natural media. Create a sketch that generates new works in the style of one of these artists. Pay particular attention to the subtleties and textures of your chosen artist’s work. How closely can you recreate these subtleties?

Pair Challenge: Deck

Working with a partner from the class, create a sketch that generates random images. All randomly chosen values should use the deck tactic discussed above. Work together in the same place and time. Both you and your partner should post output from your sketch.

Mozart’s Musikalisches Würfelspiel
Essay on Mozart’s 1787 musical dice game. This webpage is from 1995!
Wikipedia: Musikalisches Würfelspiel
More info on musical dice games.
Chance vs. Randomness
Stanford Encyclopedia of Philosophy on the subtle distinction between chance and randomness.
A true random number generator that uses atmospheric noise as a source of randomness.
LavaRand by Cloudflare
Cloudflare uses lava lamps as a source of randomness for encryption.