Game Theory: Types of Randomness

This post is a sequel of sorts to the previous discussion of luck mitigation. Read that first if you haven’t, because I use some of the concepts here. The methods in the other post are great if you and want to make sure that the random elements in your game are balanced. But what if you want to add some random factors? This is an overview of some of the major classes of randomness that can be used in game design.

Random initial conditions. In games with random setups, if the components are not balanced, a player who starts with a better arrangement can have a real advantage. Giving players a way to affect this (such as choosing starting locations, or drafting for starting abilities and resources) can help overcome this.

Fixed random events. “Event” is used loosely as really anything that changes throughout the game. Card games are the perfect example, where each card is its own “event”, and the card order is set when it the deck is shuffled. But you don’t have a way of knowing what the order of cards is. An uneven distribution can give a player all the good or bad cards.

Variable random events. This is very similar to fixed random events, but you can actually affect the probability of events by adding or removing things from the pool. This can be done with cards if you reshuffle each time you add or remove something from the deck, but it is a lot easier to do with components from a bag. Tile laying games like Carcassonne are a good example. There are numerous ways to affect this, such as letting players choose multiples at a time like Compounded, or see a few spaces ahead in the order like Ticket to Ride. Keeping the information hidden (like Keyflower) mostly sidesteps the problem by making the random factors into small bits of information rather than the main subject of the game.

Random outcomes. Dice and coin flips are the most common way to achieve this. Players can use outcome probability to help evaluate actions. This is most easily addressed by increasing the number of samples so that the outcome distribution approaches the probability, but almost any luck mitigation approach will probably work on this.

Random inputs. This is slightly different from a random outcome, because the random information is an input to the decision process. An especially good or bad distribution of inputs can really drive a game, so increasing the number of inputs and giving players ways to change the inputs can be used to affect this. (rolling dice and choosing how to use them)

External randomness. For this I am thinking of dexterity games, in which random external conditions to the game (flatness, component condition, and environment effects) can’t be evaluated. This is similar to the random outcome, but the random features are not part of the game element; similar luck mitigation approaches will affect this.

Random Randomness. Err… what? This is sort of my catch-all, and includes things like unknown game length, and luck arising from other players’ independent actions. Sometimes, you just can’t know how random effects will impact a play-through. Sometimes changing the game length is beneficial, but you usually won’t know until halfway through whether a longer or shorter will be beneficial, or which way it will go. And with larger groups, you sometimes can’t predict every player’s actions, so the other players effectively act as a random factor in ways that can’t be fully quantified. And so the only way to really mitigate this type of luck is to give the player more different ways mitigate luck, so the player can use the one that is most effective.

These random factors are all ways that the balance of the game can be thrown off, and finding a happy medium between them will keep your players happy, too.

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  1. #1 by Dr. Wictz on February 21, 2014 - 3:44 pm

    How would you define player driven results that people find hard to predict?

    • #2 by Oakleaf Games on February 21, 2014 - 4:08 pm

      Probably in the Random Randomness. It’s not truly random, but complex enough that it can’t be predicted or affected directly. Maybe that entire category is a cop-out. Someone could probably write an entire article on using complex, hard-to-predict, but not truly random mechanics. Hmm… that sounds a lot like what economists do, in fact.

      • #3 by Dr. Wictz on February 21, 2014 - 4:22 pm

        Question becomes do you want a theoretical economists or an empirical economist?

        If the theoretical economist is correct on their assumptions then we might actually know what is going on and why. Although, assuming a theorists has the correct assumption is a giant leap of faith.

        The empirical economist might be able to narrow down what is happening. But until a good theory is put together, all they can do is eliminate what might be taking place.

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