Born blind, deaf, and mute, shuffling around in the darkness at 30 miles per hour, grasping for food, searching for mates, the life of your average bacteria (or any of the several trillion single-cell organisms on the planet) is invariably nasty, brutish, and short.
Be glad you’re a eukaryote. Like amoeba, insects, chimpanzees, and every other form of complex animal life, we enjoy not only the polymorphous pleasures of multi-cellularity, but also a singular gift, one that distinguishes us from all other known life forms: the ability to share knowledge with each other.
John Locke famously argued that “beasts abstract not.” But in recent years, breakthrough research by sociobiologists and evolutionary psychologists suggests otherwise: that not only do many of our fellow “beasts” abstract, but they have also developed surprisingly sophisticated and highly variegated mechanisms for managing information.
When most of us talk about “information architecture,” we seem to situate ourselves within strictly humanist reference points like graphic design or library science (with perhaps a perfunctory nod to journalism, cognitive psychology, or semiotics).
To approach information architecture from a purely anthrocentric perspective, however, is to overlook the lessons of billions of years’ worth of evolutionary history. We are by no means the first species to grapple with the basic problems of what we now call information architecture: how to acquire knowledge in social groups, how to get the right information to the right party at the right time, how to distill meaning from raw data.
Much as we may like to think of ourselves as belonging to a uniquely privileged species, the fact is that every complex organism on this planet is engaged in a shared struggle with information overload.
As information architects (and human beings) we should be careful of presuming that all our present quandaries surfaced only in the past few years—or, for that matter, in the last few thousand years of recorded human history (a comparative millisecond on the evolutionary clock).
Long before anyone was looking for “godfathers” of information architecture, our fellow species were wrestling with some of the same problems we face today. The real godfathers of information architecture, as it turns out, emerged a very long time ago with the earliest origins of life on this planet.
The memory “switch”
Let’s dial back in time to a hot wet Tuesday in, say, 2,200,000 B.C. Swimming in the briny planetary sea, we find the earliest recognizable living organisms: our aforementioned friends, the prokaryotes.
Now by this time, prokaryotes had been sloshing around in the ooze for something like a billion years. Then, about 2 billion years ago—whether by dint of divine impetus or happy cosmic accident—something remarkable happened: These formerly independent organisms started to collaborate.
To make a long story short: Networks of formerly independent bacteria began forming loose collectives—sharing labor, food, and increasingly deploying specialized cells to complete certain discrete tasks. Eventually, these loosely affiliated organic teams began replicating in tandem, taking on a more persistent form as they became the earliest multi-cellular organisms.
Eukaryotic cells took shape as “host” bacteria started allowing other bacteria to take up residence within them, gradually conscripting the simpler, formerly independent prokaryotes into service. Eventually, these new bacteria began to reproduce in tandem with the host bacteria, forming a replicable organism that evolved into successively more complex life forms—with increasingly specialized tasks.
These early eukaryotes—close cousins of present-day amoeba or slime molds—learned to sense and respond to environmental conditions, adapting cells and forming new cells in response to incoming data from the surrounding environment. One cell would capture a sensory impression and relay it through adjacent cells across its immediate network, tripping amino acid “switches” to signify changes in the environment.
Maverick scientist Howard Bloom has theorized that the advent of multi-cellularity marked the birth of a nascent “global brain,” a worldwide neural network that would continue to grow and evolve for the next two million years. “Informationally linked microorganisms possessed a skill exceeding the capacities of any supercomputer from Cray Research or Fujitsu,” writes Bloom. “Ancient bacteria” had mastered the art of worldwide information exchange.
Meet the original information architects.
Social networks 1.0
Let’s fast-forward another 1.5 billion years to a rainy Thursday in the Pleistocene. In one of those rare bursts of evolutionary activity (what Stephen Jay Gould famously called “punctuated equilibrium”), a sudden wave of life forms is taking shape during the so-called Cambrian Explosion.
It took a billion years for species to evolve to the point where complex multi-cellular organisms—like shellfish—could emerge. With increasingly elaborate networks of interdependent organs—mouths, hearts, legs, and so forth—individual organisms began to comprise a trillion cells or more.
As life forms became more complex, they also became less directly dependent on each other for survival. As a substitute for the direct transmission of data through chemical relays, these independent organisms began developing a new mechanism for transmitting knowledge: imitation. By observing, responding, and mimicking their peer organisms, these creatures could effectively leverage each other’s senses, experiences, and decision-making capabilities.
