Self-Reinforcing Success Networks have their own logic.
When you connect all to all, curious things happen.
Mathematics says the sum value of a network increases as the square of
the number of members. In other words, as the number of nodes in a
network increases arithmetically, the value of the network increases
exponentially.* Adding a few more members can dramatically increase the
value for all members. *I use the
vernacular meaning of "exponential" to mean "explosive
compounded growth." Technically, n2 growth should be called
polynomial, or even more precisely, a quadractic; a fixed exponent (2 in
this case) is applied to a growing number n. True exponential growth in
mathematics entails a fixed number (say 2) that has a growing exponent,
n, as in 2n. The curves of some polynomials and exponentials look
similar, except the exponential is even steeper; in common discourse the
two are lumped together. This amazing boom is
not hard to visualize. Take 4 acquaintances; there are 12 distinct
one-to-one friendships among them. If we add a fifth friend to the
group, the friendship network increases to 20 different relations; 6
friends makes 30 connections; 7 makes 42. As the number of members goes
beyond 10, the total number of relationships among the friends escalates
rapidly. When the number of people (n) involved is large, the total
number of connections can be approximated as simply n 3 n, or n2. Thus
a thousand members can have a million friendships. The
magic of n2 is that when you annex one more new member, you add many
more connections; you get more value than you add. Thats not true
in the industrial world. Say you owned a milk factory, and you had 10
customers who bought milk once a day. If you increased your customer
base by 10% by adding one new customer, you could expect an increase in
milk sales of 10%. Thats linear. But say, instead, you owned a
telephone network with 10 customers who talked to each other once a day.
Your customers would make about n2 (102), or 100 calls a day. If you
added one more new customer, you increased your customer base by 10%,
but you increased your calling revenue by a whopping 20% (since 112 is
20% larger than 102). In a network economy, small efforts can lead to
large results.
A networks tendency to explode in value mathematically was first
noticed by Bob Metcalfe, the inventor of a localized networking
technology called Ethernet. During the late 1970s Metcalfe was selling a
combination of Ethernet, Unix, and TCP/IP (the internet protocol), as a
way to make large networks out of many small ones. Metcalfe says,
"The idea that the value of a network equals n squared came to me
after I failed to get networks to work on a small scale, despite many
repeated experiments." He noticed that networks needed to achieve
critical mass to make them worthwhile. But he also noticed that as he
linked together small local networks here and there, the value of the
combined large network would multiply abruptly. In 1980 he began
formulating his law: value 5 n 3 n.
In fact, n2 underestimates the total value of network growth. As
economic journalist John Browning notes, the power of a network
multiplies even faster than this. Metcalfes observation was based
on the idea of a phone network. Each telephone call had one person at
each end; therefore the total number of potential calls was the grand
sum of all possible pairings of people with phones. But online networks,
like personal networks in real life, provide opportunities for
complicated three-way, four-way, or many-way connections. You can not
only interact with your friend Charlie, but with Alice and Bob and
Charlie at the same time. The experience of communicating simultaneously
with Charlies group in an online world is a distinct experience,
separate in its essential qualities, from communicating with Charlie
alone. Therefore, when we tally up the number of possible connections in
a network we have to add up not only all the combinations in which
members can be paired, but also all the possible groups as well. These
additional combos send the total value of the network skyrocketing. The
precise arithmetic is not important. It is enough to know that the worth
of a network races ahead of its input.
This tendency of networks to drastically amplify small inputs leads to
the second key axiom of network logic: the law of increasing returns. In
one way or another this law undergirds much of the strange behavior in
the network economy. The simplest version goes like this: The value of a
network explodes as its membership increases, and then the value
explosion sucks in yet more members, compounding the result.
An old saying puts it succinctly: Them thats got shall get.
A new way of saying it: Networks encourage the successful to be yet more
successful. Economist Brian Arthur calls this effect "increasing
returns." "Increasing returns" he says, "are the
tendency for that which is ahead to get further ahead; for that which
loses advantage to lose further advantage."
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In networks, we find self-reinforcing virtuous circles. Each
additional member increases the network's value, which in turn attracts
more members, initiating a spiral of benefits. |
In the industrial economy success was self-limiting; it obeyed the law
of decreasing returns. In the network economy, success is
self-reinforcing; it obeys the law of increasing returns.
