B004R9Q09U EBOK (3 page)

To understand how these systems work requires taking a big step back into our biological past; but before we set out on the murky terrain of evolutionary theory, let me sound a note of caution. Many social scientists (and more than a few biologists) tend to regard evolutionary explanations of human culture with a skepticism bordering on outright hostility. Ever since the well-chronicled sociobiology wars of the 1970s and 1980s, scholarly debates have raged about the applicability of evolutionary theory to human culture. The conventional argument goes something like this:
Homo sapiens
achieved anatomical modernity some 100,000 years ago. Since then our genes have not evolved in any significant way. Therefore, the term “evolution” simply does not pertain to the brief span of human culture. Human culture, they argue, is a complex phenomenon that progresses independently of biological evolution. Evolution is at best a metaphor—and at worst a sloppy syllogism—for cultural change: the stuff of eugenics, Social Darwinism, and worse. The late Stephen Jay Gould, among others, made eloquent arguments against the notion that human culture evolves in any biological sense. “Using the same term—evolution—for both natural and cultural history obfuscates far more than it en
lightens,” he wrote.
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Human culture, then, cannot be reduced to simple biological processes, and the sheer variety of human culture would seem to prove its independence from biology. In this view, the process of natural selection simply deposited us on the precipice of culture; everything we have achieved since then we have done by—and to—ourselves.

In recent years, however, the srict doctrine of cultural relativism has started to reveal chinks in its conceptual armor. Thanks to breakthrough work by sociobiologists and evolutionary psychologists, many scientists are now embracing the possibility that natural selection operates at multiple levels of the biological hierarchy—up to the highest levels of human culture. Furthermore, they are beginning to appreciate the complexity of “culture” in other species, and to recognize the possibility that certain social behaviors may have evolved in concert with biological evolution. “To say that human behavior and our other attributes cannot be analyzed in evolutionary terms requires acceptance of a genuinely bizarre position,” argues John Alcock, “namely, that we alone among animal species have somehow managed to achieve independence from our evolutionary history.”
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Through the filter of evolutionary psychology, we can begin to explore the possibility that cultural behaviors once thought uniquely human—such as the ability to organize information—may have a deeper evolutionary lineage.

In 1970 biologist Lynn Margulis proposed a revolutionary hypothesis about the origin of complex organisms, suggesting that the relationship between networked and hierarchical systems is deeply woven into the fabric of life itself. To make a particularly long story short: About two billion years ago, the first multicellular organisms—eukaryotes—took shape as host bacteria and began to allow other bacteria to take up residence within them, gradually conscripting their formerly independent siblings into a kind of cellular serfdom. These early complex life forms came into existence not as fully formed organisms but rather through a gradual process of evolving social cooperation. Eventually, as teams of bacteria began to reproduce in tandem with their host organisms, they coalesced into nucleated cells capable of reproducing themselves. The first multicellular life forms
came into being, then, as self-organizing networks. Gradually, those networks gave rise to an emergent hierarchy: the complex organism. These self-directed biological hierarchies, in turn, began to interact with one another, forming new higher-level networks that, in turn, coalesced into more complex biological hierarchies. From this escalating fugue of hierarchies and networks, life has evolved.

By 500 million BC, complex organisms of a trillion cells or more began to take shape: the first insects, clams, and birds, equipped with highly specialized organs like eyes, nerve cells, and memory coils. As their neural powers grew, these animals became more autonomous. But independence came at a price: The more complex life forms became, the more isolated they became from one another. To compensate for the loss of old physical bonds, they began to develop what Bloom calls “the synapse of the social brain”
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: imitation. By following each other’s behavioral cues, certain animals could pool their sensory data, processing collective experience with a capacity far exceeding the abilities of the lone organism. Imitation provided the glue that bound these increasingly self-directed animals into a higher order of organization: social networks. They became, in effect, superorganisms.

A biological superorganism—like an insect colony, a flock of birds or a school of fish—is both a network and a hierarchy; it emerges from the networked interaction of individual organisms, in turn giving rise to higher-order hierarchies. As individual organisms transmit information to each other, they strengthen the bonds that unite the group. But what, exactly, is being transmitted? Information is, after all, noncorporeal; it is not a physical “thing” (even though it may take expression in the physical environment). Yet there is no question that animals are transmitting some kind of “thing” to each other. So what exactly is it?

