From the archive, originally posted by: [ spectre ]


“Mr. Heston recalls lunch breaks during the filming of ”Planet of the Apes.” Unable to remove their masks and get them back on in time for the afternoon shoot, actors ate with their appliances on, watching intently in mirrors so they wouldn’t spear themselves with forks and smoking cigarettes with long holders to prevent setting their heads on fire.

Even off duty there was no break from segregation and the class struggle. ”There’d be a kind of self-segregation,” Mr. Heston says. ”The gorillas would all eat at one table, the chimpanzees at another, the orangutans at another.”

Basic Geometry May Explain Segregation’s Intractability
by Peter Wayner

Computers and humans may not think alike, but is there a way for the
Spock-like logic of a computer program to reveal the basis for human

In the past, models have revealed startling insight into the basis for
racism, economic mobility, pollution, segregation and many large scale
social phenomena. This Thursday and Friday, the Brookings Institution
in Washington is running a colloquium aimed at examining the limits of
what these mechanized models of societies can reveal and debating what
can be learned in the future.

The researchers are, in essence, building computer simulations that
treat the people in society like windup toys with completely
predictable behavior. Then they start the computers running and watch
what happens when the windup toys bump into each other. Ideally, the
patterns will give some insight into how people behave.

Eliminating the problem may be harder than anyone might have imagined.
The simulated society becomes quickly segregated if people only move to
homes that have at least 13 percent of the neighbors from the same
group. The implications of this research may be wide-ranging. Many
Federal, state and local programs devote large amounts of money to
pushing for an integrated society where groups intermingle. While there
have been some successes, there have also been persistent and
frustrating failures. In some cities, for instance, schools are even
more segregated than before the system began expensive and
time-consuming experiments with projects liked forced busing.

Some, like economist Glenn Loury, the founding director of Boston
University’s Institute on Race and Social Division, are beginning to
question whether the money could be better spent on teachers, books and
infrastructure instead of pushing for a desegregated system. Associate
Justice Clarence Thomas has wondered why society implicitly assumes
that all-black institutions are somehow inferior.

The hurdles may be more difficult than previously imagined, according
to a CyberTimes segregation simulator, and may help explain why
American cities are so segregated after so much work and money has gone
into desegregating them. The people in the simulation quickly end up
segregated, even if they only require as little as 13 percent of their
neighbors to be from the same group. The simulator initially places
people from two groups (red and blue) randomly around a checkerboard.
At each turn, several people move to a new square at random, but only
settle down if there are enough neighbors of the same group. The
simulation lets you control the threshold that makes the decision. On a
checkerboard, each square has a maximum of eight neighbors. If the
threshold is set to zero, then people will move anywhere without regard
to the neighborhood. If it is set to two, then at least 25 percent of
their new neighbors (two out of eight) must come from the same group.

Running the simulator shows that people will quickly end up bunched
with members of their same group even when the threshold is as low as
25 percent or even 12.5 percent. This may seem surprising at first, but
it is a consequence of geometry. If people demand even one neighbor
from the same group, they must choose a spot next to another member of
the same group. When this decision is repeated over time by many
people, the groups grow into larger clusters. As the smaller clusters
disappear, the only viable choice is to move to the outskirts of one of
the large clusters. Once a society settles into two large clusters,
people can’t move outside. This simulation gives the user several
options for controlling the size of the neighborhood. It can either be
the immediate eight squares or the twenty-four that are closest. While
this changes the threshold, it does not change the ultimate effect. If
people worry about being too alone, segregation follows.

Thomas Schelling, a professor at the University of Maryland in College
Park, experimented with many different versions of the system, in some
cases using a checkerboard and others with a computer. He said that the
experiments have taught several lessons including just how low the
threshold can be.

Assessing the ultimate meaning of simulations like this may be
difficult, and the lessons they offer may be different from the
messages that people want to hear. Robert Axtell, a researcher at the
Brookings Institution, said, “It doesn’t mean that people are racist,
just because a system ends up segregated. People can have only modest
preferences for local segregation, but at a system level, it can have a
systematic effect that has a largely segregated outcome.”

He suggested that some of the most interesting research in the next
several years will surround stabilizing systems. For instance, he said,
“Is there a way to change the tension across the boundary [between
groups]? For instance, can you subsidize people to move or stay?”

Still, simulation is an imperfect science. These models leave out
questions of taste and feelings of history. It is not clear, however,
whether extra details make a model more accurate or merely muddy the
results. Axtell pointed out that one of the maxims of modeling is that
“You’re finished with your modeling work not when there’s nothing left
to be added, but when there’s nothing left to be taken away.”

The CyberTimes simulator shows one simple fact: If two groups of people
are placed in a city and people have only a moderate requirement about
finding a home with neighbors from the same group, it’s only a matter
of time before the society will become segregated. The unanswered
question is how to use this fact to society’s advantage.



Seeing Around Corners
By Jonathan Rauch | Apr 1, 2002

In about A.D. 1300 the Anasazi people abandoned Long House Valley. To
this day the valley, though beautiful in its way, seems touched by
desolation. It runs eight miles more or less north to south, on the
Navajo reservation in northern Arizona, just west of the broad Black
Mesa and half an hour’s drive south of Monument Valley. To the west
Long House Valley is bounded by gently sloping domes of pink sandstone;
to the east are low cliffs of yellow-white sedimentary rock crowned
with a mist of windblown juniper. The valley floor is riverless and
almost perfectly flat, a sea of blue-gray sagebrush and greasewood in
sandy reddish soil carried in by wind and water. Today the valley is
home to a modest Navajo farm, a few head of cattle, several electrical
transmission towers, and not much else.

Yet it is not hard to imagine the vibrant farming district that this
once was. The Anasazi used to cultivate the valley floor and build
their settlements on low hills around the valley’s perimeter. Remains
of their settlements are easy to see, even today. Because the soil is
sandy and the wind blows hard, not much stays buried, so if you leave
the highway and walk along the edge of the valley (which, by the way,
you can’t do without a Navajo permit), you frequently happen upon
shards of Anasazi pottery, which was eggshell-perfect and luminously
painted. On the site of the valley’s eponymous Long Housethe largest
of the ancient settlementsseveral ancient stone walls remain standing.

Last year I visited the valley with two University of Arizona
archaeologists, George Gumerman and Jeffrey Dean, who between them have
studied the area for fifty or more years. Every time I picked up a
pottery shard, they dated it at a glance. By now they and other
archaeologists know a great deal about the Anasazi of Long House
Valley: approximately how many lived here, where their dwellings were,
how much water was available to them for farming, and even (though here
more guesswork is involved) approximately how much corn each acre of
farmland produced. They have built up a whole prehistoric account of
the people and their land. But they still do not know what everyone
would most like to know, which is what happened to the Anasazi around
A.D. 1300.

“Really, we’ve been sort of spinning our wheels in the last eight to
ten years,” Gumerman told me during the drive up to the valley. “Even
though we were getting more data, we haven’t been able to answer that
question.” Recently, however, they tried something new. Unable to
interrogate or observe the real Long House Valley Anasazi, they set
about growing artificial ones.

