WISDOM of Crowds (Iden?ify -Uare the missing data)
Immortalist
by canvassing or examining groups in detail (the larger the better) of
reasonably informed, unbiased, engaged people. The group's answer is
almost invariably much better than any individual expert's answer, even
better than the best answer of the experts in the group.
The reason for this superiority is that each individual brings to the
problem some valuable unique knowledge or perspective, and any errors
in that knowledge or perspective are balanced off against those of
others in the group, so the collective wisdom of the group is likely to
be extremely accurate, reliable, knowledgeable, and predictive. If
you're skeptical, please read the book -- Surowiecki presents dozens of
examples to support this thesis. The average prediction of one such
group, the Iowa Electronic Market, over the several months before the
election, was that Bush would win by a comfortable 3% margin and that
Republicans would make gains in both houses of Congress. They were
exactly right.
http://blogs.salon.com/0002007/categories/businessInnovation/2004/11/15.html
Immortalist
Lowest common denominator, and all that. So why did Mr. Surowiecki call
his book "The Wisdom of Crowds" (and yes, it is a direct allusion to
Charles Mackay's "Extraordinary Popular Delusions & The Madness of
Crowds")? Simply put, while not denying the madness of crowd model in
the right circumstances, it turns out that under other circumstances
the crowd is wise. Very wise. In fact, wiser, more insightful, more
accurate, more intelligent than any individual genius can hope to be.
That's a strong statement, but Surowiecki backs it up with example
after example to show what he means and why.
For example, note this quote:
"A classic demonstration of group intelligence is the
jelly-beans-in-the-jar experiment, in which invariably the group's
estimate is superior to the vast majority of the individual guesses.
When finance professor Jack Treynor ran the experiment in his class
with a jar that held 850 beans, the group estimate was 871. Only one of
the fifty-six people in the class made a better guess." This is only
one example of many in the book. No matter how knowledgeable the
individual observer, a group estimate, even a group composed on
non-experts, routinely trumps the individual in insight. Whether it
involves counting jelly beans, estimating the weight of an ox, or
assigning blame in the stock market to the correct company responsible
for the Challenger accident in 1986, the crowd gets it right faster and
more accurately than the individual expert. Note this other fascinating
example:
"In May 1968, the U.S. submarine Scorpion disappeared on its way back
to Newport News after a tour of duty in the North Atlantic. Although
the navy knew the sub's last reported location, it had no idea what had
happened to the Scorpion, and only the vaguest sense of how far it
might have traveled after it had last made radio contact. As a result,
the areas where the navy began searching for the Scorpion was a circle
twenty miles wide and many thousands of feet deep. You could not
imagine a more hopeless task. The only possible solution, one might
have thought, was to track down three or four top experts on submarines
and ocean currents, ask them where they thought the Scorpion was, and
search there. But...a naval officer named John Craven had a different
plan. "First, Craven concocted a series of scenarios -- alternative
explanations for what might have happened to the Scorpion. Then he
assembled a team of men with a wide range of knowledge, including
mathematicians, submarine specialists, and salvage men. Instead of
asking them to consult with each other to come up with an answer, he
asked each of them to offer his best guess about how likely each of the
scenarios was..Craven believed that if he put all the answers together,
building a composite picture of how the Scorpion died, he'd end up with
a pretty good idea of where it was...He took all the guesses, and used
a formula called Bayes's theorem to estimate the Scorpion's final
location..When he was done, Craven had what was, roughly speaking, the
group's collective estimate of where the submarine was.
"The location that Craven came up with was not a spot that any
individual member of the group had picked. In other words, not one of
the members of the group had a picture in his head that matched the one
Craven had constructed using the information gathered from all of them.
The final estimate was a genuinely collective judgment that the group
as a whole had made, as opposed to representing the individual judgment
of the smartest people in it. It was also a genuinely brilliant
judgment. Five months after the Scorpion disappeared, a navy ship found
it. It was 220 yards from where Craven's group said it would be."
Remarkable, right? Is it just in humans that we see this sort of
behavior? No. Consider how bees find good sources of nectar:
"They don't sit around and have a collective discussion about where
foragers should go. Instead, the hives sends out a host of scout bees
to search the surrounding area. When a scout bee has found a nectar
source that seems strong, he comes back and does a waggle dance, the
intensity of which is shaped, in some way, by the excellence of the
nectar supply at the site. The waggle dance attracts other forager
bees, which follow the first forager, while foragers who have found
less-good sites attract fewer followers and, in some cases, eventually
abandon their sites entirely. The result is that bee foragers end up
distributing themselves across different nectar sources in an almost
perfect fashion, meaning that they get as much food as possible
relative to the time and energy they put into searching. It is a
collectively brilliant solution to the colony's food problem. "What's
important, though, is the way the colony gets to that collectively
intelligent solution. It does not get there by first rationally
considering all the alternatives, and then determining an ideal
foraging pattern. It can't do this, because it doesn't have any idea
what the possible alternatives -- that is, where the different flower
patches -- are. So instead, it sends out scouts in many different
directions and trusts that at least one of them will find the best
patch, return, and do a good dance so that the hive will know where the
food source is."
Now we begin to see the secret to this group wisdom effect. The more
people involved (or the more bees), the greater the input from the
group as a whole and the more likely it is that the correct solution is
reached. That makes intuitive sense, for we all know that "two heads
are better than one." So that means instead of relying on one expert,
get a group of experts together, right? Wrong:
". . . [A] group made up of some smart agents and some not-so-smart
agents almost always did better than a group made up just of smart
agents. Diversity is, on its own, valuable, so that the simple fact of
making a group diverse make it better at problem solving. That doesn't
mean intelligence is irrelevant.. but it does mean that, on the group
level, intelligence alone is not enough, because intelligence alone
cannot guarantee you different perspectives on a problem.. Adding in a
few people who know less, but have different skills, actually improves
the group's performance." OK, now we're getting radical. A group of
experts and non-experts is better than just a group of experts, even if
the group size is the same? Surowiecki knows what you are thinking at
this point and addresses it:
"Again, this doesn't mean that well-informed, sophisticated analysts
are of no use in making good decisions. (And it certainly doesn't mean
you want crowds of amateurs trying to collectively perform surgery or
fly planes.) It does mean that however well-informed and sophisticated
an expert is, his advice and predictions should be pooled with those of
others to get the most out of him. (The larger the group, the more
reliable its judgment will be.) And it means that attempting to 'chase
the expert,' looking for the one man who will have the answers to an
organization's problem, is a waste of time." So don't worry, he's not
deprecating intelligence or expertise, and he acknowledges there are
obvious times when you do want the lone expert working on your problem,
especially if "your problem" is you need brain surgery. And Surowiecki
absolutely acknowledges the problems that can come from relying on the
crowd to achieve wisdom. But the principle upon which this book rests
is expressed simply thus:
"The idea of the wisdom of crowds is not that a group will always give
you the right answer but that on average it will consistently come up
with a better answer than any individual could provide." It's that
group experience that makes the difference. Expertise is needed, but
relying on expertise alone will leave you worse off than if you couple
expertise with diversity. Does that sound familiar? It should. It's the
Linux model for developing software, and it's the Groklaw model for
gathering legal news and insight. The more diverse the crowd, the
greater the chance that one of those waggling bees will stumble upon
the right answer, or the best answer. It works when looking for nectar,
and it works when submitting bug fixes and new features for Linux.
