The trouble with Gaia,
as far as most skeptics are concerned, is that it makes a dead planet into a
"smart" machine. We already are stymied in trying to design an artificial learning
machine from inert computers, so the prospect of artificial learning evolving
unbidden at a planetary scale seems ludicrous.
But learning is overrated as something difficult to evolve. This may have to do
with our chauvinistic attachment to learning as an exclusive mark of our species.
There is a strong sense, which I hope to demonstrate in this book, in which
evolution itself is a type of learning. Therefore learning occurs wherever
evolution is, even if artificially.
The dethronement of learning is one of the most exciting intellectual frontiers we
are now crossing. In a virtual cyclotron, learning is being smashed into its
primitives. Scientists are cataloguing the elemental components for adaptation,
induction, intelligence, evolution, and coevolution into a periodic table of life.
The particles for learning lie everywhere in all inert media, waiting to be
assembled (and often self-assembled) into something that surges and quivers.
Coevolution is a variety of learning. Stewart Brand wrote in CoEvolution
Quarterly: "Ecology is a whole system, alright, but coevolution is a whole system
in time. The health of it is forward-systemic self-education which feeds on
constant imperfection. Ecology maintains. Coevolution learns."
Colearning might be a better term for what coevolving creatures do. Coteaching
also works, for the participants in coevolution are both learning and teaching
each other at the same time. (We don't have a word for learning and teaching at
the same time, but our schooling would improve if we did.)
The give and take of a coevolutionary relationship-teaching and learning at
once-reminded many scientists of game playing. A simple child's game such as
"Which hand is the penny in?" takes on the recursive logic of a chameleon on a
mirror as the hider goes through this open-ended routine: "I just hid the penny in
my right hand, and now the guesser will think it's in my left, so I'll move it
into my right. But she also knows that I know she knows that, so I'll keep it in
Since the guesser goes through a similar process, the players form a system of
mutual second-guessing. The riddle "What hand is the penny in?" is related to the
riddle, "What color is the chameleon on a mirror?" The bottomless complexity which
grows out of such simple rules intrigued John von Neumann, the mathematician who
developed programmable logic for a computer in the early 1940s, and along with
Wiener and Bateson launched the field of cybernetics.
Von Neumann invented a mathematical theory of games. He defined a game as a
conflict of interests resolved by the accumulative choices players make while
trying to anticipate each other. He called his 1944 book (coauthored by economist
Oskar Morgenstern) Theory of Games and Economic Behavior because he perceived that
economies possessed a highly coevolutionary and gamelike character, which he hoped
to illuminate with simple game dynamics. The price of eggs, say, is determined by
mutual second-guessing between seller and buyer-how much will he accept, how much
does he think I will offer, how much less than what I am willing to pay should I
offer? The aspect von Neumann found amazing was that this infinite regress of
mutual bluffing, codeception, imitation, reflection, and "game playing" would
commonly settle down to a definite price, rather than spiral on forever. Even in a
stock market made of thousands of mutual second-guessing agents, the group of
conflicting interests would quickly settle on a price that was fairly stable.
Von Neumann was particularly interested in seeing if he could develop optimal
strategies for these kinds of mutual games, because at first glance they seemed
almost insolvable in theory. As an answer he came up with a theory of games.
Researchers at the U.S. government-funded RAND corporation, a think tank based in
Santa Monica, California, extended von Neumann's initial work and eventually
catalogued four basic varieties of mutual second-guessing games. Each variety had
a different structure of rewards for winning, losing, or drawing. The four simple
games were called "social dilemmas" in the technical literature, but could be
thought of as the four building blocks of complicated coevolutionary games. They
were: Chicken, Stag Hunt, Deadlock, and the Prisoner's Dilemma
Chicken is the game played by teenage daredevils. Two cars race toward a cliff's
edge; the driver who jumps out last, wins. Stag Hunt is the dilemma faced by a
bunch of hunters who must cooperate to kill a stag, but may do better sneaking off
by themselves to hunt a rabbit if no one cooperates. Do they gamble on cooperation
(high payoff) or defection (low, but sure payoff)? Deadlock is a boring game where
mutual defection pays best. The last one, the Prisoner's Dilemma, is the most
illuminating, and became the guinea pig model for over 200 published social
psychology experiments in the late 1960s.
The Prisoner's Dilemma, invented in 1950 by Merrill Flood at RAND, is a game for
two separately held prisoners who must independently decide whether to deny or
confess to a crime. If both confess, each will be fined. If neither confesses,
both go free. But if only one should confess, he is rewarded while the other is
fined. Cooperation pays, but so does betrayal, if played right. What would you do?