We see the law of increasing returns operating in the way areas such as
Silicon Valley grow; each successful new start-up attracts other
start-ups, which in turn attract more capital and skills and yet more
start-ups. (Silicon Valley and other high-tech industrial regions are
themselves tightly coupled networks of talent, resources, and
opportunities.)
At first glance the law of increasing returns may seem identical to the
familiar textbook notion of economies of scale: The more of a product
you make, the more efficient the process becomes. Henry Ford leveraged
his success in selling automobiles to devise more productive methods of
manufacturing cars. This enabled Ford to sell his cars more cheaply,
which created larger sales, which fueled more innovation and even better
production methods, sending his company to the top.
That self-feeding circle is a positive feedback loop. While the law of
increasing returns and the economies of scale both rely on positive
feedback loops, there are two key differences.
First, industrial economies of scale increase value gradually and
linearly. Small efforts yield small results; large efforts give large
results. Networks, on the other hand, increase value
exponentiallysmall efforts reinforce one another so that results
can quickly snowball into an avalanche. Its the difference
between a piggy bank and compounded interest.
Second, and more important, industrial economies of scale stem from the
herculean efforts of a single organization to outpace the competition by
creating value for less. The expertise (and advantage) developed by the
leading company is its alone. By contrast, networked increasing returns
are created and shared by the entire network. Many agents, users, and
competitors together create the networks value. Although the gains
of increasing returns may be reaped unequally by one organization, the
value of the gains resides in the greater web of relationships.
These positive feedback loops are created by "network
externalities." Anything that creates (or destroys) value which
cannot be appointed to someones account ledgers is an externality.
The total value of a telephone system lies outside the total internal
value of the telephone companies and their assets. It lies externally in
the greater phone network itself. Networks are particularly potent
sources of external value and have become a hot spot of economic
investigation in the last decade. A parade of recently published
academic papers scrutinize the fine points of network externalities:
When do they arise? How do they break down? Are they symmetrical? Can
they be manipulated?
One reason increasing returns and network externalities are garnering
attention is because they tend to create apparent monopolies. Huge
amounts of cash pour toward network winners such as Cisco or Oracle or
Microsoft, and that makes everyone else nervous. Are network
superwinners in fact monopolies? They are not like any monopolies of the
industrial age. When antitrust hearings are conducted today, the
witnesses are not customers angered by high pricing, haughty service, or
lack of optionsthe traditional sins of a monopolist. Customers
have nothing to complain about because they get lower prices, better
service, and more features from network superwinnersat least in
the short term. The only ones complaining about superwinners are their
competitors, because increasing returns create a winner-take-most
environment. But in the long term, the customer will have reason to
complain if competitors pull back or disappear.
The new monopolies are different in several ways. Traditional monopolies
dominated commodities. In the new order, as Santa Fe Institute economist
Brian Arthur points out, "Dominance may consist not so much in
cornering a single product as in successively taking over more and more
threads of the web of technology." Superwinners can practice a type
of crossover where control of one layer of the web leverages control
into others. Owning the standard for voice phone calls can ease the
likelihood of owning the standard for fax transmissions.
The unacceptable transgression of the traditional monopolist was that as
a mono-seller (thus the Greek, mono-polist), it could push prices up and
quality down. But the logic of the net inherently lowers prices and
raises quality, even those of a single-seller monopolist. In the network
economy, the unpardonable transgression is to stifle innovation, which
is what happens when competition is stifled. In the new order,
innovation is more important than price because price is a derivative of
innovation.
Mono-sellers are actually desirable in a network economy. Because of
increasing returns and n2 value, a single large pool is superior to many
smaller pools. The network economy will breed mono-sellers with great
fertility. What is intolerable in a network economy is
"monovation"depending upon a single source of
innovation. The danger of monopolists in the network economy is not that
they can raise prices but they can become monovationists. But there are
ways to encourage "polyvation"multiple sources of
innovationin a world of monopolists: by creating open systems, by
moving key intellectual properties into the public domain, by releasing
source code democratically. As we come to understand the importance of
increasing returns and the other new rules of the network economy, we
can expect shifts in our understanding of the role of market
winners.