Notice what Surowiecki says about Linux:
"In the way it operates, in fact, Linux is not all that different from
a market.. Like a bee colony, it sends out lots of foragers and assumes
that one of them will find the best route to the flower fields. This
is, without a doubt, less efficient than simply trying to define the
best route to the field or even picking the smartest forager and
letting him go. After all, if hundreds or thousands of programmers are
spending their time trying to come up with a solution that only a few
of them are going to find, that's many hours wasted that could be spent
doing something else. And yet, just as the free market's ability to
generate lots of alternatives and then winnow them down is central to
its continued growth, Linux's seeming wastefulness is a kind of
strength (a kind of strength that for-profit companies cannot,
fortunately or unfortunately, rely on). You can let a thousand flowers
bloom and then pick the one that smells the sweetest. "So who picks the
sweetest-smelling one? Ideally the crowd would. But here's where
striking a balance between the local and the global is essential: a
decentralized system can only produce genuinely intelligent results if
there's a means of aggregating the information of everyone in the
system. Without such a means, there's no reason to think that
decentralization will produce a smart result. In the case of Linux, it
is the small number of coders, including Torvalds himself, who vet
every potential change to the operating-system source code. There are
would-be Linux programmers all over the world, but eventually all roads
lead to Linus."
So we see that wisdom from crowds comes as a result of certain
conditions. There are principles by which wisdom can come from the
crowd (as opposed to madness):
". . . the four conditions that characterize wise crowds: diversity of
opinion (each person should have some private information, even if it's
just an eccentric interpretation of the known fact), independence
(people's opinions are not determined by the opinions of those around
them), decentralization (people are able to specialize and draw on
local knowledge), and aggregation (some mechanism exists for turning
private judgments into a collective decision). If a group satisfies
those conditions, its judgment is likely to be accurate." What about
the opposite result, the one where crowds are not wise and even dumb?
Under what circumstances do crowds go wrong and start to riot (or in
the case of online communities, start to turn on the community)?
Surowiecki discusses this as well, offering this analysis of what
causes a crowd to riot:
"The process by which a violent mob actually comes together seems
curiously similar to the way a stock-market bubble works. A mob in the
middle of a riot appears to be a single organism, acting with one mind.
And obviously the mob's behavior has a collective dimension that a
group of random people just milling about does not have. But
sociologist Mark Granovetter argued that the collective nature of a mob
was the product of a complicated process, rather than a sudden descent
into madness. In any crowd of people, Granovetter showed, there are
some people who will never riot, and some people who are ready to riot
at almost any time -- these are the 'instigators.' But most people are
somewhere in the middle. Their willingness to riot depends on what
other people in the crowd are doing. Specifically, it depends on how
many other people in the crowd are rioting. As more people riot, more
people decide that they are willing to riot, too... "This makes it
sound as if once one person starts a ruckus, a riot will inevitably
result. But according to Granovetter, that's not the case. What
determines the outcome is the mix of people in the crowd. If there are
a few instigators and lots of people who will act only if a sizable
percentage of the crowd acts, then it's likely nothing will happen. For
a crowd to explode, you need instigators, 'radicals' -- people with low
thresholds for violence -- and a mass of people who can be swayed. The
result is that although it's not necessarily easy to start a riot, once
a crowd crosses the threshold into violence, its behavior is shaped by
its most violent members. If the image of collective wisdom that
informs much of this book is the average judgment of the group as a
whole, a mob is not wise. Its judgment is extreme...
"There is, in Granobetter's work, a hint as to what markets need to
avoid endless bouts with irrational exuberance or irrational despair.
In Granovetter's world, if there are enough people in the crowd who
will not riot under any conditions -- that is, whose actions are
independent of the crowd's behavior as a whole -- then a riot will be
far less likely, because the more people who do not riot, the more
people there will be who don't want to riot."
So there we have it, both the pattern for useful behavior from crowds,
as well as warnings on how to avoid being sucked into negative mob
behavior. When utilized properly, the wisdom of crowds is real.
Surowiecki writes interestingly too about the financial markets and
political thinking. But for Groklaw readers the application is direct
and obvious. We've seen the wisdom of the crowds in the growth of
Linux, and also in the growth of Groklaw. Both models follow principles
that allow wisdom to bubble up from the group, with, as Surowiecki puts
it, someone to pick the "sweetest flower". When you let a thousand
flowers bloom, it's easy to pick the one that smells the sweetest. "The
Wisdom of Crowds" is very interesting, and I highly recommend it.
Many more comments on
http://www.groklaw.net/article.php?story041107180408325
Immortalist
-- Paul Ginsparg
"You don't do good software design by committee."
-- Donald Norman
"There's no justice like angry-mob justice."
-- Principal Seymour Skinner
"A person is smart. People are stupid."
-- Agent K
The wisdom of crowds you say? As Surowiecki explains, yes, but only
under the right conditions. In order for a crowd to be smart, he says
it needs to satisfy four conditions:
1. Diversity. A group with many different points of view will make
better decisions than one where everyone knows the same information.
Think multi-disciplinary teams building Web sites...programmers,
designers, biz dev, QA folks, end users, and copywriters all
contributing to the process, each has a unique view of what the final
product should be. Contrast that with, say, the President of the US and
his Cabinet.
2. Independence. "People's opinions are not determined by those around
them." AKA, avoiding the circular mill problem.
3. Decentralization. "Power does not fully reside in one central
location, and many of the important decisions are made by individuals
based on their own local and specific knowledge rather than by an
omniscient or farseeing planner." The open source software development
process is an example of effect decentralization in action.
4. Aggregation. You need some way of determining the group's answer
from the individual responses of its members. The evils of design by
committee are due in part to the lack of correct aggregation of
information. A better way to harness a group for the purpose of
designing something would be for the group's opinion to be aggregated
by an individual who is skilled at incorporating differing viewpoints
into a single shared vision and for everyone in the group to be aware
of that process (good managers do this). Aggregation seems to be the
most tricky of the four conditions to satisfy because there are so many
different ways to aggregate opinion, not all of which are right for a
given situation.