Mr. Schelling’s Neighborhood

Growing artificial societies on computers in silico, so to speak,
requires quite a lot of computing power and, still more important,
some sophisticated modern programming languages, so the ability to do
it is of recent vintage. Moreover, artificial societies do not belong
to any one academic discipline, and their roots are, accordingly,
difficult to trace. Clearly, however, one pioneer is Thomas C.
Schelling, an economist who created a simple artificial neighborhood a
generation ago.

Today Schelling is eighty years old. He looks younger than his age and
is still active as an academic economist, currently at the University
of Maryland. He and his wife, Alice, live in a light-filled house in
Bethesda, Maryland, where I went to see him one day not long ago.
Schelling is of medium height and slender, with a full head of
iron-gray hair, big clear-framed eyeglasses, and a mild, soft-spoken
manner. Unlike most other economists I’ve dealt with, Schelling
customarily thinks about everyday questions of collective organization
and disorganization, such as lunchroom seating and traffic jams. He
tends to notice the ways in which complicated social patterns can
emerge even when individual people are following very simple rules, and
how those patterns can suddenly shift or even reverse as though of
their own accord. Years ago, when he taught in a second-floor classroom
at Harvard, he noticed that both of the building’s two narrow
stairwellsone at the front of the building, the other at the rearwere
jammed during breaks with students laboriously jostling past one
another in both directions. As an experiment, one day he asked his
10:00 A.M. class to begin taking the front stairway up and the back one
down. “It took about three days,” Schelling told me, “before the nine
o’clock class learned you should always come up the front stairs and
the eleven o’clock class always came down the back stairs”without, so
far as Schelling knew, any explicit instruction from the ten o’clock
class. “I think they just forced the accommodation by changing the
traffic pattern,” Schelling said.

In the 1960s he grew interested in segregated neighborhoods. It was
easy in America, he noticed, to find neighborhoods that were mostly or
entirely black or white, and correspondingly difficult to find
neighborhoods where neither race made up more than, say, three fourths
of the total. “The distribution,” he wrote in 1971, “is so U-shaped
that it is virtually a choice of two extremes.” That might, of course,
have been a result of widespread racism, but Schelling suspected
otherwise. “I had an intuition,” he told me, “that you could get a lot
more segregation than would be expected if you put people together and
just let them interact.”

One day in the late 1960s, on a flight from Chicago to Boston, he found
himself with nothing to read and began doodling with pencil and paper.
He drew a straight line and then “populated” it with Xs and Os. Then he
decreed that each X and O wanted at least two of its six nearest
neighbors to be of its own kind, and he began moving them around in
ways that would make more of them content with their neighborhood. “It
was slow going,” he told me, “but by the time I got off the plane in
Boston, I knew the results were interesting.” When he got home, he and
his eldest son, a coin collector, set out copper and zinc pennies (the
latter were wartime relics) on a grid that resembled a checkerboard.
“We’d look around and find a penny that wanted to move and figure out
where it wanted to move to,” he said. “I kept getting results that I
found quite striking.”

To see what happens in this sort of artificial neighborhood, look at
Figure 1, which contains a series of stills captured from a
Schelling-style computer simulation created for the purposes of this
article. (All the illustrations in the article are taken from animated
artificial-society simulations that you can view online, at You are looking down on an artificial
neighborhood containing two kinds of people, blue and red, withfor
simplicity’s sakeno blank spaces (that is, every “house” is occupied).
The board wraps around, so if a dot exits to the right, it reappears on
the left, and if it exits at the top, it re-enters at the bottom.

In the first frame blues and reds are randomly distributed. But they do
not stay that way for long, because each agent, each simulated person,
is ethnocentric. That is, the agent is happy only if its four nearest
neighbors (one at each point of the compass) include at least a certain
number of agents of its own color. In the random distribution, of
course, many agents are unhappy; and in each of many iterationsin
which a computer essentially does what Schelling and his son did as
they moved coins around their gridunhappy agents are allowed to switch
places. Very quickly (Frame 2) the reds gravitate to their own
neighborhood, and a few seconds later the segregation is complete: reds
and blues live in two distinct districts (Frame 3). After that the
border between the districts simply shifts a little as reds and blues
jockey to move away from the boundary (Frame 4).

Because no two runs begin from the same random starting point, and
because each agent’s moves affect every subsequent move, no two runs
are alike; but this one is typical. When I first looked at it, I
thought I must be seeing a model of a community full of racists. I
assumed, that is, that each agent wanted to live only among neighbors
of its own color. I was wrong. In the simulation I’ve just described,
each agent seeks only two neighbors of its own color. That is, these
“people” would all be perfectly happy in an integrated neighborhood,
half red, half blue. If they were real, they might well swear that they
valued diversity. The realization that their individual preferences
lead to a collective outcome indistinguishable from thoroughgoing
racism might surprise them no less than it surprised me and, many years
ago, Thomas Schelling.

In the same connection, look at Figure 2. This time the agents seek
only one neighbor of their own color. Again the simulation begins with
a random distribution (Frame 1). This time sorting proceeds more slowly
and less starkly. But it does proceed. About a third of the way through
the simulation, discernible ethnic clusters have emerged (Frame 2). As
time goes on, the boundaries tend to harden (Frames 3 and 4). Most
agents live in areas that are identifiably blue or red. Yet these
“people” would be perfectly happy to be in the minority; they want only
to avoid being completely alone. Each would no doubt regard itself as a
model of tolerance and, noticing the formation of color clusters, might
conclude that a lot of other agents must be racists.

Schelling’s model implied that even the simplest of societies could
produce outcomes that were simultaneously orderly and unintended:
outcomes that were in no sense accidental, but also in no sense
deliberate. “The interplay of individual choices, where unorganized
segregation is concerned, is a complex system with collective results
that bear no close relation to the individual intent,” he wrote in
1969. In other words, even in this extremely crude little world,
knowing individuals’ intent does not allow you to foresee the social
outcome, and knowing the social outcome does not give you an accurate
picture of individuals’ intent. Furthermore, the godlike outside
observerSchelling, or me, or youis no more able to foresee what will
happen than are the agents themselves. The only way to discover what
pattern, if any, will emerge from a given set of rules and a particular
starting point is to move the pennies around and watch the results.

Schelling moved on to other subjects in the 1970s. A few years later a
political scientist named Robert Axelrod (now at the University of
Michigan) used a computer simulation to show that cooperation could
emerge spontaneously in a world of self-interested actors. His work and
Schelling’s work and other dribs and drabs of research hinting at
simulated societies were, however, isolated threads; and for the next
decade or more the threads remained ungathered.