In his influential 1975 book
The Selfish Gene
, evolutionary biologist Richard Dawkins dubbed the thing a “meme.” Other biologists have variously labeled it a mnemotype, idea, idene, sociogene, or concept.
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But only “meme” has managed to penetrate the popular vernacular. Whatever term we use, there is no question that other animals regularly record, share, and preserve information that has no physical
manifestation. The exchange of memes, moreover, appears to exert a direct influence on the process of natural selection. Biologists have learned a great deal about how memes travel between individual members of a social group, mapping the trajectories of information exchange from individual to individual; less well understood are the higher-level dynamics that characterize the interactions of memes at the group level.

One highly speculative explanation of how nature’s “mass minds” operate comes from Bloom, whose unconventional theories and colorful rhetoric have marked him as an agent provocateur in mainstream scientific circles. Bloom postulates that the phenomenon of collective intelligence emerges from the interplay of five essential forces: conformity enforcers, diversity generators, inner judges, resource shifters, and intergroup tournaments. Conformity enforcers (like worker bees or middle managers) ensure that the group as a whole maintains sufficient cohesion to survive adverse conditions; diversity generators (like stray ants or artists) are the “odd ducks” who generate alternative hypotheses for the group to consider, thus ensuring variation; intergroup tournaments (like the waggle dances of bees or scientific debates) enable societies to test alternative hypotheses; inner judges reward productive behavior and punish deleterious actions. Finally, resource shifters (like alpha chimpanzees or corporate executives) make sure successful adaptations receive the support they need to benefit the group as a whole.
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Bloom’s model, while intriguing, is too figurative to pass any empirical test. Nonetheless, respected evolutionary biologists like Margulis and David Sloan Wilson have recognized value in his original, if decidedly left-field, conception of the global brain. We do not have to accept Bloom’s theory as hard science, however, to appreciate it as a metaphor. As Alfred North Whitehead put it, “It is more important that a proposition be interesting than that it be true. But of course a true theory is more apt to be interesting than a false one.”

Today, we can find ample evidence throughout the natural world of networked superorganisms pooling information, organizing and distributing that information to the right individuals at the right time, and preserving successful group strategies for future generations. In
sect societies provide the most often cited example of how seemingly simple creatures can process information with a sophistication seemingly not predicted by their genetic blueprints. In 1946 biologist Karl von Frisch won a Nobel Prize for deciphering the syntax of bees’ famous waggle dances, the carefully choreographed tournaments that enable beehives to identify the location of food supplies with astonishing precision. Certain bees scout a particular area for potential food sources and then return to the hive to present choreographed “reports” on what they found. As each bee makes it presentation, the group as a whole registers its level of enthusiasm and compares incoming reports to make a group decision about where to look for food next. No individual bee possesses the intelligence to make such a decision, but as a group, the bees generate a collective “mind” far more clever than the sum of its tiny-brained parts. Elsewhere, studies have shown that, while an individual honeybee can retain a piece of data in its memory bank for up to six days, the hive as a whole can retain that same piece of data for up to three months—double the life span of a single bee.
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Some insect colonies display even higher-order behaviors that rely not just on social displays but that actually involve coding their memes onto the physical environment. In 1959 biologist Pierre-Paul Grassé coined the term “stigmergy”
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(meaning “incite to work”) after studying how termites erect their monumental nests. Grassé observed that termites build nests by dint of a brutally simple algorithm: A single termite carries a chewed pellet of dirt in its jaws; whenever it encounters a slightly elevated mound of dirt, it drops the pellet. When a number of termites begin to drop pellets on the same mound, a small clump begins to form; other passing termites, detecting the clump, begin to drop their own pellets, and the mound grows. If one mound starts growing in close proximity to another, the termites will begin scuttling back and forth between the mounds, building diagonally to join them together. From these simple rules, great cathedral-like artifacts emerge. They are living information systems.

Ants, too, exhibit a capacity for stigmergy, communicating to each other through pheromone trails that guide their collective behavior. In one study, researcher Jean-Louis Deneubourg provided an
ant colony with two separate tree branches, each a pathway to the same food supply. Individual ants immediately began charging blindly down both paths. Within a few minutes, however, the entire colony had collectively chosen the shorter of the two branches. As the first wave of ants returned from their round-trip journey, they began signaling their remaining compatriots to follow their trail. As the collective pheromone trail grew stronger, the entire colony abandoned the unscented trail and flocked down the fast path to the food supply.