Satisfy those four conditions and you've hopefully cancelled out some
of the error involved in all decision making:
If you ask a large enough group of diverse, independent people to make
a prediciton or estimate a probability, and then everage those
estimates, the errors of each of them makes in coming up with an answer
will cancel themselves out. Each person's guess, you might say, has two
components: information and error. Subtract the error, and you're left
with the information.
The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How
Collective Wisdom Shapes Business, Economies, Societies and Nations
--by James Surowiecki (Author)
http://www.amazon.com/exec/obidos/tg/detail/-/0385503865/
Large groups of people are smarter than an elite few, no matter how
brilliant-better at solving problems, fostering innovation, coming to
wise decisions, even predicting the future...
Why is the line in which you're standing always the longest?
Why is it that you can buy a screw anywhere in the world and it will
fit a bolt bought ten-thousand miles away?
Why is network television so awful?
If you had to meet someone in Paris on a specific day but had no way of
contacting them, when and where would you meet?
Why are there traffic jams?
What's the best way to win money on a game show?
Why, when you walk into a convenience store at 2:00 A.M. to buy a quart
of orange juice, is it there waiting for you?
What do Hollywood mafia movies have to teach us about why corporations
exist?
What does guessing the weight of an ox at a country fair have to do
with the 9/11 attacks?
How does a flock of birds relate to a traffic jam?
Why doesn't collective wisdom believe in collective wisdom?
Explores these and other questions and explainations about how groups
of people, in specific kinds of circumstances, can reach intelligent,
accurate decisions that can then go horribly wrong.
---------------------------------
While our culture generally trusts experts and distrusts the wisdom of
the masses, under the right circumstances, groups are remarkably
intelligent, and are often smarter than the smartest people in them.
To support this almost counterintuitive proposition, Surowiecki
explores problems involving cognition (we're all trying to iden?ify a
correct answer), coordination (we need to synchronize our individual
activities with others) and cooperation (we have to act together
despite our self-interest).
His rubric, then, covers a range of problems, including driving in
traffic, competing on TV game shows, maximizing stock market
performance, voting for political candidates, navigating busy
sidewalks, tracking SARS and designing Internet search engines like
Google.
If four basic conditions are met, a crowd's "collective intelligence"
will produce better outcomes than a small group of experts, Surowiecki
says, even if members of the crowd don't know all the facts or choose,
individually, to act irrationally. "Wise crowds" need;
(1) diversity of opinion;
(2) independence of members from one another;
(3) decentralization; and
(4) a good method for aggregating opinions.
The diversity brings in different information; independence keeps
people from being swayed by a single opinion leader; people's errors
balance each other out; and including all opinions guarantees that the
results are "smarter" than if a single expert had been in charge.
...an interesting twist on the long held notion that Americans
generally question the masses and eschew groupthink.
...the TV studio audience of Who Wants to Be a Millionaire guesses
correctly 91 percent of the time, compared to "experts" who guess
only 65 percent correctly. Keep up the good work, comrades.
Immortalist
Writer, James Surowiecki, makes the
case for unlikely notion that the
many are smarter than the few;
THE WISDOM OF CROWDS:
Why the Many Are Smarter Than the Few
and How Collective Wisdom Shapes
Business, Economies, Societies, and Nations.
IN 1906 the English scientist Francis Gaston visited a country
livestock fair and stumbled upon an intriguing contest. An ox was on
display, and villagers were invited to guess the animal's weight after
slaughtering and dressing. Nearly 800 of them gave it a go, and not
surprisingly not one hit the exact mark: 1,198 pounds. Astonishingly,
however, the average of those 800 guesses came close - very close
indeed. It was 1,197 pounds.
This anecdote captures the striking thesis of James Surowiecki's The
Wisdom of Crowds. "[U]nder the right circumstances," Surowiecki argues,
"groups are remarkably intelligent, and are often smarter than the
smartest people in them." For evidence he cites how groups have been
used to find lost submarines, correct the spread on sporting events,
locate Web pages, even predict who will be elected president of the
United States. So why aren't we using groups more?
For one thing, crowds have a pretty bad rep. Crowds have ignited
lynchings, financial panics and styling trends like the mustache or
jock-hawk.
Furthermore, as Surowiecki notes, corporate structure is enthralled by
the idea of expertise. Strategy consultants demand $200 an hour to tell
companies what to do. Top executives rake in eight-figure salaries to
rescue sinking ships. To accept that the masses might know something
would mean radically altering how our country operates.
As counterintuitive as it sounds, however, groupthink works so long as
Surowiecki's three key criteria - independence, diversity and
decentralization - are satisfied. If you ask a large enough group to
make a prediction or estimate a probability, he writes, the errors
individuals make cancel each other out. "Subtract the error, and you're
left with the information." In this fashion the TV studio audience of
Who Wants to Be a Millionaire guessed the right answer to questions 91
percent of the time; "experts" guessed right only 65 percent of the
time.
Make no mistake, Surowiecki is a business columnist for the New Yorker
magazine, and The Wisdom of Crowds packs more textbook terms than an
economics primer. One need not be a CEO or amateur stock trader to
appreciate this book, but a certain patience with lingo does help.
Overall, however, Surowiecki is a patient and vivid writer with a knack
for culling entertaining examples. To demonstrate the importance of
diversity on group wisdom, he describes how NFL head coaches continue
to kick field goals when, statistics have shown, they are better off
going for it. Coaches come from similar backgrounds and work in similar
situations, and this leads to a group of risk-averse decision-makers.
The result? They think inside the proverbial box.
The application of group wisdom stretches far beyond football games,
though, which is why this book is not just revolutionary but essential
reading for all types of people. Take national security. In one
section, Surowiecki describes how the United States blundered into the
Bay of Pigs invasion because the decision-making group - the
president and his advisers - all shared similar conceptions and
assumptions. In short, the group lacked diversity and as a result
demonstrated a colossal example of the failings of groupthink.
The Wisdom of Crowds is likely to raise a certain amount of controversy
and not just because of Surowiecki's counterintuitive thesis. In one
section Surowiecki encourages the intelligence community to revisit the
idea of using futures markets - where people bet on impending
disasters - to enhance homeland security.
Still, there is something hopeful about Surowiecki's grand idea. One
need only read the lengthy subtitle to appreciate the whiff of populism
here. New York, Boston and Los Angeles might remain our nation's
cultural capitals, Surowiecki suggests, but the rest of the madding
crowd might know a thing or two. If for that reason alone, one hopes
the group approves of Surowiecki's book - and in a big way.