Sugarscape and Beyond

I have office space at The Brookings Institution, which is the oldest
of Washington’s think tanks. Since it is one of the more staid places
in town, it was probably inevitable that I would notice Joshua Epstein.
Epstein is tall and portly, with a wild tuft of graying hair above each
ear, a round face, and the sort of exuberant manner that brings to mind
a Saint Patrick’s Day parade more readily than a Washington think tank.
“No foam!” he roared, grinning, to a Starbucks server one day when we
went out for coffee. “Keep your damn foam!” Anyone who notices Epstein
is soon likely to encounter Robert Axtell, his collaborator and alter
ego. A programming wizard with training in economics and public policy,
Axtell is of medium height, quiet, and as understated as Epstein is
boisterous. When he speaks, the words spill out so quickly and
unemphatically that the listener must mentally insert spaces between

Epstein was born in New York City and grew up in Amherst,
Massachusetts. His father was a logician and a philosopher of science.
Nonetheless, Epstein never managed to finish high school. Instead he
got into college on a piano audition and, after composing a series of
chamber-music pieces, ended up switching to the study of mathematics
and political economy. That led to a Ph.D. in political science in 1981
and then a position at Brookings, plus the realization that he was
fascinated by mathematical models. One day in the early 1990s, when he
was giving a talk about his model of arms races, he met Axtell, who was
then a graduate student. He wound up bringing Axtell to Brookings, in

Not long after, Epstein attended a conference at the Santa Fe
Instituterenowned as a pioneering center for research on “complexity,”
the generation of spontaneous order and intricate patterns from
seemingly simple rules. At Santa Fe just then a big subject was
artificial life, often called A-life. “All of the work was about coral
reefs, ecology, growing things that look like trees, growing things
that look like flocks of birds, schools of fish, coral, and so on,”
Epstein told me. “And I thought, jeez, why don’t we try to use these
techniques to grow societies?” Fired up, he returned to Brookings and
discussed the idea with Axtell.

There followed the inevitable napkin moment, when the two of them sat
in the cafeteria and sketched out a simple artificial world in which
little hunter-gatherer creatures would move around a landscape finding,
storing, and consuming the only resource, sugar. When they brought
Sugarscape, as they called it, to life with the computer, they were
startled to see that almost immediately their rudimentary A-society
produced a skewed distribution of sugar that looked very much like the
skewed distribution of wealth in human societies, even though nothing
about the agents’ simple behavioral rules pointed to any such outcome.
For several years they built up and elaborated Sugarscape, and
discovered that simple rules could produce complex social phenomena
that mimicked migrations, epidemics, trade. “Every time we build one of
these things, it does some shocking thing,” Epstein told me. “You can
make it as simple as you want, and it will do something surprising,
almost certainly.”

Epstein and Axtell then began applying their technique, which they
called agent-based modeling, to a variety of problems and questions,
and as they did so they quietly inverted a number of tenets of the more
conventional varieties of social modeling. In Sugarscape, and in the
other artificial societies that followed, Epstein and Axtell made their
agents heterogeneous. That is, the artificial people, like real people,
were different from one another. Each Sugarscape agent has its own
“genetic code”: a distinctive combination of metabolic rate (how much
sugar each agent needs in order to stay alive), vision (how far the
agent can “see” as it hunts for sugar), and so forth. This was a small
move that was actually quite radical, and not just because of the
daunting computational requirements. In most conventional
social-science models people are assumed to be more or less the same:
multiple copies of a single representative person. Even in Thomas
Schelling’s artificial neighborhood all the agents are alike except in
color. Moreover, conventional models tend to assume that all their
clonelike individuals have complete or near complete knowledge of their
world. In Schelling’s model unhappy agents, like the modeler himself,
could survey the whole scene to find a better situation. In ordinary
economic models, by the same token, people all see essentially the same
big picture, so if a stock is underpriced, for example, traders will
quickly spot the anomaly. Epstein and Axtell instead built models in
which agents’ vision and knowledge were limited; agents knew only what
was going on nearby or what they “heard” from their “friends” (often a
unique social network was assigned to every agent). Each agent,
therefore, had unique preferences and unique knowledge.

It took me a little while to understand why in some respects this is a
whole new ball game. In years of writing on economics I had grown
comfortable with the sort of equation-based modeling that is common
and, unquestionably, indispensable in the social sciences. The modeler
looks at social patterns in the real world and tries to write equations
that describe what’s going on. The modeler, that is, views the world
from on high and attempts to fit it to regular lines and curves, which
are then used to make predictions. A simple and elegant artificial
society created by Ross Hammond brought home to me what I had been

Hammond is well over six feet tall and reed thin, with a broad forehead
and a pointed chin that make his face a neat triangle. When I met him,
last year, he worked as an assistant to Epstein and Axtell (he has
since moved on to graduate school at the University of Michigan), but
he originally devised his world in 1999, for a senior thesis at
Williams College. He decided to make an abstract model of social
corruption. He created an artificial world populated with two kinds of
agents: citizens and bureaucrats. Each of these agents has his own
susceptibility to corruption and his own network of friends. Every time
a citizen meets a bureaucrat, the two conduct a transaction. If they
collude corruptly, both pocket a nice kickback, whereas if both behave
honestly, neither gets payola. If a mismatch occurs, and only one agent
is willing to cheat, the honest agent “reports” the corrupt one to an
unseen policing authority.

So far the setup is conventional game theory. Less conventional is
this: no agent knows exactly how many reports of corruption will land
him in jail, or how many other agents are honest or corrupt, or what
most other agents are doing. He knows only what has happened recently
to himself and his friends. If suddenly many of them land in jail, he
will assume that the cops are cracking down and will behave more
honestly until the coast looks clearer. (This excludes a sprinkling of
George Washingtonsagents who are incorruptibly honest.) The agents, in
other words, have varying personalities and limited information, and
they display what economists call “bounded rationality”that is, they
make the most rational choices they can based on that limited

Hammond had no idea what his stipulations would produce. Somewhat
surprisingly, he found that within many plausible ranges of corruption
payoffs, punishments, and agent characteristics, his artificial society
quickly settled down into rampant honesty. But there were some
plausible parameters (big payoffs and short jail terms) that produced a
truly startling result. To see it, look at Figure 3, below.

This shows Ross Hammond’s little A-society, a world of citizens
(bureaucrats are omitted for simplicity’s sake) who at any given moment
can be either corrupt, honest, or in jail. Schelling’s checkerboard
represented a physical space; the space in Figure 3, in contrast, is
purely abstract. Whether agents are near each other makes no
difference. What does matter is whether in any given transaction they
behave honestly or corruptly. A corrupt agent is a yellow rectangle, an
honest one blue, and a jailed one red. The population at any given
moment stretches along a thin horizontal ribbon one rectangle deep, so
the window actually portrays society over time. Thus a long vertical
blue bar represents a single agent who is incorruptible (a George
Washington), whereas an isolated blue rectangle represents an agent who
usually behaves corruptly but on that occasion chooses honesty.

At the top of the first frame, as the agents begin doing business, they
are randomly distributed. The field is almost entirely yellow, which
means that corruption is the norm. Only occasionally does a yellow
agent turn bluepresumably when a bunch of his friends have gone to
jail (the friends are not necessarily near him physically, and the
social networks are not displayed in this demonstration). Frame 2,
captured later, shows more of the same; in this society, clearly,
corruption pays and is the norm. Look closely, though, a little more
than halfway down Frame 2, and you may notice a vaguely horizontal
cluster of reds. Just randomly, in the course of things, there has been
a surge of agents going to jail. That turns out to be important for
reasons that become clearer when you look at Frame 3, captured later
still. Here, just above the bottom of the frame, an unusually large
number of agents are again being jailedand suddenly everyone turns
blue. This predominantly corrupt society has become uniformly honest.
But for how long? As the last frame shows, honesty is the new norm.
With everybody behaving honestly, there is no payoff for corruption
(payoff requires two corrupt dealers), so the A-society stays honest.
If the simulation continued running, it would show nothing but blue.