Ants’ dual roles as both actors and signals make them a compelling reference model for computer programmers. The new field of “swarm intelligence” has grown up in recent years to explore the application of insect-like solutions to practical problems like computer network design. At Hewlett-Packard, a research team developed a network routing system based explicitly on the tracking behaviors of ant colonies, consisting of a large number of tiny computer programs that wound their way through a distributed network, searching out the least congested routes. Depositing “virtual pheromone” trails at each node, the swarm of programs quickly deduced the fastest path through the network.

Whereas other forms of communication in the natural world typically involve one-to-one transmission between individuals, stigmergy involves at least one step of intermediation: encoding information on an external object. It is a mechanism for indirect collaboration, equipping life forms with what philosopher Ron McClamrock describes as the ability to “enforce on the environment certain kinds of stable properties that will lessen our computational burdens.”
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In other words, stigmergy allows social groups to harness the physical world as a memetic storehouse (something at which our own species has long excelled).

While insect societies provide nature’s most elegant examples of information systems that transcend the capacities of individuals, the animal kingdom is rife with other examples of species that share information in groups. In an oft-cited bird study in 1950s England, a small population of English tits stumbled upon the secret of pecking through aluminum milk bottle caps, as a means to get at the milk inside. The new strategy soon took hold among the tit population at
large, and an avian meme took flight. An epidemic of bottle pecking spread across the surrounding counties, as successive generations of tits learned the fine art of milk larceny from their peers. In New Zealand, flocks of oystercatcher birds have developed their own idiosyncratic customs for cracking open mussel shells: Some groups hammer the shell until it opens; others pry it open with their beaks. All oystercatchers are born with the capacity for either behavior; the sole determinant of how an individual bird behaves appears to rest entirely on the bird’s social affiliation. In other words, social networks—not genetics—may determine how behavioral information gets transmitted.

While insects and birds demonstrate how information systems emerge in species whose intelligence ranks far below our own, we can find examples of complex social networking behaviors in more intelligent animals, notably the primates. Throughout the primate world, social networks provide a fast conduit for innovation and information-sharing that help the group as a whole adapt to its environment.

In Japan a troop of hungry macaques stumbled on an innovative process for separating edible wheat from indigestible sand. On one island an enterprising macaque came up with a handy trick: By throwing the entire mixture of wheat and sand into the sea, the macaque discovered that the wheat would float to the top while the sand sank to the bottom. Success begat imitation; soon after the first macaque stumbled on the wheat-sifting technique, the practice spread through imitative “word of mouth”; within 5 years the entire macaque population of Japan had learned to sift their wheat.
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Our closest cousins, chimpanzees, also display highly localized traditions that persist across generations. Some chimpanzee troops use rocks to break open nuts; others rely on tree trunks. Some groups have figured out how to use twigs as rudimentary “fishing rods” for digging up ants; others forge long hooked branches into tools for retrieving figs. These local traditions get passed down through generations purely through social transmission, not genetic inheritance. In a similar study, biological anthropologist Carel van Schaik and his colleagues discovered a group of orangutans on the western coast of
Sumatra that had developed highly localized customs for using tools. One group had perfected an intricate procedure for removing seeds from the toughened husks of the
Neesia
tree, by stripping the bark from twigs and using the rudimentary tools to jimmy open the husks.
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This particular trick seems to have emerged only within one local population; other nearby groups in similar environments had no such innovation, and their
Neesia
seeds went uneaten. The original group had also developed a series of other tool-use innovations, far outstripping the achievements of the surrounding communities. Why did one group of orangutans prove so consistently innovative while their nearby cousins languished? The key to cultural innovation, it seems, has something to do with population density. Van Schaik hypothesizes that “populations in which individuals had more chances to observe others in action would show a greater diversity of learned skills than would populations offering fewer learning opportunities. And indeed, we were able to confirm that sites in which individuals spend more time with others have greater repertoires of learned innovations.”
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The orangutans of Saaq crossed a threshold of social proximity that enabled them to adapt more quickly and successfully to their environment. As we will see later in the book, a similar phenomenon recurs among human social groups, where population density appears to determine the velocity of technological change. Social concentration, it seems, is the engine of innovation.