The archetypal success story of the Cambrian Era is the trilobite, a wildly prolific organism whose numbers at one point circled the entire planet (its survival as a species was aided in no small part by its predilection for wild, shells-off mating orgies). These organisms were self-contained, self-directed, and less dependent on a constant stream of data inputs for survival. Rather, they had evolved to the point where the individual organism had the resources to ensure its own immediate survival: sensing, responding, eating, and mating. But they were not exactly what you would call independent thinkers.
These new self-directed organisms still relied heavily on their peers for survival and adaptation. As a substitute for the direct transmission of data over the biological network, they began developing a new mechanism for transmitting knowledge: imitation. By observing, responding, and mimicking their peer organisms, these creatures could effectively leverage each other’s senses, experiences, and decision-making capabilities.
These early social learning networks relied not on biological or chemical signals, but rather on imitative learning and the gradual development of behavioral “memes” that could persist beyond the lifespan of any one organism.
Pulitzer Prize-winning entomologist E.O. Wilson coined the term “sociobiology” to describe the study of social behavior from an evolutionary perspective. Wilson’s landmark 1975 book, Sociobiology: the New Synthesis, brilliantly punctured the prevalent scientific view that animal behavior could be adequately explained through the traditional disciplinary filters of biology, chemistry, and genetic inheritance.
Wilson argued that the social learning mechanisms evident in other species required a new perspective, a “synthesis” of biology and the social sciences. Wilson argued that “learned modifications of behavior are not inherited; only the innate predispositions are inherited, and only these can evolve by natural selection.” In other words, social groups can transmit and preserve knowledge through non-biological means, forming “learning machines” with remarkable powers of collective memory, calculation, and distributed decision-making capabilities.
Regrettably, Wilson’s work has been misinterpreted by certain doctrinaire humanists, who have chosen to infer parallels between sociobiology and pseudo-sciences such as genetic determinism or—worse yet—eugenics (the dark science of Nazi genetic engineering). Alas, like Darwin, Wilson’s theories have lent themselves to misuse and misappropriation by groups with political or social agendas. Some feminists, for example, have objected strenuously to the entire discipline of sociobiology on the basis that it seems to offer an apologia for male dominance. Wilson himself would vigorously protest such abuse; he has frequently cautioned against perversions of science in the service of political “advocacy.”
Thanks to the conceptual foundation of sociobiology and, more recently, evolutionary psychology, we are beginning to understand the complexity and sophistication of nature’s super-organisms, some of them seeming to exhibit properties once thought the exclusive province of humanity: language, reason, even the outlines of culture.
For a glimpse of how these early “learning machines” may have operated, we need look no further than some of our contemporary planetary species.
Perhaps the most widely studied examples of nature’s collective learning machines are insect colonies. Ants and bees in particular exhibit remarkable powers of collective reasoning and an ability to accumulate and share sensory data in social groups.
Wilson devoted much of his career to studying the behaviors of ant colonies, which perform seemingly complex feats of calculation and geometry, and elaborately orchestrated group warfare.
Throughout the insect world, colonies of individual organisms appear to exhibit powers of reasoning seemingly not predicted by the capabilities of a single organism. Douglas Hofstadter first applied the term “emergence” to the behavior of ant colonies in his landmark essay “Ant Fugue,” in which he described the dual nature of individual ants as both functioning organisms and as, in effect, signals.
In his 2001 book Emergence, Steven Johnson explored emergence theory as a context for explaining the self-organizing properties of internet communications, and as a construct for self-directed software agents in a future, more intelligent incarnation of the World Wide Web.
While a few software developers have attempted intriguing experiments at modeling software after the distributed behaviors of ant colonies, we should bear in mind that that the essential mechanisms of colony behavior cannot be solely explained in terms of mechanistic or mathematical models. Wilson argued that insect colony social behavior must be properly understood as “an idiosyncratic adaptation” to the surrounding environment, rather than a purely mechanical operation.
In other words, these behaviors constitute distinctly social responses, transmitted across generations through an elaborate dance of imitative learning and adaptation. There is another force at work here: information.
Monkeys in the mirror
Ever since Carl Linnaeus boldly decided to group humans with monkeys and apes into a family he designated “primates,” we have looked to these close evolutionary cousins for clues to our own behavior patterns.
Although we may tend to think of “culture” as a uniquely human trait, numerous primate studies have revealed the presence of localized social traditions, rudimentary language, and the facility for transmitting learned knowledge across generations.