John Freeman is a writer in New York.
http://www.chron.com/cs/CDA/ssistory.mpl/ae/books/reviews/2658031
Immortalist
Frédéric Delsuc
Reading the French version of Journey to the Ants by Bert Hölldobler
and Edward O. Wilson (1996) during the course of my graduate studies, I
remember being amazed by the description of circular mills formed by an
isolated group of army ants. This phenomenon occurs when
a group of foragers is separated
from the main column of the raiding
swarm by a perturbation of their
pheromonal communication (Schneirla 1944).
The separated workers then run in a
densely packed circle until they all
die from exhaustion (Schneirla 1971).
As a student in evolutionary biology, I was puzzled by how such an
apparently aberrant behavior could have originated and could be
maintained during the course of evolution. This natural phenomenon is
reproducible in the laboratory and has recently been shown to result
from a self-organizing pattern (Couzin and Franks 2003). After a period
of disorder, a random direction is collectively selected by ants, and a
circular mill forms, following simple rules of motion governed by
direct interactions between individuals. But now, a phylogenetic study
by Seán G. Brady (2003) sheds new light on the origin of this behavior
by showing that the answer to the apparent paradox of circular milling
lies at least in part in the evolutionary history of these ants.
In fact, the formation of circular mills is a somewhat extreme
illustration of the obligate collective foraging behavior
characteristic of army ants, which are ineffective at foraging
solitarily. These species stage "swarm raids" composed of numerous
workers foraging for prey, which is overwhelmed and brought back to the
nest along dense traffic lanes. Army ant species are also
nomadic-they construct temporary nests whose location depends on food
resource availability. Queens of army ant colonies are highly modified
relative to those of other ant species in being wingless and able to
produce a huge number of eggs per brood cycle. These three
characteristics-obligate collective foraging, nomadism, and highly
modified queens-define what has been called "the army ant
syndrome" of behavioral and reproductive traits shared by all army
ant species. Until now, the dominant view was that this syndrome
originated at least three times independently in army ant lineages, two
restricted to the Old World (Aenictinae and Dorylinae) and one to the
New World (Ecitoninae). This hypothesis implies that the army ant array
of behavioral and reproductive traits has multiple origins and that
their morphological and behavioral resemblances are the result of
adaptive evolutionary convergence towards a similar strategy of
collective foraging.
By using an arsenal of modern phylogenetic methods, Brady has
reconstructed the evolutionary history of army ants to test whether
army ant syndrome definitely evolved in three separate lineages. Based
on the sequencing of mitochondrial and nuclear genes, on the analysis
of morphological characters, and on a comprehensive taxon sampling of
the group, including their closest non-army ant relatives, the author
built a robust picture of the phylogenetic relationships among army
ants. This phylogeny convincingly shows that all army ants species
shared a single common ancestor. Consequently, the complex of
behavioral and reproductive adaptations constituting the army ant
syndrome appears to have evolved only once, contrary to what was
traditionally thought and taught. It is thus likely that the main
components of the syndrome were already present in the most recent
common ancestor of extant army ant species.
Furthermore, using a recently developed molecular dating methodology
that explicitly incorporates fossil data (Thorne and Kishino 2002),
Brady derived a molecular timescale for the evolution of army ants.
These dates, which correspond to the divergence between New World and
Old World army ant lineages, place the origin of army ants around 105
million years ago, making them much more ancient than previously
thought. This date, remarkably congruent with the complete separation
of African and American tectonic plates, strongly suggests that the two
major groups of army ants are the result of a speciation event driven
by the dislocation of the ancient Southern supercontinent Gondwana.
This finding weakens the supposition that New World and Old World army
ants originated independently in South America and Africa,
respectively, after the breakup of Gondwana and adds support to the
hypothesis that army ants have a single origin. Such a process of
diversification is also consistent with the biology of army ants in
which dispersal is known to be limited due to the presence of
flightless queens. New species are therefore more likely to form by
allopatric speciation, in which speciation occurs because of the
emergence of geographical barriers within a population, than by
nonallopatric speciation. The evolutionary history of army ants in fact
possesses relatively ancient roots and appears to have been shaped by
biogeographical processes driven by plate tectonics.
Put together, this evidence demonstrates that the complex array of
behavioral and reproductive adaptations in army ants has originated
only once during the course of evolution and has subsequently been
maintained. Remarkably, after more than 100 million years of evolution,
none of the army ants species studied to date appears to lack any of
the three main components of the syndrome. Such broad-scale inertia
suggests that a strong phylogenetic constraint has influenced the
evolution of these complex adaptive traits. Of course, evolution
continues to operate within each component, but radical modification of
the syndrome is apparently quite difficult. Extreme specialization in
the ancestral army ant lineage seems to have prevented the appearance
of alternative evolutionary strategies in its descendant species.
To return to my initial question: how does this story help us to
understand the circular milling paradox? The answer is that the
occasional but deadly formation of circular mills seems to be the
evolutionary price that army ants pay to maintain such an ecologically
successful and stable strategy of collective foraging. The sporadic
appearance of this "pathological" behavior might thus be viewed as
the footprints left by the evolutionary trajectory in which these ants
have been trapped.
This model study illustrates the importance of taking into account
evolutionary history for understanding the mechanisms by which complex
morphological, behavioral, and reproductive characters have evolved.
Modern molecular and phylogenetic tools now allow the rigorous
reconstruction of the history of species by providing a way of
inferring both phylogenies and evolutionary timescales. Longstanding
evolutionary hypotheses of organismal evolution can therefore be tested
by bridging the gap between the paleontological and molecular records
of life (Benner et al. 2002), an approach that has already been most
successfully applied in the case of placental mammal phylogeny (Delsuc
et al. 2002; Springer et al. 2003).
Furthermore, the construction of a phylogenetic framework for army ants
promises a better understanding of the behavioral adaptations that have
led to the ecological success of this group. Future comparative
analyses using the derived phylogeny as a backbone will allow further
tests of the respective roles played by natural selection and
phylogenetic history in shaping the evolution of morphological and
behavioral traits developed by these ants. The accurate reconstruction
of the patterns of species diversification is a prerequisite for a
detailed understanding of the causal processes that underlie organismal
evolution.
--- Footnotes
Frédéric Delsuc is a Lavoisier postdoctoral research fellow from the
French Ministry of Foreign Affairs working in The Allan Wilson Centre
for Molecular Ecology and Evolution at Massey University in Palmerston
North, New Zealand, co-directed by David Penny and Michael D. Hendy.
E-mail: F.De...@massey.ac.nz
--- References
Benner SA, Caraco MD, Thomson JM, Gaucher EA. Planetary
biology-Paleontological, geological, and molecular histories of life.