Industrial monopolies exploited simple economies of scale for their own
benefit. Network effects are not about economies of scale, they are
about value that is created above and beyond a single
organizationby a larger networkand then returned to the
parts, often unevenly. Because some portion of the value of a network
firm so obviously comes from external sources, allegiance is often
granted to external sources. We see this in the way network
effects govern the growth of Silicon Valley. Silicon Valleys
success is external to any particular companys success, and so
loyalty is external, too. As AnnaLee Saxenian, author of Regional
Advantage, notes, Silicon Valley has in effect become one large,
distributed company. People job-hop so frequently that folks "joke
that you can change jobs without changing car pools. Some say they wake
up thinking they work for Silicon Valley. Their loyalty is more to
advancing technology or to the region than it is to any individual
firm."
This trend seems likely to extend further. We are headed into an era
when both workers and consumers will feel more loyalty to a network than
to any ordinary firm. The great innovation of Silicon Valley is not the
wowie-zowie hardware and software it has invented. Silicon Valleys
greatest "product" is the social organization of its companies
and, most important, the networked architecture of the region
itselfthe tangled web of former jobs, intimate colleagues,
information leakage from one firm to the next, rapid company life
cycles, and agile email culture. This social web, suffused into the warm
hardware of jelly bean chips and copper neurons, creates a true network
economy.
The social web, even in the Valley, displays some stress marks. There is
no question that the network economy is, at worst, winner-take-all, and
at best, winner-take-most. The trajectory of increasing returns and a
shortage of attention focuses success toward a few points. Stars and
hits rise, while the rest languish. Mundane appliances and bulky objects
now seem to follow the Hollywood model: A few brands sell like crazy,
and the rest sell only a few. Its a "hits" economy,
where resources flow to those that show some life. If a new novel, new
product, or new service begins to succeed it is fed more; if it falters,
its left to wither. Them that has, gets more.
The current great debate is whether the law of increasing returns favors
the early or not. In some of the first studies of increasing returns,
economist Brian Arthur discovered that when technological competitors,
such as the VHS and Betamax video formats, were modeled in a computer,
increasing returns favored one technology over the otherto the
eventual demise of the unfortunate one (in this case Betamax). And
"unfortunate" is the right word. According to Arthurs
research, the technology that came to dominate, thanks to increasing
returns, was not necessarily the superior one. It was just the lucky
one. Or the early one. Arthur writes: "If a product or a company or
a technologyone of many competing in a marketgets ahead by
chance or clever strategy, increasing returns can magnify this
advantage, and the product or company can go on to lock in the
market."
All things being equal, early success has a measurable advantage. But in
real life all things are rarely equal. Technologies which seem to be
inferior and yet prevail through the dynamics of increased returns often
reveal themselves under further study to be slightly superior in key
ways. The Sony Betamax format lost to VHS because it couldnt
record for as long as VHS could, and, according to some, because Sony
discouraged Beta use for pornoan early use of video. Apple
Computers superior operating system lost to Windows because Apple
had an inferior pricedue to its misguided monopolist strategy. The
supposedly ergonomic Dvorak keyboard lost to the all-too-familiar QWERTY
keyboard because the Dvorak layout really wasnt any faster.
Being first or best sometimes helps, but not always. The outcome of
competition in a network is not determined solely by the abilities of
the competitors, but by tiny differences, including luck, that are
greatly magnified by the power of positive feedback loops. The fate of
competition is "path dependent" on minor nudges and hurdles
that can "tip" the system in one direction or another. Final
destiny cannot be predicted on the basis of exceptional attributes
alone.
What can be predicted is the way in which networks enlarge small
advantages, and then lock the advantage in. In the same way, initial
parameters and conventions can quickly freeze into unalterable
standards. The solidifying standards of a network are both a blessing
and a cursea blessing because the ad hoc agreement reduces risk,
and thus sparks widespread progress, and a curse because those who own
or control the standard are disproportionately rewarded.
But the network economy doesnt allow the blessing without the
curse. Microsofts billions are tolerated (more or less) because so
many others in the network economy have made their collective billions
on the advantages of Microsofts increasing-returns standards.
We forget how recent and sudden Microsofts prominence is.
Microsoft is a textbook example of Metcalfes law ("The value
of Windows increases exponentially as its users increase
arithmetically") and the law of increasing returns ("The more
who use NT, the more attractive NT becomes"). Microsoft also
illustrates the third corollary of increasing returns: how small signals
can suddenly become booms.
During its first 10 years, Microsofts profits were negligible. Its
profits rose above the background noise of Wall Street only around 1985.
But once they began to rise, they exploded. A chart of Microsofts
cornucopia of profits is an exponentially booming curve, one that
parallels several other rising stars in the network economy.