In the jargon, a dynamic system’s sudden shift from one kind of
behavior to another is typically referred to as “tipping” (and has been
since well before the term became a fashionable metaphor for sudden
change of whatever sort). Hammond’s little world, despite its almost
brutal simplicity, had tipped.

Hammond was astonished, so he ran the simulation again and again. No
two runs were the same, because each began from a different random
starting point, and no run was predictable in its details, because the
agents’ interactions, even in so simple a world, were unfathomably
complicated. Sometimes the A-society would tip from corrupt to honest
almost immediately; sometimes it would tip only after running for hours
on end; but always, sooner or later, it tipped. The switch appeared to
be inevitable, but its timing and the path taken to reach it were
completely unpredictable. What was going on?

Every so often, in the course of random events, a particularly large
number of corrupt agents, who happen to have particularly large
networks of friends who perhaps themselves have large social networks,
will be arrested. That, Hammond figures, has a doublebarreled effect:
it leads a lot of agents to notice that many of their friends are under
arrest, and it also increases the likelihood that they will encounter
an honest agent in the next transaction. Fearing that they will meet
their friends’ fate, the agents behave more honestly; and in doing so
they heighten yet further the odds that a corrupt agent will be nailed,
inspiring still more caution about corruption. Soonin fact, almost
instantlyso many agents are behaving honestly that corruption ceases
to pay, and everyone turns honest.

“There are plenty of different cities and countries that have gone from
a high degree of corruption to a low degree of corruption,” Hammond
says. His A-society suggests that in such a transition, the fear of
being caught may be at least as important as the odds of actually being
caught. To test that possibility, Hammond re-ran his simulation, but
this time he allowed all the agents to know not just how many of their
friends were in jail but how many people were jailed throughout the
whole society: in other words, the agents knew the odds of arrest as
well as the police did. Sure enough, fully informed agents never got
scared enough to reform. Hammond’s A-society seemed to have “grown” a
piece of knowledge that many law-enforcement agencies (think of the
Internal Revenue Service, with its targeted, high-profile audits) have
long intuitednamely, that limited resources are often more effectively
spent on fearsome, and fearsomely unpredictable, high-profile sweeps
than on uniform and thus easily second-guessed patterns of enforcement.

Hammond also wondered what would happen if he made all the agents
alike, instead of giving each a personality marked by a randomly varied
proclivity to cheat. What if, say, all agents preferred honesty exactly
half the time? The answer was that the A-society never made a
transition; it stayed corrupt forever, because everyone “knew” how
everyone else would behave. A social model that viewed individuals as
multiple copies of the same fully informed person could thus never
“see” the social transformation that Hammond found, for the simple
reason that without diversity and limited knowledge, the transformation
never happens . Given that human beings are invariably diverse and that
the knowledge at their disposal is invariably limited, it would seem to
follow that even societies in which unsophisticated people obey
rudimentary rules will produce surprises and discontinuitiesevents
that cannot be foreseen either through intuition or through the more
conventional sorts of social science.

Growing Zipf’s Law

Every so often scientists notice a rule or a regularity that makes no
particular sense on its face but seems to hold true nonetheless. One
such is a curiosity called Zipf’s Law. George Kingsley Zipf was a
Harvard linguist who in the 1930s noticed that the distribution of
words adhered to a regular statistical pattern. The most common word in
English”the”appears roughly twice as often in ordinary usage as the
second most common word, three times as often as the third most common,
ten times as often as the tenth most common, and so on. As an
afterthought, Zipf also observed that cities’ sizes followed the same
sort of pattern, which became known as a Zipf distribution.
Oversimplifying a bit, if you rank cities by population, you find that
City No. 10 will have roughly a tenth as many residents as City No. 1,
City No. 100 a hundredth as many, and so forth. (Actually the
relationship isn’t quite that clean, but mathematically it is strong
nonetheless.) Subsequent observers later noticed that this same Zipfian
relationship between size and rank applies to many things: for
instance, corporations and firms in a modern economy are

Nature is replete with such mysteriously constant statistical
relationships. “Power laws,” scientists call them, because the
relationship between size and rank is expressed as an exponent.
Earthquakes, for instance, follow Zipf-style power laws. Large
earthquakes are rare, small ones are common, and the size of each event
multiplied by its rank is a rough constant. In the 1980s scientists
began to believe that power-law relationships are characteristic of
systems that are in a state known as self-organized criticality, of
which the textbook example is a trickle of sand pouring onto a
tabletop. At first the sand merely piles up, but eventually it reaches
a point where any additional sand is likely to trigger an
avalancheoften very small, occasionally quite large. The sand pile now
maintains itself at a roughly constant height, and the overall
distribution of large and small avalanches follows a power law, even
though the size of any particular avalanche is always unpredictable.

That sand and other inanimate things behave in this way is interesting,
even striking. That human societies might display similar patterns,
however, is weird. People are (generally) intelligent creatures who act
deliberately. Yet their cities, for example, sort themselves out in a
mathematically regular fashion, a fact that I confirmed by glancing at
the World Almanac . In 1950 and 1998 the lists of the top twenty-five
cities in America were quite different, yet the cities’ relative sizes
were almost exactly the same. The biggest city (New York in both years)
was about four times as big as the fourth biggest (Los Angeles in 1950,
Houston in 1998), which was about three times as big as the sixteenth
biggest (New Orleans in 1950, Baltimore in 1998)not an exact fit, but
close. It was as though each city knew its permitted size relative to
all the others and modulated its growth to keep the relationships
constant. But, obviously, people moving to one city have not the
faintest notion how their movements will affect the relative sizes of
all cities. What might be going on? One plausible inference is that
societies are like sand piles: complex systems whose next perturbation
is unpredictable but whose behavior, viewed on a large scale and over
time, follows certain patternspatterns, moreover, that the individual
actors in the system (grains of sand, human beings) are quite unaware
of generating.

The day I started getting really excited about artificial societies was
the day Rob Axtell mentioned that he had created artificial companies
and cities, and that the companies and cities both followed Zipf’s Law.
According to Axtell, conventional economic theory has yet to produce
any accepted explanation for why the size distribution of firms or
cities follows a power law. Perhaps, Axtell thought, the trick is not
to explain Zipf’s Law but to grow it. He went to his computer and built
an artificial world of diverse agents ranging from workaholics to
idlers. Axtell’s workers start out self-employed but can organize
themselves into firms and job-hop, always in search of whatever
combination of money and leisure fits their temperament. When
individuals join forces to form companies, their potential productivity
rises, because of companies’ efficiency advantages. At the same time,
however, as each company grows larger, each agent faces a greater
temptation to slack off, collect the paycheck, and let colleagues carry
the load.

