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When Players Make the Rules: On Memes and the Meta-Game
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When Players Make the Rules: On Memes and the Meta-Game

November 15, 2012 Article Start Page 1 of 3 Next

Are players pushing your game in directions you never intended it to go? In this article, Gamasutra explores how games like StarCraft II and Call of Duty: Modern Warfare encourage good and bad player behaviors.

An interesting thing about games is that the player always helps design them. No matter how simple or complex the game is, there is always room for our own creative input. We add new rules, new contexts, new narratives and new measures of success, and we choose which of the original characteristics of the game we want to interact with. Games are much like books in this manner, and we will often find the most interesting things about games between the lines of the author's instructions.

When designing games, this is both a blessing and curse. How the player behaves within the context of the game has an enormous impact on how enjoyable the game will be for the player, and game designers often find themselves struggling with how to encourage the players to play in a way that will be rewarding. Managing the expectations and behaviors of the player is a daunting task, but one of tremendous importance. Games that are well developed in every sense can still fall short to an unhealthy in-game culture. The game is only as good as the players.

So how does one manage, or even anticipate, how players might behave within the game? To understand how, we will first have to gain a rudimentary understanding of behavior itself.

Behavior is the way someone acts in response to a particular situation or stimulus. It's important here that we don't confuse behavior, which is a model for describing someone's actions, with the actions themselves.

"I am going to sleep" is a good example of what an action might be, and "I will go to sleep after this TV show, even if I'm tired now" shows us what a behavior would be. Whereas an action could be described as a data point, behavior is a graph attempting to make sense of the data. If we have a good model for someone's behavior, we can extrapolate, and derive, and experiment.

Some behaviors are particularly successful at achieving things that are good for us, while other behaviors can be wasteful, detrimental, and destructive to us. This helps us rationally choose some of our behaviors, and avoid others. We brush our teeth with toothpaste to keep our teeth white, but only very few will make the leap and attempt to brush their teeth with bleach.

But we aren't that good at avoiding destructive behavior. We routinely engage in behaviors that are bad for us, even when we are very well aware of the negative effects the behavior might have. Consider things like smoking, gambling, unprotected sex, and speeding. How can we explain these irrational behaviors?

The key lies in understanding that the roles we ourselves play in determining what behaviors are prevalent in our culture are fairly limited. Behaviors and ideas seem to have a life of their own.

The famous evolutionary biologist Richard Dawkins coined the term "meme" in his book The Selfish Gene, in an attempt to better explain what generates culture. As a game enthusiast, you may remember that Huizinga thought that our desire to play was what generates culture, but that is not a complete model. Dawkins found a way to explain how the things that play generated got to be so popular -- how they could move from isolated behaviors into the realm of culture.

A meme is a chunk of behavioral code -- a behavioral gene -- that can get copied from one individual to another. Memes are the building blocks of behavior. The words and gestures we use, the phrases we choose, the way we fold our laundry, the way we get our hair cut, these are all memes, and they are ideas that can be observed, copied, and mutated. 

In The Selfish Gene, Dawkins explains to us how natural selection acts on the genes, rather than the individuals. This gene-centric view of evolution has helped shed a lot of light on some of the more peculiar aspects of biology. The theory shows us that certain genes are more successful at being reproduced than others. The more successful genes will outperform the less successful genes and, with time, we will see more of the successful genes than we will of the less successful genes.

This simple process generates organisms that are very well adapted to the environment they live in. The gene-centric view of evolution has helped explain things like diseases and cancer, and is now a more useful model than Charles Darwin's own model of evolution. The individual is a machine built by and for genes, with the sole purpose of replicating genes.

Although they obviously do not exist in any physical sense of the word, imagining that same process of natural selection on ideas helps us understand why bad ideas spread. The survival fitness of a meme is not determined by the effect it has on its host, but rather by how well it propagates to other hosts. Memes seemingly hijack our brains and make us into machines for spreading more memes. Behaviors and ideas have a viral life of their own, just like our genes do, and we are the sometimes-unfortunate hosts of this second replicator. Memes spread through observation (even involuntary observation), and they copy themselves and mutate into new, potentially viral, strains of ideas.

When designing games, we are not completely at the mercy of these memes; there are ways for us to guide the evolutionary process of memes to a place we want. Although we can't choose which particular memes will emerge from selection, we can alter the environment of selection itself. By carefully designing the environment that the memes will populate, we can make some predictions about what will emerge.

A fitness function is a model for evaluating the fitness of an entity. When programming things like genetic algorithms we are in complete control of the fitness function -- we author it ourselves -- but even when we are not the direct authors of the fitness function, we can approach it sideways and try to model how it would work in the environment we created.

In the real world, we can see things like giraffes evolving over time to fill a niche where they have an opportunity to thrive. The victory condition for a giraffe is to survive long enough to reproduce, and this will require a steady source of food, relative safety from predators and a fair chance at competing for a mate.

Their longer necks allow them access to a food source with less competition, and the population grows in response to the improved living conditions.

 Just like the possible emergence of something like giraffes can be predicted by seeing that there are untapped resources in the form of tall, lush trees, we can anticipate the emergence of certain behaviors by examining the victory conditions of the game.

Article Start Page 1 of 3 Next

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