Federal Express experienced a similar trajectory: years of minuscule
profit increases, slowly ramping up to an invisible threshold, and then
surging skyward in a blast sometime during the early 1980s.
The story of fax machines is likewise a tale of a 20-year-long overnight
success. After two decades of marginal success, the number of fax
machines quietly crossed the point of no return during the
mid-1980sand the next thing you know, they were everywhere.
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Network organizations experience small gains while thieir
network is being seeded. Once the network is established, explosive
growth follows with relatively little additional genius. |
The archetypal case of a success explosion in a network economy is the
Internet itself. As any proud old-time nethead will be happy to explain,
the internet was a lonely (but thrilling!) cultural backwater for two
decades before it showed up on the media radar. A graph of the number of
internet hosts worldwide, starting in the 1970s, stays barely above the
bottom line, until around 1991, when the global tally of hosts suddenly
mushroomed, exponentially acting upward to take over the world.
The curves of Microsoft, the internet, fax machines and FedEx (I owe
Net Gain author John Hagel credit for these four examples) are
templates of exponential growth, compounding in a biological way. Such
curves are almost the definition of a biological system. Thats one
reason the network economy is often described most accurately in
biological terms. Indeed, if the web feels like a frontier, its
because for the first time in history we are witnessing biological
growth in technological systems.
A good definition of a network is organic behavior in a technological
matrix.
The compounded successes of Microsoft, FedEx, fax machines, and the
internet all hinge on the prime law of networks: Value explodes
exponentially with membership, and this heightened value acts like
gravity drawing in yet more members. The virtuous circle inflates until
all potential members are joined.
This explosion, however, did not ignite until approximately the late
1980s. Two things happened thenthe dual big bangs of almost-free
jelly bean chips and collapsing telco charges. It became
feasiblethat is, dirt cheapto exchange data almost anywhere,
anytime. The net, the grand net, began to precipitate out of this
supersaturated solution. Network power followed.
One of the hallmarks of the industrial age was its reasonable
expectations. Success was in proportion to effort. Small effort, small
gains. Large effort, large gains. This linear ratio is typical of
capital investments and resource allotments. According to data from the
U.S. Statistical Abstract, the best-selling products in the
1950sappliances such as refrigerators, clocks and washing
machinessold steadily with only a slight 2% annual increase in the
number of units sold per year. To imagine the future of an enterprise or
innovation one needed only to extrapolate the current trends in a
straight line. There was a comfortable assumptionlargely
truethat the world proceeded linearly. Entirely new phenomenona
did not ordinarily appear out of nowhere and change everything within
months.
With the advent of large-scale electronic media networks in the mid
century, that assumption began to erode. Millions of kids watching TV
grew up to create rapid fads (hula hoops), instant youth cultures such
as the beats and hippies, with sudden spontaneous gatherings of half a
million, as at Woodstock. Events did not happen linearly. With media
networks it was no longer safe to extrapolate the future from the recent
past. When success came, it often fed on itself in crazy hyperkinetic
booms. The recent sales of electronic pets is one example. Tamagotchis,
the original brand of Japanese toy pets, went from sales of zero in
Japan to 10 million units in their first year, to 20 million by the
second year. When they were introduced in the United States a half
million units were sold in the first month. The Tamagotchis could be
actual breeding animals judging simply from their growth rate because
their sales curve follows the population curve of reproducing biological
animals. One day there are two pets, the next year there are 200. In
biological populations, success can easily compound into runaway growth;
now this wild runaway growth is happening with technology.
Everyday we see evidence of biological growth in technological systems.
This is one of the marks of the network economy: that biology has taken
root in technology. And this is one of the reasons why networks change
everything. Heres how this happened. Most of the
technology in the early part of the century was relegated to the inside
of a factory. Only businessmen cared about advancing
technologycheaper production methods or more specialized
materials. The consumer products this advanced technology spun off into
homes were, more often than not, labor-saving devicessewing
machines, vacuum cleaners, water pumps. They saved time, and thereby
enhanced the prevailing culture. But the devices themselves (except for
the automobile) were merely gadgets. They were
technologysomething foreign, best used in small doses, and
clearly not the social and economic center of our lives. It was once
very easy to ignore technology because it did not penetrate the areas of
our lives we have always really cared about: our networks of friendship,
writing, painting, cultural arts, relationships, self-identity, civil
organizations, the nature of work, the acquisition of wealth, and power.