Science 2002;296:864-868. [PubMed] [Full Text]
Brady SG. Evolution of the army ant syndrome: The origin and long-term
evolutionary stasis of a complex of behavioral and reproductive
adaptations. Proc Natl Acad Sci U S A 2003;100:6575-6579. [Free Full
text in PMC]
Couzin ID, Franks NR. Self-organized lane formation and optimized
traffic flow in army ants. Proc R Soc Lond B 2003;270:139-146.
[PubMed] [Free Full Text]
Delsuc F, Scally M, Madsen O, Stanhope MJ, de Jong WW, et al. Molecular
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sampling on the placental tree rooting. Mol Biol Evol
2002;19:1656-1671. [PubMed] [Free Full Text]
Hölldobler, B.; Wilson, EO.Paris: Seuil; 1996. Voyage chez les
fourmis: Une exploration scientifique; 249 p.
Schneirla TC. A unique case of circular milling in ants, considered in
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Mus Novit 1944;1253:1-26.
Schneirla, TC.San Francisco: Freeman; 1971. Army ants: A study in
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http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=261877
Immortalist
cardboard wands in the air. Each wand is red on one side, green on the
other. Far in back of the huge auditorium, a camera scans the frantic
attendees. The video camera links the color spots of the wands to a
nest of computers set up by graphics wizard Loren Carpenter.
Carpenter's custom software locates each red and each green wand in the
auditorium. Tonight there are just shy of 5,000 wandwavers. The
computer displays the precise location of each wand (and its color)
onto an immense, detailed video map of the auditorium hung on the front
stage, which all can see. More importantly, the computer counts the
total red or green wands and uses that value to control software. As
the audience wave the wands, the display screen shows a sea of lights
dancing crazily in the dark, like a candlelight parade gone punk. The
viewers see themselves on the map; they are either a red or green
pixel. By flipping their own wands, they can change the color of their
projected pixels instantly.
Loren Carpenter boots up the ancient video game of Pong onto the
immense screen. Pong was the first commercial video game to reach pop
consciousness. It's a minimalist arrangement: a white dot bounces
inside a square; two movable rectangles on each side act as virtual
paddles. In short, electronic ping-pong. In this version, displaying
the red side of your wand moves the paddle up. Green moves it down.
More precisely, the Pong paddle moves as the average number of red
wands in the auditorium increases or decreases. Your wand is just one
vote.
Carpenter doesn't need to explain very much. Every attendee at this
1991 conference of computer graphic experts was probably once hooked on
Pong. His amplified voice booms in the hall, "Okay guys. Folks on the
left side of the auditorium control the left paddle. Folks on the right
side control the right paddle. If you think you are on the left, then
you really are. Okay? Go!"
The audience roars in delight. Without a moment's hesitation, 5,000
people are playing a reasonably good game of Pong. Each move of the
paddle is the average of several thousand players' intentions. The
sensation is unnerving. The paddle usually does what you intend, but
not always. When it doesn't, you find yourself spending as much
attention trying to anticipate the paddle as the incoming ball. One is
definitely aware of another intelligence online: it's this hollering
mob.
The group mind plays Pong so well that Carpenter decides to up the
ante. Without warning the ball bounces faster. The participants squeal
in unison. In a second or two, the mob has adjusted to the quicker pace
and is playing better than before. Carpenter speeds up the game
further; the mob learns instantly.
"Let's try something else," Carpenter suggests. A map of seats in the
auditorium appears on the screen. He draws a wide circle in white
around the center. "Can you make a green '5' in the circle?" he asks
the audience. The audience stares at the rows of red pixels. The game
is similar to that of holding a placard up in a stadium to make a
picture, but now there are no preset orders, just a virtual mirror.
Almost immediately wiggles of green pixels appear and grow haphazardly,
as those who think their seat is in the path of the "5" flip their
wands to green. A vague figure is materializing. The audience
collectively begins to discern a "5" in the noise. Once discerned, the
"5" quickly precipitates out into stark clarity. The wand-wavers on the
fuzzy edge of the figure decide what side they "should" be on, and the
emerging "5" sharpens up. The number assembles itself.
"Now make a four!" the voice booms. Within moments a "4" emerges.
"Three." And in a blink a "3" appears. Then in rapid succession,
"Two... One...Zero." The emergent thing is on a roll.
Loren Carpenter launches an airplane flight simulator on the screen.
His instructions are terse: "You guys on the left are controlling roll;
you on the right, pitch. If you point the plane at anything
interesting, I'll fire a rocket at it." The plane is airborne. The
pilot is...5,000 novices. For once the auditorium is completely silent.
Everyone studies the navigation instruments as the scene outside the
windshield sinks in. The plane is headed for a landing in a pink valley
among pink hills. The runway looks very tiny.
There is something both delicious and ludicrous about the notion of
having the passengers of a plane collectively fly it. The brute
democratic sense of it all is very appealing. As a passenger you get to
vote for everything; not only where the group is headed, but when to
trim the flaps.
But group mind seems to be a liability in the decisive moments of
touchdown, where there is no room for averages. As the 5,000 conference
participants begin to take down their plane for landing, the hush in
the hall is ended by abrupt shouts and urgent commands. The auditorium
becomes a gigantic cockpit in crisis. "Green, green, green!" one
faction shouts. "More red!" a moment later from the crowd. "Red, red!
REEEEED!" The plane is pitching to the left in a sickening way. It is
obvious that it will miss the landing strip and arrive wing first.
Unlike Pong, the flight simulator entails long delays in feedback from
lever to effect, from the moment you tap the aileron to the moment it
banks. The latent signals confuse the group mind. It is caught in
oscillations of overcompensation. The plane is lurching wildly. Yet the
mob somehow aborts the landing and pulls the plane up sensibly. They
turn the plane around to try again.
How did they turn around? Nobody decided whether to turn left or right,
or even to turn at all. Nobody was in charge. But as if of one mind,
the plane banks and turns wide. It tries landing again. Again it
approaches cockeyed. The mob decides in unison, without lateral
communication, like a flock of birds taking off, to pull up once more.
On the way up the plane rolls a bit. And then rolls a bit more. At some
magical moment, the same strong thought simultaneously infects five
thousand minds: "I wonder if we can do a 360?"
Without speaking a word, the collective keeps tilting the plane.
There's no undoing it. As the horizon spins dizzily, 5,000 amateur
pilots roll a jet on their first solo flight. It was actually quite
graceful. They give themselves a standing ovation.