Played only once, betrayal of the other is the soundest choice. But when two
"prisoners" played the game over and over, learning from each other-a game known
as the Iterated Prisoner Dilemma-the dynamics of the game shifted. The other
player could not be dismissed; he demanded to be attended to, either as obligate
enemy or obligate colleague. This tight mutual destiny closely paralleled the
coevolutionary relationship of political enemies, business competitors, or
biological symbionts. As study of this simple game progressed, the larger question
became, What were the strategies of play for the Iterated Prisoner's Dilemma that
resulted in the highest scores over the long term? And what strategies succeeded
when played against many varieties of players, from the ruthless to the kind?
In 1980, Robert Axelrod, a political science professor at University of Michigan,
ran a tournament pitting 14 submitted strategies of Prisoner's Dilemma against
each other in a round robin to see which one would triumph. The winner was a very
simple strategy crafted by psychologist Anatol Rapoport called Tit-For-Tat. The
Tit-For-Tat strategy prescribed reciprocating cooperation for cooperation, and
defection for defection, and tended to engender periods of cooperation. Axelrod
had discovered that "the shadow of the future," cast by playing a game repeatedly
rather than once, encouraged cooperation, because it made sense for a player to
cooperate now in order to ensure cooperation from others later. This glimpse of
cooperation set Axelrod on this quest: "Under what conditions will cooperation
emerge in a world of egoists without central authority?"
For centuries, the orthodox political reasoning originally articulated by Thomas
Hobbes in 1651 was dogma: that cooperation could only develop with the help of a
benign central authority. Without top-down government, Hobbes claimed, there would
be only collective selfishness. A strong hand had to bring forth political
altruism, whatever the tone of economics. But the democracies of the West,
beginning with the American and French Revolutions, suggested that societies with
good communications could develop cooperative structures without heavy central
control. Cooperation can emerge out of self-interest. In our postindustrial
economy, spontaneous cooperation is a regular occurrence. Widespread
industry-initiated standards (both of quality and protocols such as 110 volts or
ASCII) and the rise of the Internet, the largest working anarchy in the world,
have only intensified interest in the conditions necessary for hatching
This cooperation is not a new age spiritualism. Rather it is what Axelrod calls
"cooperation without friendship or foresight"-cold principles of nature that work
at many levels to birth a self-organizing structure. Sort of cooperation whether
you want it or not.
Games such as Prisoner's Dilemma can be played by any kind of adaptive agent-not
just humans. Bacteria, armadillos, or computer transistors can make choices
according to various reward schemes, weighing immediate sure gain over future
greater but riskier gain. Played over time with the same partners, the results are
both a game and a type of coevolution.
Every complex adaptive organization faces a fundamental tradeoff. A creature must
balance perfecting a skill or trait (building up legs to run faster) against
experimenting with new traits (wings). It can never do all things at once. This
daily dilemma is labeled the tradeoff between exploration and exploitation.
Axelrod makes an analogy with a hospital: "On average you can expect a new medical
drug to have a lower payoff than exploiting an established medication to its
limits. But if you gave every patient the current best drug, you'd never get
proven new drugs. From an individual's point of view you should never do the
exploration. But from the society of individuals' point of view, you ought to try
some experiments." How much to explore (gain for the future) versus how much to
exploit (sure bet now) is the game a hospital has to play. Living organisms have a
similar tradeoff in deciding how much mutation and innovation is needed to keep up
with a changing environment. When they play the tradeoff against a sea of other
creatures making similar tradeoffs, it becomes a coevolutionary game.
Axelrod's 14-player Prisoner's Dilemma round robin tournament was played on a
computer. In 1987, Axelrod extended the computerization of the game by setting up
a system in which small populations of programs played randomly generated
Prisoner's Dilemma strategies. Each random strategy would be scored after a round
of playing against all the other strategies running; the ones with the highest
scores got copied the most to the next generation, so that the most successful
strategies propagated. Because many strategies could succeed only by "preying" on
other strategies, they would thrive only as long as their prey survived. This
leads to the oscillating dynamics found everywhere in the wilds of nature; how fox
and hare populations rise and fall over the years in coevolutionary circularity.
When the hares increase the foxes boom; when the foxes boom, the hares die off.
But when there are no hares, the foxes starve. When there are less foxes, the
hares increase. And when the hares increase the foxes do too, and so on.
In 1990, Kristian Lindgren, working at the Neils Bohr Institute in Copenhagen,
expanded these coevolutionary experiments by increasing the population of players
to 1,000, introducing random noise into the games, and letting this artificial
coevolution run for up to 30,000 generations. Lindgren found that masses of dumb
agents playing Prisoner's Dilemma not only reenacted the ecological oscillations
of fox and hare, but the populations also created many other natural phenomenon
such as parasitism, spontaneously emerging symbiosis, and long-term stable
coexistence between species, as if they were an ecology. Lindgren's work excited
some biologists because his very long runs displayed long periods when the mix of
different "species" of strategy was very stable. These historical epochs were
interrupted by very sudden, short-lived episodes of instability, when old species
went extinct and new ones took root. Quickly a new stable arrangement of new
species of strategies arose and persisted for many thousands of generations. This
motif matches the general pattern of evolution found in earthly fossils, a pattern
known in the evolutionary trade as punctuated equilibrium, or "punk eek" for
One marvelous result from these experiments bears consideration by anyone hoping
to manage coevolutionary forces. It's another law of the gods. It turns out that
no matter what clever strategy you engineer or evolve in a world laced by
chameleon-on-a-mirror loops, if it is applied as a perfectly pure rule that you
obey absolutely, it will not be evolutionary resilient to competing strategies.