Dutch primatologist Frans de Wahl recounts an experiment in which he introduced a group of rhesus monkeys—a particularly argumentative, pugnacious group—with a troop of more even-tempered stump-tailed monkeys. Within a few months, the rhesus monkeys “developed peacemaking skills on a par with those of their more tolerant counterparts” through imitative learning and ritual displays. Most importantly, the rhesus monkeys carried on these behaviors long after they had been permanently separated from the stumptails. In other words, social transmission of knowledge effected a permanent change in group behavior.
All primate cultures seem to rely on learned—rather than genetically determined—social arrangements, which often vary between different social groups within the same species (as demonstrated in numerous chimpanzee studies).
While these social knowledge transmissions have no external symbolic manifestation—other primates don’t write books or create external symbolic language—they do nonetheless create, store, and transmit social knowledge that persists across generations: surely a manifestation of the same impulse that drives us towards information architecture.
The disintegration of hierarchy
Throughout most of human history, information has flowed through small groups in ways not so different from the imitative social learning mechanisms evident in other primates.
Only in the past four thousand-odd years of recorded human history have we developed the capacity for symbolic representation—and with it, a new externalized construct of “information.”
The rise of written language paralleled (and facilitated) the rise of the modern institution: churches, governments, universities, and corporations, to name a few. As these larger collective entities began to supplant the close-knit family and kinship bonds of earlier social groups, the institution also took on a new function as a container for shared knowledge—what Francis Fukuyama has called the “knowledge bureau.”
Fukayama has argued that the rise of the “knowledge worker” in Western society, coupled with the liberating effects of communications technologies, is gradually undermining these institutional hierarchies that have characterized our collective social experience for the past four thousand years. And with the fragmentation of institutions comes the upending of traditional knowledge bureaus.
In The Social Life of Information, John Seely Brown and Paul Duguid draw the distinction between “fixed” sources of information that are typically the province of institutions (such as government records, books, and other documents) and the “fluid” information that tends to emanate from individuals and small groups—(such as email, instant messages, and threaded discussions).
Howard Rheingold has recently chronicled the rising tide of fluidity in newly evident social phenomena like “smart mobs” and “flocking”—social behaviors in which large groups of individuals act in seeming concert, without any apparent organizational hierarchy at work. From recent political riots in the Philippines to more recent mass events like the Nigerian Miss World Pageant riots, or the unprecedented wave of recent anti-war protests, we seem to be undergoing the early stages of a dramatic transformation in the behavior of social groups.
If we look closely at the behavioral dynamics of these new behavior patterns—widely dispersed, non-hierarchical social relay systems—we can easily recognize the contours of earlier patterns of communication and knowledge-sharing evident in every species to the earliest forms of life on this earth. While these recent phenomena may seemingly result from modern technologies, they appear to manifest some very old patterns of social learning and knowledge sharing.
From social networks to social capital
Today, the practice of information architecture remains primarily an institutional endeavor, driven by the needs of corporations, governments, and educational institutions.
Today’s information architects are the heirs of yesterday’s scribes, clerks, and clerics: laboring to acquire, store, and disseminate knowledge for the sake of humanity, but ultimately in the service of institutions.
Now, some IAs may protest that assertion, arguing that the practice of IA is not about the organization, but about “the user.” But I would argue that if we look closely at that elusive user, we may discover not real human need but a flimsy straw man, a construct designed to serve an intrinsically institutional agenda.
What evolution teaches us is this: in order to understand the deeper roots of our need to generate and manage information, we need to look beyond the individual organism, towards the social groups that drive the mechanisms of evolution and adaptation for all species.
In recent years, the term “social software” has gained currency as a rubric for describing a new breed of software: groupware, social network visualization, discussion lists, and a host of other collaborative tools that support the needs of small, self-selected groups of individuals rather than organizational imperatives.
The real promise of social software has less to do with commercial productivity, and more to do with generating social capital: trust, social engagement, and the development of sustainable knowledge-sharing mechanisms that enable our advancement and evolution within social groups.
What does this mean for information architects? Over time, I believe we may find ourselves progressively less focused on solving the problems of institutions, and increasingly attuned to the needs of groups: a new kind of information architecture—and a very old one too.
Brown, John Seely and Duguid, Paul. The Social Life of Information. Harvard Business School Press, February 2000.
Johnson, Steven. Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Scribner, September 2001.
Wilson, Edward Osborne. Sociobiology: The New Synthesis. Harvard University Press, September 1975.