By this standard, Eric Zimmerman is the Lou Reed of the new gaming culture. A stocky thirty-year-old, with short, club-kid hair and oversize Buddy Holly glasses, Zimmerman has carved out a career for himself that would have been unthinkable even a decade ago: bouncing between academia (he teaches at NYU’s influential Interactive Telecommunications Program), the international art scene (he’s done installations for museums in Geneva, Amsterdam, and New York), and the video-game world. Unlike John Maeda and or Jodi.org, Zimmerman doesn’t “reference” the iconography of gaming in his work—he openly embraces that tradition, to the extent that you have to think of Zimmerman’s projects as games first and art second. They can be fiendishly fun to play and usually involve spirited competition between players. But they are also self-consciously designed as emergent systems.
“One of the pleasures of what I do,” Zimmerman tells me, over coffee near the NYU campus, “is that you get to see a player take what you’ve designed and use it in completely unexpected ways.” The designer, in other words, controls the micromotives of the player’s actions. But the way those micromotives are exploited—and the macrobehavior that they generate—are out of the designer’s control. They have a life of their own.
Take Zimmerman’s game Gearheads, which he designed during a brief sojourn at Phillips Interactive in 1996. Gearheads is a purebred emergent system: a meshwork of autonomous agents following simple rules and mutually influencing each other’s behavior. It is a close relative of StarLogo or Gordon’s harvester ants, but it’s ingeniously dressed up to look like a modern video game. Instead of spare colored pixels, Zimmerman populated the Gearhead world with an eclectic assortment of children’s toys that march across the screen like a motley band of animated soldiers.
“There are twelve windup toys,” Zimmerman explains. “You design a box of toys by choosing four of them. You wind up your toy and release it from the edges of the game board, and the goal of the game is to get as many toys as possible across your opponent’s side of the screen. Each of the toys has a unique set of behaviors that affect the behavior of other toys.” A skull toy, for instance, “frightens” toys that it encounters, causing them to reverse direction, while an animated hand winds up other toys, allowing them to march across the screen for a longer duration. As with the harvester ants or the slime mold cells, when one agent encounters another agent, both agents may launch into a new pattern of behavior. Stumble across your hundredth forager of the afternoon, and you’ll switch over to midden duty; stumble across Zimmerman’s skull toy and you’ll turn around and go the other way.
“The key thing is that once you’ve released your toys, they’re autonomous. You’re only affecting the system from the margins,” Zimmerman says. “It’s a little chaos machine: unexpected things happen, and you only control it from the edges.” As Zimmerman tested Gearheads in early 1996, he found that this oblique control system resulted in behavior that Zimmerman hadn’t deliberately programmed, behavior that emerged out of the local interactions of the toys, despite the overall simplicity of the game.
“Two toys reverse the direction of other toys—the skull, and the Santa toy, who’s called Krush Kringle,” Zimmerman says. “He walks for a few steps and then he pounds the ground, and all the toys near him reverse direction. During our testing, we found a combination where you could release one Krush Kringle out there, then the walking hand that winds up toys, then another Krush Kringle. The hand would run out and wind up the first Krush, and then the Krush would pound the floor, reversing the direction of the hand, and sending it back to the second Krush, which it would wind up. Then the second Krush would stomp on the ground, and the hand would turn around and wind up the first Krush. And so the little system of these three toys would march together across the screen, like a small flock of birds. The first time we saw it happen, we were astonished.”
These unexpected behaviors may not seem like much at first glance, particularly in a climate that places so much emphasis on photo-realistic, 3-D worlds and blood-spattering combat. Zimmerman’s toys are kept deliberately simple; they don’t simulate intelligence, and they don’t trigger symphonies of surround sound through your computer speakers. A snapshot of Resnick’s slime molds looks like something you might have seen on a first-generation Atari console. But I’ll put my money on the slime molds and Krush Kringles nonetheless. Those watermelon clusters and autowinding flocks strike me as the very beginning of what will someday form an enormously powerful cultural lineage. Watching these patterns emerge spontaneously on the screen is a little like watching two single-celled organisms decide to share resources for the first time. It doesn’t look like much, but the same logic carried through a thousand generations, or a hundred thousand—like Hillis growing his gardens of code—can end up changing the world. You just have to think about it on the right scale.
*
Most game players, alas, live on something close to day-trader time, at least when they’re in the middle of a game—thinking more about their next move than their next meal, and usually blissfully oblivious to the ten-or twenty-year trajectory of software development. No one wants to play with a toy that’s going to be fun after a few decades of tinkering—the toys have to be engaging now, or kids will find other toys. And one of the things that make all games so engaging to us is that they have rules. In traditional games like Monopoly or go or chess, the fun of the game—the play—is what happens when you explore the space of possibilities defined by the rules. Without rules, you have something closer to pure improv theater, where anything can happen at any time. Rules give games their structure, and without that structure, there’s no game: every move is a checkmate, and every toss of the dice lands you on Park Place.