The conferees did what birds do: they flocked. But they flocked self-
consciously. They responded to an overview of themselves as they
co-formed a "5" or steered the jet. A bird on the fly, however, has no
overarching concept of the shape of its flock. "Flockness" emerges from
creatures completely oblivious of their collective shape, size, or
alignment. A flocking bird is blind to the grace and cohesiveness of a
flock in flight.
Immortalist
football called "Genghis." Brooks had pushed his downsizing to an
extreme. Genghis had six legs but no "brain" at all. All of its 12
motors and 21 sensors were distributed in a decomposable network
without a centralized controller. Yet the interaction of these 12
muscles and 21 sensors yielded an amazingly complex and lifelike
behavior.
Each of Genghis's six tiny legs worked on its own, independent of the
others. Each leg had its own ganglion of neural cells-a tiny
microprocessor-that controlled the leg's actions. Each leg thought for
itself! Walking for Genghis then became a group project with at least
six small minds at work. Other small semiminds within its body
coordinated communication between the legs. Entomologists say this is
how ants and real cockroaches cope-they have neurons in their legs that
do the leg's thinking.
In the mobot Genghis, walking emerges out of the collective behavior of
the 12 motors. Two motors at each leg lift, or not, depending on what
the other legs around them are doing. If they activate in the right
sequence-Okay, hup! One, three, six, two, five, four!-walking
"happens."
No one place in the contraption governs walking. Without a smart
central controller, control can trickle up from the bottom. Brooks
called it "bottom-up control." Bottom-up walking. Bottom-up smartness.
If you snip off one leg of a cockroach, it will shift gaits with the
other five without losing a stride. The shift is not learned; it is an
immediate self-reorganization. If you disable one leg of Genghis, the
other legs organize walking around the five that work. They find a new
gait as easily as the cockroach.
In one of his papers, Rod Brooks first laid out his instructions on how
to make a creature walk without knowing how:
There is no central controller which directs the body where to put each
foot or how high to lift a leg should there be an obstacle ahead.
Instead, each leg is granted a few simple behaviors and each
independently knows what to do under various circumstances. For
instance, two basic behaviors can be thought of as "If I'm a leg and
I'm up, put myself down, " or "If I'm a leg and I'm forward, put the
other five legs back a little." These processes exist independently,
run at all times, and fire whenever the sensory preconditions are true.
To create walking then, there just needs to be a sequencing of lifting
legs (this is the only instance where any central control is evident).
As soon as a leg is raised it automatically swings itself forward, and
also down. But the act of swinging forward triggers all the other legs
to move back a little. Since those legs happen to be touching the
ground, the body moves forward.
Once the beast can walk on a flat smooth floor without tripping, other
behaviors can be added to improve the walk. For Genghis to get up and
over a mound of phone books on the floor, it needs a pair of sensing
whiskers to send information from the floor to the first set of legs. A
signal from a whisker can suppress a motor's action. The rule might be,
"If you feel something, I'll stop; if you don't, I'll keep going."
While Genghis learns to climb over an obstacle, the foundational
walking routine is never fiddled with. This is a universal biological
principle that Brooks helped illuminate-a law of god: When something
works, don't mess with it; build on top of it. In natural systems,
improvements are "pasted" over an existing debugged system. The
original layer continues to operate without even being (or needing to
be) aware that it has another layer above it.
When friends give you directions on how to get to their house, they
don't tell you to "avoid hitting other cars" even though you must
absolutely follow this instruction. They don't need to communicate the
goals of lower operating levels because that work is done smoothly by a
well-practiced steering skill. Instead, the directions to their house
all pertain to high-level activities like navigating through a town.
Animals learn (in evolutionary time) in a similar manner. As do
Brooks's mobots. His machines learn to move through a complicated world
by building up a hierarchy of behaviors, somewhat in this order:
Avoid contact with objects
Wander aimlessly
Explore the world
Build an internal map
Notice changes in the environment
Formulate travel plans
Anticipate and modify plans accordingly
The Wander-Aimlessly Department doesn't give a hoot about obstacles,
since the Avoidance Department takes such good care of that.
Sir Frederick
Your 'crowds' thesis should be included by anyone composing
the 'Principles of Information', whenever they get around to doing so.
You might email it to the Santa Fe Institute, as people there are working
on the PofI.
--
Best,
Frederick Martin McNeill
Poway, California, United States of America
mmcn...@fuzzysys.com
http://www.fuzzysys.com
http://members.cox.net/fmmcneill/
*************************
Phrase of the week :
It is a great thing to make scientific discoveries of rare value,
but it is even greater to be willing to share these discoveries
and to encourage other workers in the same field of scientific
research.-- William J. Mayo (1861-1937)
:-))))Snort!)
*************************
Immortalist
How did you discover the wisdom of crowds?
The idea really came out of my writing on how markets work. Markets are
made up of diverse people with different levels of information and
intelligence, and yet when you put all those people together and they
start buying and selling, they come up with generally intelligent
decisions. Sometimes, though, they come up with remarkably stupid
decisions-as they did during the stock-market bubble in the late
1990s. I was interested in what explained the successes and the
failures of markets, and as I got further into it I realized that it
wasn't just markets that were smart. In fact, crowds of all sorts were
often remarkably wise.
Could you define "the crowd?"
A "crowd," in the sense that I use the word in the book, is really any
group of people who can act collectively to make decisions and solve
problems. So, on the one hand, big organizations-like a company or a
government agency-count as crowds. And so do small groups, like a
team of scientists working on a problem. But just as interested-maybe
even more interested-in groups that aren't really aware themselves as
groups, like bettors on a horse race or investors in the stock market.
They make up crowds, too, because they're collectively producing a
solution to a complicated problem: the bets of people betting on a
horse race determine what the odds on the race will be, and the choices
of investors determine stock prices.
Under what circumstances is the crowd smarter?
There are four key qualities that make a crowd smart. It needs to be
diverse, so that people are bringing different pieces of information to
the table. It needs to be decentralized, so that no one at the top is
dictating the crowd's answer. It needs a way of summarizing people's
opinions into one collective verdict. And the people in the crowd need
to be independent, so that they pay attention mostly to their own
information, and not worrying about what everyone around them thinks.
And what circumstances can lead the crowd to make less-than-stellar
decisions?
Essentially, any time most of the people in a group are biased in the
same direction, it's probably not going to make good decisions. So when
diverse opinions are either frozen out or squelched when they're
voiced, groups tend to be dumb. And when people start paying too much
attention to what others in the group think, that usually spells
disaster, too. For instance, that's how we get stock-market bubbles,
which are a classic example of group stupidity: instead of worrying
about how much a company is really worth, investors start worrying
about how much other people will think the company is worth. The
paradox of the wisdom of crowds is that the best group decisions come
from lots of independent individual decisions.