But with the steady advent of technology into the networks of
communication and transportation, technology has completely overwhelmed
these social areas. Our social space has been invaded by the telegraph,
the phonograph, the telephone, the photograph, the television, the
airplane and car, then by the computer, and the internet, and now by the
web.
Technology has become our culture, our culture technology.
Technology is no longer outside, no longer alien, no longer at the
periphery. It is at the center of our lives. "Technology is the
campfire around which we gather," says musician/artist Laurie
Anderson. For many decades high tech was marginal in presence. Then
suddenlyblinkit is everywhere and all-important.
Technology has been able to infiltrate into our lives to the degree it
has because it has become more like us. Its become organic in
structure. Because network technology behaves more like an organism than
like a machine, biological metaphors are far more useful than mechanical
ones in understanding how the network economy runs.
But if success follows a biological model, so does failure. A cautionary
tale: One day, along the beach, tiny red algae suddenly blooms into a
vast red tide. A few weeks later, just when the red mat seems indelible,
it vanishes. Lemmings boom, then disappear as suddenly. The same
biological forces that multiply populations can decimate them. The same
forces that feed on one another to amplify network presences creating
powerful standards overnight can also work in reverse to unravel them in
a blink. The same forces that converge to build up organizations in so
biological a fashion can also converge to tear them down. One can expect
that when Microsofts fortunes falter, their profits will plunge in
a curve inversely symmetrical to their success. All the self-reinforcing
reasons to join a networks success run in reverse when the success
turns to failure and everyone wants to flee.
One more biological insight can be gleaned from the success of
Microsoft, FedEx, and the internet. In retrospect one can see that at
some point in their history the momentum toward them became so
overwhelming that success became a runaway event. Success became
infectious, so to speak, and spread pervasively to the extent that it
became difficult for the uninfected to avoid succumbing. Take the
arrival of the phone network. How long can you hold out not having a
phone? Only 6% of U.S. homes are still holding out.
In epidemiology, the point at which a disease has infected enough hosts
that it must be considered a raging epidemic can be thought of as the
tipping point. The contagions momentum has tipped from pushing
uphill against all odds to rolling downhill with all odds behind it. In
biology, the tipping points of fatal diseases are fairly high, but in
technology, they seem to be triggered at much lower points.
There has always been a tipping point in any business, industrial or
network, after which success feeds upon itself. However, the low fixed
costs, insignificant marginal costs, and rapid distribution that we find
in the network economy depresses tipping points below the levels of
industrial times; it is as if the new bugs are more contagiousand
more potent. It takes a smaller initial pool to lead to runaway
dominance, sooner.
Lower tipping points also mean that the threshold of
significancethe period before the tipping point during which a
movement, growth, or innovation must be taken seriouslyis also
dramatically lower than it was during the industrial age. Detecting
developments while they are beneath this threshold of significance is
essential.
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During the exponential gains peculiar to networks, compounding
effects can pass a point of runaway growth. But it is before this point,
before momentum builds, that one needs to pay attention. |
Major U.S. retailers refused to pay attention to TV home-shopping
networks during the 1980s because the number of people watching and
buying from them was initially so small and marginalized that it did not
meet the established level of retail significance. The largest U.S.
retailers work in the realm of hundreds of millions. The first TV home
shopping was dealing in the realm of thousands. Retailers discovered
that shoppers would watch 50 hours of home-shopping programs before
making their first purchase. The retailers considered this horrible
news. But it turns out "watching others do it" was an
initiation ritual. Shoppers trust other shoppers. Once shoppers were
"invested" in the process by watching many others do it
successfully, they kept coming back. So small numbers grew steadily and
then rapidly as more shoppers brought in yet more shoppers. Instead of
heeding the new subtle threshold of network economics, the retailers
waited until the alarm of the tipping point sounded, which meant, by
definition, that it was too late for them to cash in.
In the past, an innovations momentum indicated significance. Now,
in the network environment, where biological behavior reigns,
significance precedes momentum.
One final parable rooted in biology. In a pond one summer a floating
lily leaf doubles in size every day until it covers the entire surface
of water. The day before it completely covers the pond, the water is
only half covered, and the day before that, only a quarter covered, and
the day before that, only a measly eighth. While the lily grows
imperceptibly all summer long, only in the last week of the cycle would
most bystanders notice its "sudden" appearance. By then, it is
far past the tipping point.