The resulting universe of A-firms, Axtell found, is like the sand pile,
full of avalanches small and large as firms form, prosper, grow lazy,
lose talent to hungrier firms, and then shrink or collapse. As in real
life, a few A-firms live and thrive for generations, but most are
evanescent, and now and then a really big one collapses despite having
been stable for years. Sometimes the addition of one slacker too many
can push a seemingly solid firm into instability and fission; but you
can’t be sure in advance which firm will crumble, or when.

In such a world you might expect no regularity at all. And yet, Axtell
told me, “The first time we turned it on, we got Zipf!” Despite the
firms’ constant churning, the distribution of large and small firms
maintained the same sort of mathematical regularity seen in real life.
Axtell and Richard Florida, a professor of regional economic
development at Carnegie Mellon University, took the logical next step
and built a model of cities, which were assumed to be basically
agglomerations of firms. Same result: with no tuning or tweaking, the
artificial cities unfailingly lined themselves up in a Zipf
distribution and then, as a group, preserved that distribution even as
particular cities grew and shrank in what looked to the naked eye like
random turmoil. “All of a sudden,” Florida told me, “I looked at Rob’s
model and it dawned on me. This creates the city system .” The
artificial cities and their artificial residents were all unknowingly
locked in a competition for talent, but they could retain only so much
of it before they lost ground relative to other clusters of talent.
Richard Florida, to whom the Zipf distribution of cities had previously
seemed a mere curiosity, infers that the Zipf relationship is much more
than a pretty anomaly or a statistical parlor trick. It bespeaks the
higher-order patterns into which human beings, and thus societies,
unconsciously arrange themselves.

Artificial Genocide

If societies can order themselves systematically but unconsciously, it
stands to reason that they can also disorder themselves systematically
but unconsciously. As societies, the Balkans, Rwanda, Indonesia, and
South Central Los Angeles have little in common, yet all have
experienced, in recent memory, sudden and shocking transformations from
a tense but seemingly sustainable communal peace to communal disorder
and violence. Obviously, riots in America are in no way morally
comparable to genocide in Rwanda, but what is striking in all these
cases is the abruptness with which seemingly law-abiding and peaceable
people turned into looters or killers. Scholars often use the metaphor
of contagion in talking and thinking about mass violence, because the
violence seems to spread so quickly from person to person and
neighborhood to neighborhood. Yet sociologists who have studied mass
behavior have learned that people in crowds and groups usually remain
rational, retain their individuality, and exercise their good judgment;
that is, they remain very much themselves. The illusion that some
larger collective mind, or some sort of infectious hysteria, has seized
control is just that: an illusion. Somehow, when communal violence
takes hold, individuals make choices, presumably responding to local
incentives or conditions, that make the whole society seem to have
suddenly decided to turn savage. Might it be that rampant violence is
no more the result of mass hysteria than the rampant segregation in
Thomas Schelling’s artificial neighborhood is the result of mass

Figure 4 shows Joshua Epstein’s artificial society containing two kinds
of people, blues and greens. As usual in Epstein’s models, each agent
has his own personalitythe relevant traits being, in this case, the
agent’s degree of privation or discontent, his level of ethnic
hostility, and his willingness to risk arrest when the police are
around. Also as usual, agents can “see” what is going on only in their
immediate neighborhoods, not across the whole society. The agents’
environment is one of ethnic tension between blues and greens; the
higher the tension, the more likely it is that the agents will, in
Epstein’s term, “go active”which in real life could mean looting a
neighbor’s store or seizing his house, but which in the current
instance will mean killing him. When an agent turns red, his discontent
or hatred has overcome his fear of arrest, and he has killed one
randomly selected neighbor of the other color. Those are the rules.
They are very simple rules.

In Figure 4 none of the agents are red. There is not enough ethnic
tension to inspire them to go active, so they coexist peacefully, and
indeed fill up the screen as their populations grow (they can
procreate). Between Frames 1 and 2 all that happens is that blues and
greens move around and occupy previously empty spaces. The situation
looks safe and stable, but it is not. In Figure 5, below, ethnic
tension has increased only slightly, but that increment has shifted the
society into a radically different state. In Frame 1 the randomly
distributed agents have set about killing one another, so their world
is awash with red dots. Shortly afterward, only a few seconds into the
simulation, the population has thinned dramatically (Frame 2), with
most of the agents who live in ethnically mixed zones having been
picked off. By Frame 3 blues and greens have separated, with violence
flaring along the borders and blues predominating.

Epstein has run this simulation countless times from different random
starting points, and it turns out that neither color enjoys an inherent
advantage: blues and greens are equally likely to prevail, with the
outcome depending on random local events that tilt the balance one way
or the other. No two runs are quite alike. But all are the same in one
respect: once a side has attained the upper hand, its greater numbers
allow it to annihilate the other side sooner or later. In Frame 4
greens are confined to a single ethnic enclave (the bottom of the frame
wraps around to join the top), where they huddle in beleaguered
solidarity as blues continue to nibble at them. The rest of the story,
in Frames 5 and 6, speaks for itself.

Epstein then added a third element, one that might be of special
interest to the United Nations: cops, or, if you prefer, peacekeepers.
In Figure 6, below, cops are represented by black dots. Like other
agents, they can “see” only in their immediate vicinity. Their rule is
to look around for active agents and put them in jail. The less
hotheaded agents will behave peaceably when a cop is nearby, so as to
avoid arrest. The result is a markedly different situation.

In Frame 1 agents and cops are scattered randomly, and the bolder
agents (in red) are setting upon their victims. When they commit murder
near a cop, the agents go to jail. Even so, the cops are initially
overwhelmed by the sheer quantity of violence, and in Frames 2 and 3 an
enclave of embattled greens forms, just as before. Now, however, there
is an important difference: the enclave is stable. Once it has dwindled
to a certain size, the cops are able to contain the violence by making
arrests along the border. As long as the cops stay in place, the
enclave is safe. But what if the cops are withdrawn? The result is
exactly the same as what happened when peacekeepers abandoned enclaves
in Bosnia and Rwanda. In Frame 4 the cops have all departed. Again,
Frames 5 and 6 speak for themselves.

I don’t think I’m alone in finding this artificial genocide eerie. The
outcome, of course, is chilling; but what is at least as spooky is that
such complicatedto say nothing of familiarsocial patterns can be
produced by mindless packets of data following a few almost
ridiculously simple rules. If I showed you these illustrations and told
you they represented genocide, you might well assume you were seeing a
schematic diagram of an actual event. Moreover, the model is designed
without any element of imitation or communication, so mass hysteria or
organized effort is literally impossible. No agent is knowingly copying
his peers or following the crowd; none is consciously organizing a
self-protective enclave. All the agents are separately and individually
reacting “rationally”according to rules, in any caseto local
conditions that the agents themselves are rapidly altering. As hotheads
begin to go active, the odds that any one misbehaving agent will be
arrested decline, emboldening more-timid agents nearby to act up,
reducing the odds of arrest still further, emboldening more agents, and
so on. As in real life, the violence, once begun, can spread rapidly as
cops are overwhelmed in one neighborhood after another. Although the
agents are atomized and disorganized, the violence is communal and
coherent. It has form and direction and even a sort of malevolent

At a Brookings conference last year, where Epstein presented his
artificial genocide, Alison Des Forges was in attendance. Des Forges, a
senior adviser to Human Rights Watch Africa, is one of the world’s
leading authorities on the Rwandan genocide of 1994. After the session
I asked her what she made of Epstein’s demonstration. Neither she nor
anyone else, Epstein included, believes that an array of little dots
explains the Rwandan cataclysm or any other real-world event; the very
notion is silly. What the simulation did suggest to Des Forges is that
disparate social breakdowns, in widely separated parts of the world,
may have common dynamicslinking Rwanda, for instance, to other horrors
far away. She also told me that Epstein’s demonstration reminded her of
Hutu killers’ attack on Tutsis who had gathered on a Rwandan hilltop:
the torches, the fires, the killing working its way up the hill.