What kind of problems are crowds good at solving and what kind are they
not good at solving?
Crowds are best when there's a right answer to a problem or a question.
(I call these "cognition" problems.) If you have, for instance, a
factual question, the best way to get a consistently good answer is to
ask a group. They're also surprisingly good, though, at solving other
kinds of problems. For instance, in smart crowds, people cooperate and
work together even when it's more rational for them to let others do
the work. And in smart crowds, people are also able to coordinate their
behavior-for instance, buyers and sellers are able to find each other
and trade at a reasonable price-without anyone being in charge.
Groups aren't good at what you might call problems of skill-for
instance, don't ask a group to perform surgery or fly a plane.
Why are we not better off finding an expert to make all the hard
decisions?
Experts, no matter how smart, only have limited amounts of information.
They also, like all of us, have biases. It's very rare that one person
can know more than a large group of people, and almost never does that
same person know more about a whole series of questions. The other
problem in finding an expert is that it's actually hard to identify
true experts. In fact, if a group is smart enough to find a real
expert, it's more than smart enough not to need one.
Can you explain how a betting pool can help predict the future?
Well, predicting the future is what bettors try to do every day, when
they try to figure out what horse will win a race or what football team
will win on Sunday. What horse-racing odds or a point spread represent,
then, is the group's collective judgment about the future. And what we
know from many studies is that that collective judgment is often
remarkably accurate. Now, we have to be careful here. In the case of a
horse race, for instance, what the group is good at predicting is the
likelihood of each horse winning. The potential benefits of this are
pretty obvious. If you're a company, say, that's trying to decide which
product you should put out, what you want to know is the likelihood of
success of your different options. A betting pool-or a market, or
some other way of tapping into the wisdom of crowds-is the best way
for you to get that information.
Can you give an example of a current company that is tapping into the
"wisdom of crowds?"
There's a division of Eli Lilly called e.Lilly, which has been
experimenting with using internal stock markets and hypothetical drug
candidates to predict whether new drugs will gain FDA approval. That's
an essential thing for drug companies to know, because their whole
business depends on them not only picking winners-that is, good, safe
drugs-but also killing losers before they've invested too much money
in them.
You've explained how tapping into the crowd's collective wisdom can
help a corporation, but how can it help other entities, like a
government, or perhaps more importantly, an individual?
Well, the same principles that make collective wisdom useful to a
company make it just as useful to the government. For instance, in the
book I talk about the Columbia disaster, showing how NASA's failure to
deal with the shuttle's problems stemmed, in part, from a failure to
tap into knowledge and information that the people in the organization
actually had. And in a broader sense, I think the book suggests that
the more diverse and free the flow of information in a society is, the
better the decisions that society will reach. As far as individuals go,
I think there are two consequences. First, we can look to collective
decisions-as long as the groups are diverse, etc.-to give us good
predictions. But the collective decisions will only be smart if each of
us tries to be as independent as possible. So instead of just taking
the advice of your smart friend, you should try to make your own
choice. In doing so, you'll make the group smarter.
When you talk about using the crowd to make a decision, are you talking
about consensus?
No, and this is one of the most important points in the book. The
wisdom of crowds isn't about consensus. It really emerges from
disagreement and even conflict. It's what you might call the average
opinion of the group, but it's not an opinion that every one in the
group can agree on. So that means you can't find collective wisdom via
compromise.
What would Charles MacKay-the author of Extraordinary Popular
Delusions and the Madness of Crowds-think of your book?
He would probably think I'm deluded. Mackay thought crowds were doomed
to excess and foolishness, and that only individuals could produce
intelligent decisions. On the other hand, a good chunk of my book is
about how crowds can, as it were, go mad, and what allows them to
succumb to delusions. Mackay would like those chapters.
What do you most hope people will learn from reading THE WISDOM OF
CROWDS?
I think the most important lesson is not to rely on the wisdom of one
or two experts or leaders when making difficult decisions. That doesn't
mean that expertise is irrelevant, or that we don't need smart people.
It just means that together all of us know more than any one of us
does.
http://www.randomhouse.com/features/wisdomofcrowds/Q&A.html
http://www.randomhouse.com/features/wisdomofcrowds/
Albert Wagner
<snip>
Googlebot strikes yet again.
--
"I consider it quite possible that physics cannot be based
on the field concept, i. e., on continuous structures. In that
case nothing remains of my entire castle in the air,
gravitation theory included, [and of] the rest of modern physics."
-- Albert Einstein in a 1954 letter to Michele Besso.
tooly
I wonder if there is a 'short term' and a 'long term' discussion upon the
subject of mass wisdom versus individual? I ask this because of technology
and the growing 'power' of the media to influence how people think. Fox and
CNN are two clear examples. If one is liberal, Fox is clearly bordering on
yellow journalism...very biased. If one is conservative however, they have
sat back for decades and listened to a decidedly growing liberal bias
througout the national media, especially that of CNN.
Add to this mix the growing understanding of human psychology that is
embraced at the corporate level to sell their products. It is all quite
covert and people often are quite unaware of how they are being led around
by the nose so to say.
Finally, Education itself has become a foghorn to espouse very 'politically'
undecided social hegemony. Free thought in education is no longer real [one
is free only as long as they espouse that politically charged social
hegemony].
Were the millions of Soviet citizens who embraced communism 'right'?...since
at the time, that was mass will? [just one example] [but the purety of
'mass will' can be questioned here since coercion was at play; but coercion
is perhaps always present in some form; very present in today's politically
correct social minefield].
It is very very difficult to stand 'alone' in this world..and few succeed
if that is their choice. But fortunately, the few who do succeed end up
being the 'leaders' of thought for the masses in future eras.
No, I still give my allegiance to individuals...not the herd. Rule by
committee is perhaps what is so wrong with today's world. IMO.
" You can fool some of the people all of the time, and all of the people
some of the time, but you can not fool all of the people all of the time."
zerkanX
> and the growing 'power' of the media to influence how people think.
The power of the mass media today is taking a nose-dive but you won;t hear this
on CNN or FOX. What their pathetic funtion is today is primarily telling
people what is and what is not acceptable all the while giving a illusion of
real difference one being on the side of 'tastes great' the other being
viciously in support of 'less filling', both however are just pushing the same
product.
> a decidedly growing liberal bias
> througout the national media, especially that of CNN.
> " You can fool some of the people all of the time, and all of the people
> some of the time, but you can not fool all of the people all of the time."
.. I am afraid you have been fooled! It isn't CNN who grew into 'liberal bias'
it is the definitions that have moved.
zerkanX
> comfortable 3% margin
Sorry, there 3% is hardly comfortable!! Isn't this inside the standard
margin of error?
feedbac...@yahoo.com
> Why is network television so awful?