The network economy is like a lily pond. Most of the pond looks empty,
but a few lilies are doubling in size. The web, for example, is a leaf
doubling every six months. Despite the one million web sites to date,
the webs future has just begun. Other lily leaves are sprouting
along the edges of the pond: MUDs, Irridium phones, wireless data ports,
collaborative bots, WebTV, and remote solid state sensors. Right now,
they are all just itsy-bitsy lily cells brewing at the beginning of a
hot network summer. One by one, they will pass their tipping points, and
suddenly become ubiquitous. Strategies
Check for externalities. The initial stages of exponential
growth looks as flat as any new growth. How can you detect significance
before momentum? By determining whether embryonic growth is due to
network effects rather than to the firms direct efforts. Do
increasing returns, open systems, n2 members, multiple gateways to
multiple networks play a part? Products or companies or technologies
that get slightly aheadeven when they are second bestby
exploiting the nets effects are prime candidates for exponential
growth. Coordinate smaller webs. The fastest way to
amp up the worth of your own network is to bring smaller networks
together with it so they can act as one larger network and gain the
total n2 value. The internet won this way. It was the network of
networks, the stuff in between that glued highly diverse existing
networks together. Can you take the auto parts supply network and
coordinate it with the insurance adjusters network plus the garage
repair network? Can you coordinate the intersection of hospital records
with standard search engine technology? Do the networks of county
property deed databases, U.S. patents, and small-town lawyers have
anything useful in common? Three thousand members in one network are far
more powerful than one thousand members in three networks.
Create feedback loops. Networks sprout connections and
connections sprout feedback loops. There are two elementary kinds of
loops: Self-negating loops such as thermostats and toilet bowl valves,
which create feedback loops that regulate themselves, and
self-reinforcing loops, which are loops that foster runaway growth such
as increasing returns and network effects. Thousands of complicated
loops are possible using combinations of these two forces. When internet
providers first started up, most charged users steeper fees to log on
via high-speed modem; the providers feared speedier modems would mean
fewer hours of billable online time. The higher fees formed a feedback
loop that subsidized the providers purchase of better modems, but
discouraged users from buying them. But one provider charged less for
high speed. This maverick created a loop that rewarded users to buy
high-speed modems; they got more per hour and so stayed longer. Although
it initially had to sink much more capital into its own modem purchases,
the maverick created a huge network of high-speed freaks who not only
bought their own deluxe modems but had few alternative places to go at
high speed. The maverick provider prospered. As a new economy business
concept, understanding feedback is as important as
return-on-investment. Protect long incubations.
Because the network economy favors the nimble and quick, anything
requiring patience and slowness is handicapped. Yet many projects,
companies, and technologies grow best gradually, slowly accumulating
complexity and richness. During their gestation period they will not be
able to compete with the early birds, and later, because of the law of
increasing returns, they may find it difficult to compete as well.
Latecomers have to follow Druckers Rulethey must be ten
times better than what they hope to displace. Delayed participation
often makes sense when the new offering can increase the ways to
participate. A late entry into the digital camera field, for instance,
which offered compatibility with cable TV as well as PCs, could make the
wait worthwhile. Its a hits game for everyone.
In the network economy the winner-take-all behavior of Hollywood hit
movies will become the norm for most productseven bulky
manufactured items. Oil wells are financed this way now; a few big
gushers pay for the many dry wells. You try a whole bunch of ideas with
no foreknowledge of which ones will work. Your only certainty is that
each idea will either soar or flop, with little in between. A few
high-scoring hits have to pay for all the many flops. This lotterylike
economic model is an anathema to industrialists, but thats how
network economies work. There is much to learn from long-term survivors
in existing hits-oriented business (such as music and books). They know
you need to keep trying lots of things and that you dont try to
predict the hits, because you cant. Two economists
proved that hitsat least in show bizwere unpredictable. They
plotted sales of first-run movies between May 1985 and January 1986 and
discovered that "the only reliable predictor of a films box
office was its performance the previous week. Nothing else seemed to
matternot the genre of the film, not its cast, not its
budget." The higher it was last week, the more likely it will be
high this weekan increasing returns loop fed by word of mouth
recommendations. The economists, Art De Vany and David Walls, claim
these results mirror a heavy duty physics equation known as the
Bose-Einstein distribution. The fact that the only variable that
influenced the result was the result from the week before, means, they
say, that "the film industry is a complex adaptive system poised
between order and chaos." In other words, it follows the logic of
the net: increasing returns and persistent disequilibrium.
continue...
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