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Emotion Engineering: A Scientific Approach For Understanding Game Appeal
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Emotion Engineering: A Scientific Approach For Understanding Game Appeal

July 29, 2008 Article Start Previous Page 2 of 6 Next

In The Chemistry Of Game Design, Daniel Cook outlines the benefits game designers would draw from a standardized quasi-scientific descriptive model. Such a model would help game rules design, iterative design, experience design, and even game testing, thus reducing the cost and risk of game design.

If we extend his metaphor to biochemistry, we - like Watson and Crick - aim to unlock the secrets of the DNA of game design, but we're still struggling to become Mendels. Gregor Mendel was a 19th century monk who is known today as the father of modern genetics. Around 1860, he spent seven years experimenting with pea strain hybridization in his monastery's garden. His observations, combined with some amazing insights, led him to the discovery of the characters of heredity.

I think that if there are no widely accepted grand theories of game design, it's because Watson and Crick's discovery was built on Mendel's, and we're still lacking such a base. Mendel's success stems from the tedious repetition of a loop familiar to game developers: tweak some parameters, wait, observe, and measure.

But Mendel had an advantage over us: being a botanist, he had a fairly good idea of what to observe and measure: colors, shapes, textures, size, growth rates, etc. (Furthermore, he didn't care if his peas looked or tasted great, while game designers are trying to understand what make games good while making good games.)

If Science = Measures + Insight, what should we measure in our games to move toward a scientific understanding of game design, and how can we equate these measures with quality?

Measurement of game assets and gameplay is nothing new (be it Ben Cousins' systematic studies within a genre or Microsoft's usability labs), but I'm looking for abstract game variables that could measure any game in any genre.

What is a Good Game?

If we could agree on what a good game is, the description would be a starting point for finding the gameplay variables that, like botanic for Mendel, would help us objectively measure game characteristics. Unfortunately, there are as many definitions of what a game is that you care to look for. There are fewer of what a good game is but it's still staggering. So I'll just pick one I like:

"A [good] game is a series of interesting choices" - Sid Meier

In my experience, this koan sticks to the memory of every designer who hears it, so there may be some truth in there. Let's parse it.

Choices imply that the player has a certain degree of freedom.

Noah Falstein (referenced here) professes that one can map the availability of choices during a given challenge to a convexity. A typical challenge starts with few choices since the starting conditions are set. As consequences from the first choices materialize, the sub-space of the attainable game space grows, thus increasing the number of available choices.

When success or failure conditions are met, the number of choices decreases until the challenge is completed and no choices are left. This is for instance how the game can lead the player toward a climactic ending. What is interesting in this description is that it shows that choices are something we can count.

Convexity of available choices during a challenge

Falstein goes on to note the fractal nature of convexities in a game. Long-term goals can be attained by choosing between options of medium-term missions, themselves composed of short-term challenges.

Sequences of fractal convexities in a game

This means that freedom is not one of the variables we're looking for, but more probably a defining characteristic for a series of variables.

Since more freedom or more choices is not always better, we shouldn't be looking for ways of maximizing the values of our game variables, but for ways of attaining the ranges in which they procure the experience we want to design.

Next, a choice is only real if it is informed, meaningful and irreversible.

Informed choice: To be able to make a choice, the player must be provided with a system of rules which logic he can understand and that he can trust to be consistent. Otherwise, his choice is random since he cannot predict its consequences.

This is how, for instance, he can choose which unit or building to produce in a RTS.

Meaningful choice: The player must have sufficient data to describe the context of his choice, the objects of his desire, his options, and the costs associated which each of them. If there are no costs, it's not really a choice since each option can be tried in turn. If there are neither costs nor contexts, choices don't matter. Context can be simple (Placing a block in Tetris) to extremely complex (Final Fantasy X's sphere grid experience system). Cost can vary from small (Buying a potion when the player has plenty of gold) to big (Choosing one's character class in a MMOG, limiting the content one can experience).

Irreversible choice: A choice, to be truly significant, must create a set of conditions that have a high degree of persistence. Otherwise, this means that the cost paid is meaningless.

For instance, a player can change his mind after committing to a Zerg rush strategy in StarCraft, but it will cost him time, require some effort, and impede his chances of winning.

Incidentally, this means that some game variable changes can be temporary - those that are the consequences of actions that are not considered choices, like most actions performed by the game's systems.

Article Start Previous Page 2 of 6 Next

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