That is, a competing strategy will figure out how to exploit your rule in the long
run. A little touch of randomness (mistakes, imperfections), on the other hand,
actually creates long-term stability in coevolutionary worlds by allowing some
strategies to prevail for relative eons by not being so easily aped. Without
noise-wholly unexpected and out-of-character choices-the opportunity for
escalating evolution is lost because there are not enough periods of stability to
keep the system going. Error keeps the glue of coevolutionary relationships from
binding too tightly into runaway death spirals, and therefore error keeps a
coevolutionary system afloat and moving forward. Honor thy error.
Playing coevolutionary games in computers has provided other lessons. One of the
few notions from game theory to penetrate the popular culture was the distinction
of zero-sum and nonzero-sum games. Chess, elections, races, and poker are zero-sum
games: the winner's earnings are deducted from the loser's assets. Natural
wilderness, the economy, a mind, and networks on the other hand, are nonzero-sum
games. Wolverines don't have to lose just because bears live. The highly connected
loops of coevolutionary conflict mean the whole can reward (or at times cripple)
all members. Axelrod told me, "One of the earliest and most important insights
from game theory was that nonzero-sum games had very different strategic
implications than zero-sum games. In zero-sum games whatever hurts the other guy
is good for you. In nonzero-sum games you can both do well, or both do poorly. I
think people often take a zero-sum view of the world when they shouldn't. They
often say, 'Well I'm doing better than the other guy, therefore I must be doing
well.' In a nonzero-sum you could be doing better than the other guy and both be
Axelrod noticed that the champion Tit-For-Tat strategy always won without
exploiting an opponent's strategy-it merely mirrored the other's actions.
Tit-For-Tat could not beat anyone's strategy one on one, but in a nonzero-sum game
it would still win a tournament because it had the highest cumulative score when
played against many kinds of rules. As Axelrod pointed out to William Poundstone,
author of Prisoner's Dilemma, "That's a very bizarre idea. You can't win a chess
tournament by never beating anybody." But with coevolution-change changing in
response to itself-you can win without beating others. Hard-nosed CEOs in the
business world now recognize that in the era of networks and alliances, companies
can make billions without beating others. Win-win, the cliché is called.
Win-win is the story of life in coevolution.
Sitting in his book-lined office, Robert Axelrod mused on the consequences of
understanding coevolution and then added, "I hope my work on the evolution of
cooperation helps the world avoid conflict. If you read the citation which the
National Academy of Science gave me," he said pointing to a plaque on the wall,
"they think it helped avoid nuclear war." Although von Neumann was a key figure in
the development of the atom bomb, he did not formally apply his own theories to
the gamelike politics of the nuclear arms race. But after von Neumann's death in
1957, strategists in military think tanks began using his game theory to analyze
the cold war, which had taken on the flavor of a coevolutionary "obligate
cooperation" between two superpower enemies. Gorbachev had a fundamental
coevolutionary insight, says Axelrod. "He saw that the Soviets could get more
security with fewer tanks rather than with more tanks. Gorbi unilaterally threw
away 10,000 tanks, and that made it harder for US and Europe to have a big
military budget, which helped get this whole process going that ended the cold
Perhaps the most useful lesson of coevolution for "wannabe" gods is that in
coevolutionary worlds control and secrecy are counterproductive. You can't
control, and revelation works better than concealment. "In zero-sum games you
always try to hide your strategy," says Axelrod. "But in nonzero-sum games you
might want to announce your strategy in public so the other players need to adapt
to it." Gorbachev's strategy was effective because he did it publicly;
unilaterally withdrawing in secret would have done nothing.
The chameleon on the mirror is a completely open system. Neither the lizard nor
the glass has any secrets. The grand closure of Gaia keeps cycling because all its
lesser cycles inform each other in constant coevolutionary communication. From the
collapse of Soviet command-style economies, we know that open information keeps an
economy stable and growing.
Coevolution can be seen as two parties snared in the web of mutual propaganda.
Coevolutionary relationships, from parasites to allies, are in their essence
informational. A steady exchange of information welds them into a single system.
At the same time, the exchange-whether of insults or assistance or plain
news-creates a commons from which cooperation, self-organization, and win-win endgames can spawn.
In the Network Era-that age we have just entered-dense communication is creating
artificial worlds ripe for emergent coevolution, spontaneous self-organization,
and win-win cooperation. In this Era, openness wins, central control is lost, and
stability is a state of perpetual almost-falling ensured by constant error.