This emphasis on rules might seem like the antithesis of the open-ended, organic systems we’ve examined over the preceding chapters, but nothing could be further from the truth. Emergent systems too are rule-governed systems: their capacity for learning and growth and experimentation derives from their adherence to low-level rules: ants choosing to forage or not, based on patterns in their encounters with other ants; the Alexa software making connections based on patterns in the clickstream. If any of these systems—or, to put it more precisely, the agents that make up these systems—suddenly started following their own rules, or doing away with rules altogether, the system would stop working: there’d be no global intelligence, just a teeming anarchy of isolated agents, a swarm without logic. Emergent behaviors, like games, are all about living within the boundaries defined by rules, but also using that space to create something greater than the sum of its parts.
Understanding emergence should be a great boon for the video-game industry. But some serious challenges face the designers of games that attempt to harness the power and adaptability of self-organization and channel it into a game aimed at a mass audience. And those challenges all revolve around the same phenomenon: the capacity of emergent systems to suddenly start behaving in unpredictable ways, sorcerer’s-apprentice style—like Zimmerman’s flock of Krush Kringles.
Consider the case of Evolva, a widely hyped game released in mid-2000 by a British software company called Computer Artworks. The product stood as something of a change for CA, which was last seen marketing a trippy screen-saver called Organic Art that allowed you to replace your desktop with a menagerie of alien-looking life-forms. That program came bundled with a set of prepackaged images, but more adventurous users could also grow their own, “breeding” new creatures with the company’s A-Life technology. While the Organic Art series was a success, it quickly became clear to the CA team that interacting with your creatures would be much more entertaining than simply gazing at snapshots of them. Who wants to look at Polaroids of Sea-Monkeys when you can play with the adorable little critters yourself?
And so Computer Artworks turned itself into a video-game company. Evolva was their first fully interactive product to draw upon the original artificial-life software, integrating its mutation and interbreeding rout
ines into a game world that might otherwise be mistaken for a hybrid of Myth and Quake. The plot was standard-issue video-game fare: Earth has been invaded by an alien parasite that threatens world destruction; as a last defense, the humans send out packs of fearless “genohunters” to save the planet. Users control teams of genohunters, occupying the point of view of one while issuing commands to the others. A product of biological engineering themselves, genohunters are capable of analyzing the DNA of any creature they kill and absorbing useful strands into their own genetic code. Once you’ve absorbed enough DNA, you can pop over to the “mutation” screen and tinker with your genetic makeup—adding new genes and mutating your existing ones, expanding your character’s skills in the process. It’s like suddenly learning how to program in C++, only you have to eat the guy from tech support to see the benefits.
That appetite for DNA gives the A-Life software its entrée into the gameplay. “As the player advances through the game, new genes are collected and added to the available gene pool,” lead programmer Rik Heywood explained to me in an e-mail conversation. “When the player wants to modify one of their creations, they can go to the mutation screen. Starting from the current set of DNA, two new generations can be created by combining the DNA from the existing genohunter with the DNA in the collected gene pool and some slight random mutations. The new sets of DNA are used to morph the skin, grow appendages all over the body, and develop new abilities, such as breathing fire or running faster.”
The promotional material for Evolva makes a great deal of noise about this open-endedness. Some 14 billion distinct characters can be generated using the mutation screen, which means that unless Computer Artists strikes a licensing deal with other galaxies, players who venture several levels deep in the game will be playing with genetically unique genohunters. For the most part, those mutations result in relatively superficial external changes, more like a new paint job than an engine overhaul. The more sophisticated alterations to the genohunters’ behavior—fire-breathing, laser-shooting, longdistance jumping, among others—are largely discrete skills programmed directly by the CA team. You won’t see any genohunters spontaneously learning how to play the cello or use sonar. The bodies of your genohunters may end up looking dramatically different from where they started, but those bodies won’t let their hosts adopt radically new skills.
These limitations may well make the game more enjoyable. For a sixteen-year-old Quake player who’s just trying to kill as many parasites as possible on his way to the next level, suddenly learning how to read braille is only going to be a distraction. Anyone who has spent time playing a puzzle-based narrative game like Myst knows nothing is more frustrating than spending two hours trying to solve a puzzle that you don’t yet have the tools to solve, because you haven’t stumbled across them in your explorations of the game space. Imagine how much more frustrating to get stumped by a puzzle because you haven’t evolved gills or lock-picking skills yet. In a purely open-ended system—where the tools may or may not evolve depending on the whims of natural selection—that frustration would quickly override any gee-whiz appeal of growing your own characters. And so Heywood and his team have planted DNA for complex skills near puzzles or hurdles that require those skills. “For example, if we wanted to be sure that the player had developed the ability to breath fire by a particular point in the game,” he explains, “we would block the path with some flammable plants and place some creatures with a fire-breathing ability nearby.”