In 1994 Epstein went back to the Santa Fe Institute, this time to
lecture on Sugarscape. He told me, “I came to a run in the Sugarscape
that we called the Protohistory, which was really this made-up toy
history of civilization, where it starts with some little soup of
agents and they go to peaks on the Sugarscape and coalesce into tribes
and have lots of kids and this forces them down in between the peaks
and they smash into the other tribe and they have all this assimilation
and combat and all this other stuff. And I showed that toy history to
this typically unlikely Santa Fe collection of archaeologists and
biologists and physicists, and I said, ‘Does this remind anyone of
anything real?’ And a hand shot up, and it was George Gumerman’s hand.
I had never met George. And he said, ‘It reminds me of the Anasazi.’ I
said, ‘What the heck is that?’ And he told me the story of this tribe
that flourished in the Southwest and suddenly vanished. And why did
they suddenly vanish? I thought, That’s a fascinating question.”

The greatest challenge for A-society researchers is to show that their
wind-up worlds bear on anything real. Epstein asked Gumerman if he had
data on the Anasazi, and Gumerman replied that there were lots of data,
data covering a span of centuries and recording, year by year,
environmental conditions, settlement patterns, demographic trends, and
more. “I thought, jeez,” Epstein says, “if there’s actual data, maybe
we can actually reconstruct this civilization computationally. I came
back all excited and told Rob. We built this terrain in a computer and
we literally animated this entire history, looking down on it as if it
were a movie. We said, Okay, that’s what really happened. Let’s try to
grow that in an agent-based model. Let’s create little cyber-Anasazi
and see if we can equip them with rules for farming, moving, mating,
under which you just leave them alone with the environment changing as
it truly did, and see if they reproducegrowthe true, observed

Gumerman and Jeffrey Dean (and several other scholars who joined in the
effort) were equally interested, for reasons of their own. Some
scholars believed that drought and other environmental problems caused
the Anasazi to leave; others blamed marauders or internecine warfare or
disease or culture, as well as drought. The argument had waxed and
waned ever since the 1920s. “We’ve thought the environment was
important,” Gumerman told me, “and other archaeologists said they
didn’t think it was that important, and that’s been the level of
argument until now.” The prospect of growing artificial Anasazi in
cyberspace suggested a new way to get some traction on the question.

So they created a computerized replica of the Long House Valley
environment from A.D. 800 to A.D. 1350 and populated it with agentsin
this case, digital farmers. Each agent represents a household and is
given a set of what the scholars believed to be realistic attributes:
family size, life-spans, nutritional needs, and so on. Every year each
artificial household harvests the corn on its land during the growing
season and draws down its stocks in the winter. If a household’s land
produces enough corn to feed the family, the family stays and farms the
same land again the next year; if the yield is insufficient, the family
moves to the nearest available plot that looks promising and tries
again; if the family still cannot eke out sustenance, it is removed
from the simulation. I have simplified the parameters, which allow for
the formation of new households, the birth of children, and so on.
Still, the rules are fairly straightforward, basically directing the
artificial Anasazi to follow the harvest and to leave or die off if the
land fails to support them.

To see what happens, look at Figure 7. You are looking down, as if from
a helicopter, on paired images of Long House Valley starting in the
year 800. Within the valley blue zones represent places where water is
available for farming (darker blue means more water). In both images
the red circles represent Anasazi settlements. Butthe crucial
differencethe right-hand image shows where real Anasazi settlements
were, whereas the left-hand one shows where cyber-Anasazi settled.

As always, no two simulations are alike; but once again, this one is
pretty typical. In the first frame, as the simulation begins, both the
real and the artificial populations are sparse, but the settlements’
locations have little in commonto be expected, since this simulation
begins randomly. In Frames 2 and 3 (A.D. 855 and A.D. 1021) the real
Anasazi population grows and spreads to farmland in the south of the
valley; the artificial population also grows and spreads, but with a
considerable lag, and the cyber-settlements are more likely than real
ones to cling to the edges of fertile zones. Nonetheless, by 1130
(Frame 4) the real and artificial populations look strikingly similar,
except that the artificial farmers appear to have overlooked some
desirable land in the extreme south. By 1257 (Frame 5) the real
population is well along in its decline, and the virtual one continues
to track it. (Note that reality and simulation agree that by this point
the southern portion of the valley supports only one family, though
they disagree about where that family lived.) But in Frame 6, at the
end of the period, real history and cyber-history have diverged: the
real Anasazi have vanished, whereas several families hang on in the

What does all this tell us? Nothing for certain; but it suggests two
things. First, environmental conditions alone can indeed explain much
of what is known about Anasazi population and settlement patterns.
Differences between reality and simulation are many; still, given the
relative simplicity of the rules and the fact that all but
environmental factors are excluded, what is remarkable is how much the
simulation manages to look like the real thing. But, second,
environmental hardship does not, at least in this model, explain the
final disappearance. A steep decline, yes; but a small population could
have stayed. Perhaps some unknown force drove them out; or perhaps,
more likely, the last few gave up and chose collectively to leave; or
perhaps there is a turning point that this first, still relatively
crude model has not found.

Even if the modelers fail to explain why the Anasazi left, they will
have shown that artificial societies can come within hailing distance
of replicating, in a general but suggestive way, the large trends of
real societies, and even some of the smaller trends. In Long House
Valley, Gumerman and Dean led me up a sandstone slope to the site of
the ancient Long House settlement. Gumerman planted himself in the
midst of the ruin and put his arms out and shouted, over an icy morning
wind that lashed the valley in early spring, “It boggles the mind. More
than half the simulations produce the biggest site right herewhere the
biggest site actually was.”

Learning From Lumpiness

There is no such thing as society,” Margaret Thatcher famously said in
1987. “There are individual men and women, and there are families.” If
all she meant was that in a liberal democracy the individual is
sovereign, then she was right. But if she also meant that, as some
conservatives believe, the notion of a capital-S Society is a
collectivist fiction or a sneaky euphemism for the nanny state, then it
appears that she was demonstrably wrong; and the artificial societies I
have shown you are the demonstrations. They are, it is true, almost
laughably simple by comparison with real people and real societies, but
that is exactly the point. If even the crudest toy societies take on a
life and a logic of their own, then it must be a safe bet that real
societies, too, have their own biographies. Intuition tells us that it
is meaningful to speak of Society as something greater than and
distinct from the sum of individuals and families, just as it is
meaningful to speak of the mind as something greater than and distinct
from the sum of brain cells. Intuition appears to be correct.

