One good example of not-so-random randomness is in Tetris, where the blocks aren't generated in a truly random manner. Typically, a "bag of pieces" (with the size of the bag varying on the version, from 7 (one of each), to more (so there are multiple copies of each piece in the bag)) is used, and the bag is randomly shuffled and then drawn from until depletion, then "refilled", shuffled again, etc. This reduces the risk of generating multiple S and Z shapes in a row (which would ruin your day). So it's random, but it's randomness is limited.

I wonder, if you apply physics to a 3d die that has bullet physics applied to it and toss it up and let it fall, wouldn't it land on a random face?

I have an inkling that no one motion is perfectly like any other motion, so that every motion is always random. Are computerized physics "Perfect world" physics if it uses real world data for it's calculations?

Yet, at the end of the day it is true that as far as computer programming goes, you can't make anything 100% random unless you can toss a die in the air that has an infinite amount of sides.

I don't know of one computer that can generate a random number between negative infinity and positive infinity.

Don't know of one that can calculate 1003!

I tried to create an automatic animation system (where a 3d model can animate itself), and I came to the issue that if a 3d model were to animate itself, then it would have to be able to move in an infinite amount of ways while having a finite range of motion. I still think this is possible. Perhaps I can work on it again.

I wonder, if you apply physics to a 3d die that has bullet physics applied to it and toss it up and let it fall, wouldn't it land on a random face?

I have an inkling that no one motion is perfectly like any other motion, so that every motion is always random. Are computerized physics "Perfect world" physics if it uses real world data for it's calculations?

Yet, at the end of the day it is true that as far as computer programming goes, you can't make anything 100% random unless you can toss a die in the air that has an infinite amount of sides.

I don't know of one computer that can generate a random number between negative infinity and positive infinity.

Don't know of one that can calculate 1003!

I tried to create an automatic animation system (where a 3d model can animate itself), and I came to the issue that if a 3d model were to animate itself, then it would have to be able to move in an infinite amount of ways while having a finite range of motion. I still think this is possible. Perhaps I can work on it again.

You can determine quite precisely "when" a standard loot drop will occur, given it's rarity. You are only required to decide how often you want to be wrong. Let's say we wish to be right 19 times out of 20, so we'll be wrong 1 in 20.

Given a loot drop chance of 1/10 000, how many tries will it take to get that loot drop?

log(1/20) / log(9999/10000) =29956 tries

After approximately 30 000 tries, 19 out of 20 players will have that loot drop.

@AngleWyrm: You're right, but my article is about introducing modifiers to the probability of rolling a 1 (success) that can be programmed easily and present options to the underlying Game Design and/or balancing.

so we'll be wrong 1 in 20.

Rolling a simple 1d/20 will result in sequences like:

8, 12, 4, 19, 1, 1, 1, 1, 1, 1, 15, 15, (hundred times NOT 1), 1, ...

This means the player found six consecutive "rare items" near the beginning and after that no rare drop for 102 times.

That is perfectly random but not very well distributed and undesirable from the point of Game Design and balancing.

So we introduce modifiers.

To have some "space" to modify the numbers we make them bigger to start with.

In my example I roll 1d/10000.

Given a loot drop chance of 1/10 000, how many tries will it take to get that loot drop?

Did you understand from my protocode that the value 10000 get's decreased by 100 for every subsequent roll?

Until the player has found a rare drop and the value get's back to 10000.

The probability of success increases with every roll until success is practically granted.

Why use 100/10000 and not 1/1000?

To have another modifier. The value 100 can also be in- or decreased with game mechanics according to the Game Design.

If you simply roll 1d/20, how would you handle, for instance, a buff that gives 5% to finding rare items? Easy, just make it a 1d/19!

But what about if you want to give a player a higher probability incrementation, because it's "Lootfest Holiday Special"?

You could set the chance to 2/19, which might be a bit high for your overall Game Design. So, you set it to 3/38.

If you need more modifiers, pretty soon you manage variable values like there's no tomorrow. And we know from experience that that is where bugs occur...

Use a central function or method similar to the one I described in the article and you're all set :-)

I was wondering if using real world data would make things in a game more random. All we need is "close enough"

This is an interesting topic but I think we agree that modern random generators, properly seeded, are good for games.

This article is about introducing modifiers to influence the probability of the outcome of a "dice roll".

From the perspective of Game Design and balancing: Using random() is fine enough but the resulting sequences of numbers are not sufficient in many cases.

Rolling a simple 1d/20 will result in sequences like:

8, 12, 4, 19, 1, 1, 1, 1, 1, 1, 15, 15, (hundred times NOT 1), 1, ...

The point seems to be discussing what are called Runs, run lengths, and run frequencies. A random number generator produces runs with specific set of runs-up and runs-down lengths and frequencies, and the output can be tested to make sure that it conforms to mathematical definitions of randomness.

A good article on this topic is Non-Parametric Tests for Randomness by Ying Wang.

@carsten The encapsulation described in the article makes perfect sense. But I was wondering if you know of an open source framework for organizing this kind of system. I.e. something that helps organize the behavior of the random 'provider' subject to attributes or properties of the environment and character?

-Josh

@jjd: Actually, I think it wouldn't make much sense having some kind of framework for this and I don't know of any.

The implementation of this method is simple and relies heavily on how your code is structured, storing values etc., and more so on your game design, what values to use, how and when.

In my experience it takes much longer to think about what this method is to do within your game then to implement it :-)

Implementing the simple protocode I described in, say, Javascript shouldn't take more then a few minutes (sans testing).

Addendum: What framework or language are you looking for? I can search old backups for something that isn't owned by a company.

Yeah, I figured that was likely to be the case. The situation I was thinking about was that this kind of approach often starts out as a one-off solution because it is so straightforward to implement. However, I can see it easily leading to tight coupling between components of the system as more parameters from those components are used to affect the outcome.

As far as language goes and I not looking for anything in particular. Although these days I would be more inclined toward something in python or javascript.

Old 4X games used this model for chance of accomplishing research

(often you had to pay the research price once in full and by additional payment increased the chance of breakthrough every turn)