The blind watchmaker of Evolva’s mutation engine turns out to have some sight after all. Heywood’s solution might be the smartest short-term move for the gamers, but it’s worth pointing out that it also runs headlong against the principles of Darwinism. Not only are you playing God by deliberately selecting certain traits over others, but the DNA for those traits is planted near the appropriate obstacles. It’s like some strange twist on Lamarckian evolution: the giraffe neck grows longer each generation, but only because the genes for longer necks happen to sprout next to the banana trees. The space of possibility unleashed by an open-ended Darwinian engine was simply too large for the rule-space of the game itself. A game where anything can happen is by definition not a game.
*
Is there a way to reconcile the unpredictable creativity of emergence with the directed flow of gaming? The answer, I think, will turn out to be a resounding yes, but it’s going to take some trial and error. One way involves focusing on traditional emergent systems—such as flocks and clusters—and less on the more open-ended landscape of natural selection. Evolva is actually a great example of the virtues of this sort of approach. Behind the scenes, each creature in the Evolva world is endowed with sensory inputs and emotive states: fear, pain, aggression, and so on. Creatures also possess memories that link those feelings with other characters, places, or actions—and they are capable of sharing those associations with their comrades. As the web of associations becomes more complex, and more interconnected, new patterns of collective behavior can evolve, creating a lifelike range of potential interactions between creatures in the world.
“Say you encounter a lone creature,” Heywood explains. “When you first meet it, it is maybe feeling very aggressive and runs in to attack your team. However, you have it outnumbered and start causing it some serious pain. Eventually fear will become the dominant emotion, causing the creature to run away. It runs around a corner and meets a large group of friends. It communicates with these other creatures, informing them of the last place it saw you. Being in a large group of friends brings its fear back down, and the whole group launches a new attack on the player.” The group behavior can evolve in unpredictable ways, based on external events and each creature’s emotional state, even if the virtual DNA of those creatures remains unchanged. There is something strangely comforting in this image, particularly for anyone who thinks social patterns influence our behavior as readily as our genes do. Heywood had to restrict the artificial-life engine because the powers of natural selection are too unpredictable for the rules-governed universe of a video game. But building an emergent system to simulate collective behavior among characters actually improved the gameplay, made it more lifelike without making it impossible. Emergence trumps “descent with modification”: you may not be able to use Evolva’s mutation engine to grow wings, but your creatures can still learn new ways to flock.
There is a more radical solution to this problem, though, and it’s most evident in the god-games genre. Classic games like SimCity—or 1999’s bestselling semi-sequel The Sims, which lets game players interact with simulated personalities living in a small neighborhood—have dealt with the unpredictability of emergent software by eliminating predefined objectives altogether. You define your own goals in these games; you’re not likely to get stuck on a level because you haven’t figured out how to “grow” a certain resource, for the simple reason that there are no preordained levels to follow. You define your own hurdles as you play. In SimCity, you decide whether to build a megalopolis or a farming community; whether to build an environmentally correct new urbanist village or a digital Coketown. Of course, you may find it hard to achieve those goals as you build the city, but because those goals aren’t part of the game’s official rules, you don’t feel stuck in the same way that you might feel stuck in Evolva, staring across the canyon without the genes for jumping.
There’s a catch here, though. “The challenge is, the more autonomous the system, the more autonomous the virtual creatures, the more irrelevant the player is,” Zimmerman explains. “The problem with a lot of the ‘god games’ is that it’s difficult to feel like you’re having a meaningful impact on the system. It’s like you’re wearing these big, fuzzy gloves and you’re trying to manipulate these tiny little objects.” Although it can be magical to watch a Will Wright simulation take on a life of its own, it can also be uniquely frustrating—when that one neighborhood can’t seem to shake off its crime problem, or your Sims refuse to fall in love. For better or worse, we c
ontrol these games from the edges. The task of the game designer is to determine just how far off the edge the player should be.
Nowhere is this principle more apparent than in the control panel that Will Wright built for The Sims. Roll your cursor along the bottom of the screen while surveying your virtual neighborhood, and a status window appears, with the latest info on your characters’ emotional and physical needs: you’ll see in an instant whether they’ve showered today, or whether they’re pining for some companionship. A click away from that status window is a control panel screen, where you can adjust various game attributes. A “settings” screen is by now a standard accoutrement of any off-the-shelf game: you visit the screen to adjust the sound quality or the graphics resolution, or to switch difficulty levels. At first glance, the control panel for The Sims looks like any of these other settings screens: there’s a button that changes whether the window scrolls automatically as you move the mouse, and another that turns off the background music. But alongside these prosaic options, there is a toggle switch that says, in unabashed Cartesian terms, “Free will.”
Emergence Page 17