That, however, should not provide a lot of comfort to liberals and
progressives. They like the idea of Society because it is not an It but
an Us, a group project. For them, Society can be built like a house, or
guided like a child, by a community of enlightened activists and
politicians who use their own intuition as a blueprint. Artificial
societies suggest that real ones do not behave so manageably. Their
logic is their own, and they can be influenced but not directed,
understood but not anticipated. Not even the Olympian modeler, who
writes the code and looks down from on high, can do more than guess at
the effect of any particular rule as it ricochets through a world of
diverse actors. The diversity of individuals guarantees that society
will never be remotely as malleable or as predictable as any person.

Assimilating this style of thinking took me a while, but then I began
seeing human society as both more complicated and less strange than
before. Many of the seminal changes in American life have been
characterized by the sorts of abrupt discontinuities and emergent
patterns that also characterize artificial societies. Why, after
twenty-five years of rapid growth, did productivity in America suddenly
shift to a dramatically lower gear in the early 1970s? That event,
probably more than any other, shaped the discontents of the 1970s and
the political and social changes that followed, yet conventional
economics still has not mustered an accepted explanation. Why did the
homicide rate in New York City, after more than a century of relative
stability at a remarkably low level, quadruple after 1960? Why did the
rate of violent crime in America as a whole triple from 1965 to 1980?
Why did the percentage of children born out of wedlock quadruple from
1965 to 1990? Why did crack use explode in the 1980s and then collapse
in the 1990s? If we think of societies in terms of straight lines and
smooth curves, such landslides and reversals seem mystifying, bizarre;
if we think in terms of sand piles and teeming cyber-agents, it seems
surprising if avalanches do not happen.

Washington, D.C., is a place deeply committed to linearity. Want to cut
crime in half? Then double the number of cops or the length of prison
sentences. That is how both Washington and the human brain are wired to
think. Yet in recent years many people even in Washington have come to
understand that something is amiss with straight-line or smooth-curve
thinking. In fact, the notion of unintended consequences has become
almost a cliché. Policy measures sometimes work more or less as
expected, but often they misfire, or backfire. So far the trouble has
been that the idea of unintended consequences, important and well
founded though it may be, is an intellectual dead end. Just what is one
supposed to do about it? One cannot very well never do anything (which,
in any case, would have unintended consequences of its own), and one
also cannot foresee the unforeseeable. And so Washington shuffles along
neurotically in a state of befuddled enlightenment, well aware of the
law of unintended consequences but helpless to cope with it.

It is at least possible that with the development of artificial
societies, we have an inkling of an instrument that can peer into the
black box of unintended consequences. That is not to say that
A-societies will ever predict exact events and detailed outcomes in
real societies; on the contrary, a fundamental lesson of A-societies
seems to be that the only way to forecast the future is to live it.
However, A-societies may at least suggest the kinds of surprises that
could pop up. We won’t know when we will be blindsided, but we may well
learn which direction we are most likely to be hit from.

Moreover, A-societies may also eventually suggest where to look for the
sorts of small interventions that can have large, discontinuous
consequences. “It may be that you could learn of minimally costly
interventions that will give you a more satisfactory outcome,” Thomas
Schelling told meinterventions not unlike his trick of reordering the
traffic flow in Harvard’s stairwells by changing the behavior of a
single class. I used to think that the notion of government funding for
late-night basketball was silly, or at best symbolic. In fact it may be
exactly the right approach, because pulling a few influential boys off
the streets and out of trouble might halt a chain reaction among their
impressionable peers. It now seems to me that programs like President
Clinton’s effort to hire 100,000 additional police officers and spread
them in a uniform film across every jurisdiction are the gestural,
brain-dead ones, because they ignore the world’s lumpiness.
Increasingly, cops themselves are coming to the same conclusion. More
than a few cities have learned (or relearned) that pre-emptively
concentrating their efforts on key areas and offenders can dramatically
reduce crime across an entire city at comparatively little cost.

The flip side of learning to find small interventions with large
returns, and at least as important, is learning to avoid large
interventions with small returns. In the stretches between avalanches
and other discontinuities, A-societies are often surprising not by
being capricious but by being much more stable than intuition would
suggest. For example, in his model of communal violence Epstein tried
adding more and more artificial peacekeepers to see how many were
necessary to reliably prevent genocide. The result was disconcerting,
to say the least. Even saturating the population with peacekeepersone
for every ten civiliansdid not significantly reduce the odds that
genocide would ultimately occur; it merely delayed the end. Why?
Epstein’s artificial peacekeepers are passive, reacting to nearby
violence rather than striking pre-emptively; eventually a rash of
clustered killings will always overwhelm their ability to respond, at
which point the violence quickly gets out of hand. Epstein concludes
that simply throwing forces at an ethnic conflict is no answer;
intervention needs to anticipate trouble. That, of course, would not
have come as news to the reactive and largely ineffective peacekeeping
forces in, say, Rwanda, Bosnia, or Sierra Leone. In Rwanda frustrated
peacekeepers pleaded for permission to seize arms caches and intimidate
extremists before large-scale killing could begin. Their pleas were
denied, at a cost apparent in Figure 6. (See “Bystanders to Genocide,”
by Samantha Power, September 2001 Atlantic .)

The science of artificial societies is in its infancy. Whether toy
genocides will truly be relevant to real ones remains an open question.
But the field is burgeoning, and a lot is going on, some of which will
bear fruit. Researchers are creating cyber-models of ancient Indians of
Colorado’s Mesa Verde and Mexico’s Oaxaca Valley; they are creating
virtual Polynesian societies and digital mesolithic foragers; they are
growing crime waves in artificial neighborhoods, price shocks in
artificial financial markets, sudden changes in retirement trends among
artificial Social Security recipients, and epidemics caused by
bioterrorism. At least two sets of researchers are growing artificial
polities in which stable political parties emerge spontaneously
(conventional political science has never satisfactorily explained why
political parties appear to be a feature of every democracy). To me,
the early results of this work suggest that social engineering can
never be as effective as liberals hope, but also that it need not be as
clumsy as conservatives insist.

Today’s universities and think tanks are full of analysts who use
multivariate equations to model the effects of changes in tax rates or
welfare rules or gun laws or farm subsidies; I can easily envision a
time, not long from now, when many of those same analysts will test
policy changes not on paper but on artificial Americas that live and
grow within computers all over the country, like so many bacterial
cultures or fruit-fly populations. The rise and refinement of
artificial societies is not going to be a magic mirror, but it promises
some hope of seeing, however dimly, around the next corner.

Computer animations of the artificial societies discussed in this
article can be viewed online, at


University of Maryland Economist Wins Nobel Memorial Prize in Economics

Thomas C. Schelling

COLLEGE PARK, Md. – University of Maryland economist Thomas C.
Schelling has won the 2005 Nobel Memorial Prize in Economics for his
work in game theory analysis. He shares the award with Robert J. Aumann
of Hebrew University in Jerusalem.

The Royal Swedish Academy of Sciences awarded the prize today to
Schelling and Aumann “for having enhanced our understanding of conflict
and cooperation through game-theory analysis.” More details are
available from

Schelling, emeritus distinguished university professor in the
Department of Economics and the School of Public Policy, has published
highly influential works in a number of areas including nuclear
proliferation and arms control, terrorism, organized crime, energy and
environmental policy, climate change and racial segregation. His work
on nuclear deterrence helped shape Cold War strategies. He joined the
University of Maryland faculty in 1990. Schelling’s Economics webpage
has a short biography. Prof. Schelling’s C.V. is also available.

“I’m deeply honored by this recognition,” Schelling says. “I’ve been
doing this for over 50 years and it’s hard to find a shorthand way to
describe my interests. But in my mind it all comes together, and what
links this work is my fascination with how people react to and
influence others – as individuals and as nations.”

Alice and Thomas Schelling

Schelling began his career in 1945, working for the U.S. Bureau of the
Budget, and later served as an advisor in the Truman administration. He
taught for many years at Yale and Harvard, and has been honored with
membership in the National Academy of Sciences, the Institute of
Medicine and as a fellow in the American Academy of Arts and Sciences.

“There is no higher recognition than the Nobel Prize, and there is no
greater distinction for the university than to have Nobel Laureates on
the faculty,” says University of Maryland President C.D. Mote Jr. “All
of us in the university community salute and celebrate his
achievement.” This is the third Nobel Prize awarded to a member of the
University of Maryland faculty.

Schelling’s Research

A major part of Schelling’s work has focused on arms control and
nuclear deterrence. In 1993, the National Academies honored him “for
his pioneering work in the logic of military strategy, nuclear war, and
arms races which has profoundly influenced our understanding of this
crucial subject.”

Schelling says he remains optimistic that an unwritten taboo against
the use of nuclear weapons may continue to hold, even amid the
pressures of nuclear proliferation, just as it has for 60 years. “My
main source of optimism is that the Soviet Union faced some of its
gravest challenges without ever resorting to nuclear weapons,” he says.
“It was not a foregone conclusion they would honor this nuclear taboo
with their backs to the wall.” Schelling has applied these same
principles in other contexts as well, for example, looking at the
strategy and tactics of bargaining and negotiation involved in
industrial and labor conflict. “Some people have described me as a game
theorist, but this is wrong. I’m simply a user of game theory,”
Schelling says.

Another strand of his research has involved the interactive behavior of
crowds, ethnic groups, neighborhoods and entire populations, applied to
such topics as segregation and integration. “For his entire career, Tom
Schelling has been at the forefront in advancing our understanding of
risk and uncertainty in topics ranging from climate change to arms
control,” says Edward Montgomery, dean of the University of Maryland
School of Behavioral and Social Sciences. “His work has had a
revolutionary impact on our thinking and practice.”

“I don’t know anyone more deserving,” says Steve Fetter, dean of the
Maryland School of Public Policy. “Tom is an extraordinarily creative
and penetrating thinker who has made major contributions in a wide
range of fields, from nuclear strategy and arms control to the
economics of climate change.”

CALLS FOR WITHDRAWAL,2763,1664215,00.html

Chris McGreal in Jerusalem
Saturday December 10, 2005
The Guardian

The following correction was printed in the Guardian’s Corrections and
clarifications column, December 15 2005

The report below includes the following quote from an Israeli writer
Shraga Elam: “Every person, including a Nobel prize laureate, is
entitled to his political views. But … it is not enough to say that
politics does not enter in to it. Can a racist or a Holocaust denier
receive the Nobel prize even if he is very talented in his scientific
field? Political views are relevant.” Mr Elam has asked us to make
clear that only the first sentence should have been attributed to him,
and the rest to Gideon Spiro, his colleague in promoting a petition
calling for this year’s award for economics to be withdrawn.

A group of Israeli intellectuals and activists has demanded that the
Nobel prize committee withdraw the award for economics to be made today
to an Israeli mathematician and his American colleague on the grounds
that they are “warmongers”.

The economics prize is to be presented to Robert Aumann of Hebrew
University in Jerusalem and Thomas Schelling of Maryland University in
recognition of their “having enhanced our understanding of conflict and
cooperation through game-theory analysis”, a mathematical study of how
individuals and governments react to other people’s actions including
in war.

The awarding of Nobel peace prizes is often controversial but it is
rare for the scientific laureates to generate significant opposition.
However, a petition to the Royal Swedish Academy of Sciences signed by
about 1,000 intellectuals and academics from Israel, Europe and America
describes the awarding of this year’s prize to the two professors as

The critics accuse Professor Aumann – a member of the hawkish
thinktank, Professors for a Strong Israel, which believes the Jewish
state should retain the occupied territories – of using his
mathematical theories to promote his political views. “Aumann uses his
analysis to justify the Israeli occupation and the oppression of the
Palestinians,” the petition says.

It describes Professor Schelling’s theories as directly inspiring the
US military strategy in Vietnam, including the indiscriminate bombing
of civilians. “This strategy resulted in 2 million civilian deaths and
was a complete failure in realising its objectives,” the petition says.

“Neither of these individuals has contributed anything that improves
the human condition; rather, they have contributed to the misery of
millions.” The petition is signed by Israeli peace campaigners,
economists, academics, Holocaust survivors and leftwing politicians.
Signatories from about 50 other countries, including the US and several
Arab states, have also supported it. Those from Britain include
academics at several universities, members of groups such as Jews
against Zionism, and activists in the Respect party.

Shraga Elam, an Israeli writer among those behind the petition,
concedes that his objection is to Prof Aumann’s political views and not
to the quality of the analysis on game theory. “Every person, including
a Nobel prize laureate, is entitled to his political views,” he said.
“But … it is not enough to say that politics does not enter in to it.

“Can a racist or a Holocaust denier receive the Nobel prize even if he
is very talented in his scientific field? Political views are
relevant.” Prof Aumann, who fled Nazi Germany in the 1930s and moved to
Israel in 1956, has described the removal of Jewish settlers from the
Gaza strip as an “expulsion” and described it as “immoral, inhuman and

In a recorded interview with an American website after winning the
Nobel prize, Prof Aumann said game theory showed that prime minister
Ariel Sharon’s withdrawal of Jewish settlers from the Gaza strip was a
mistake. “From a game theory point of view it was a very bad move. But
if I didn’t study game theory, I would also say the same thing.

“It was a bad move because it sends a signal to the other side that if
you apply enough pressure then we will respond in a way that you’re
applying pressure. It’s a bad move theoretically. It sends the wrong
signal,” he said. In another interview he was asked if, according to
his theory, he foresaw an end to the conflict in the near future. “It’s
been going on for at least 80 years and as far as I can see it is going
to go on for at least another 80 years. I don’t see any end to this
one, I’m sorry to say,” he said.

The petition calls on the Swedish Academy to withdraw the prize. “You
should reverse your decision to reward Professors Schelling and Aumann.
We request that you find people who have truly advanced the health and
welfare of humanity, as has always been the intention of the Nobel
prize,” it says. The Swedish academy responded to the campaign by
saying it “makes its decisions based on the quality of the scientific

Prof Aumann, who was travelling to accept the award, was not
immediately available for comment but he was asked about the petition
at a recent press conference and said he would not dignify the petition
by speaking about it.