>
> If you had to meet someone in Paris on a specific day but had no way
of
> contacting them, when and where would you meet?
>
> Why are there traffic jams?
>
http://www.digitalnpq.org/archive/2004_winter/prigogine.html
feedbac...@yahoo.com
> The separated workers then run in a
> densely packed circle until they all
> die from exhaustion (Schneirla 1971).
>
> As a student in evolutionary biology, I was puzzled by how such an
> apparently aberrant behavior could have originated and could be
> maintained during the course of evolution. This natural phenomenon is
> reproducible in the laboratory and has recently been shown to result
> from a self-organizing pattern (Couzin and Franks 2003
You might be interested in her account of stasis and recruitment.
http://ant.stanford.edu/
D. M. Gordon.1999. Ants at Work: how an insect society is organized.
Free Press, Simon and Schuster. 2000 paperback, W. W. Norton.
Immortalist
feedbac...@yahoo.com wrote:
> > Why is network television so awful?
> >
> > If you had to meet someone in Paris on a specific
> > day but had no way of contacting them, when and
> > where would you meet?
> >
> > Why are there traffic jams?
> >
>
> in 1997 in chemistry for the discovery
> of dissipative structures, is considered
> the father of "chaos theory." Prigogine
> died earlier this year after preparing
> this column for NPQ's Nobel laureate series...
>
> In large societies, we see collective motions.
> Accordingly, the role of individual activity
> is drastically reduced. Human societies
> could face the same evolution when tied
> together by networks.
>
> Years ago I studied traffic flows. I found
> that when the flow of traffic was light,
> each driver behaved more or less as he
> wanted. I called that an "individual regime."
> But when the flow becomes more and more dense,
> the characteristics of a "collective regime"
> take over in which everybody is pushing the
> other and is pushed by the other.
>
> http://www.digitalnpq.org/archive/2004_winter/prigogine.html
See "Dietrich Braess" below;
Network logic is counterintuitive. Say you need to lay a telephone
cable that will connect a bunch of cities; let's make that three for
illustration: Kansas City, San Diego, and Seattle. The total length of
the lines connecting those three cities is 3,000 miles. Common sense
says that if you add a fourth city to your telephone network, the total
length of your cable will have to increase. But that's not how network
logic works. By adding a fourth city as a hub (let's make that Salt
Lake City) and running the lines from each of the three cities through
Salt Lake City, we can decrease the total mileage of cable to 2,850 or
5 percent less than the original 3,000 miles. Therefore the total
unraveled length of a network can be shortened by adding nodes to it!
Yet there is a limit to this effect. Frank Hwang and Ding Zhu Du,
working at Bell Laboratories in 1990, proved that the best savings a
system might enjoy from introducing new points into a network would
peak at about 13 percent. More is different.
On the other hand, in 1968 Dietrich Braess, a German operations
researcher, discovered that adding routes to an already congested
network will only slow it down. Now called Braess's Paradox, scientists
have found many examples of how adding capacity to a crowded network
reduces its overall production. In the late 1960s the city planners of
Stuttgart tried to ease downtown traffic by adding a street. When they
did, traffic got worse; then they blocked it off and traffic improved.
In 1992, New York City closed congested 42nd Street on Earth Day,
fearing the worst, but traffic actually improved that day.
Then again, in 1990, three scientists working on networks of brain
neurons reported that increasing the gain-the responsivity-of
individual neurons did not increase their individual signal detection
performance, but it did increase the performance of the whole network
to detect signals.
http://www.kk.org/outofcontrol/ch2-g.html
The prime variable Kauffman played with was the connectivity of the
network. In a sparsely connected network, each node would on average
only connect to one other node, or less. In a richly connected network,
each node would link to ten or a hundred or a thousand or a million
other nodes. In theory the limit to the number of connections per node
is simply the total number of nodes, minus one. A million-headed
network could have a million-minus-one connections at each node; every
node is connected to every other node. To continue our rough analogy,
every employee of GM could be directly linked to all 749,999 other
employees of GM.
As Kauffman varied this connectivity parameter in his generic networks,
he discovered something that would not surprise the CEO of GM. A system
where few agents influenced other agents was not very adaptable. The
soup of connections was too thin to transmit an innovation. The system
would fail to evolve. As Kauffman increased the average number of links
between nodes, the system became more resilient, "bouncing back" when
perturbed. The system could maintain stability while the environment
changed. It would evolve. The completely unexpected finding was that
beyond a certain level of linking density, continued connectivity would
only decrease the adaptability of the system as a whole.
Kauffman graphed this effect as a hill. The top of the hill was optimal
flexibility to change. One low side of the hill was a sparsely
connected system: flat-footed and stagnant. The other low side was an
overly connected system: a frozen grid-lock of a thousand mutual pulls.
So many conflicting influences came to bear on one node that whole
sections of the system sank into rigid paralysis. Kauffman called this
second extreme a "complexity catastrophe." Much to everyone's surprise,
you could have too much connectivity. In the long run, an overly linked
system was as debilitating as a mob of uncoordinated loners.
Somewhere in the middle was a peak of just-right connectivity that gave
the network its maximal nimbleness. Kauffman found this measurable
"Goldilocks'" point in his model networks. His colleagues had trouble
believing his maximal value at first because it seemed counterintuitive
at the time. The optimal connectivity for the distilled systems
Kauffman studied was very low, "somewhere in the single digits." Large
networks with thousands of members adapted best with less than ten
connections per member. Some nets peaked at less than two connections
on average per node! A massively parallel system did not need to be
heavily connected in order to adapt. Minimal average connection, done
widely, was enough.
Kauffman's second unexpected finding was that this low optimal value
didn't seem to fluctuate much, no matter how many members comprised a
specific network. In other words, as more members were added to the
network, it didn't pay (in terms of systemwide adaptability) to
increase the number of links to each node. To evolve most rapidly, add
members but don't increase average link rates. This result confirmed
what Craig Reynolds had found in his synthetic flocks: you could load a
flock up with more and more members without having to reconfigure its
structure.
Kauffman found that at the low end, with less than two connections per
agent or organism, the whole system wasn't nimble enough to keep up
with change. If the community of agents lacked sufficient internal
communication, it could not solve a problem as a group. More exactly,
they fell into isolated patches of cooperative feedback but didn't
interact with each other.
At the ideal number of connections, the ideal amount of information
flowed between agents, and the system as a whole found the optimal
solutions consistently. If their environment was changing rapidly, this
meant that the network remained stable-persisting as a whole over time.
Kauffman's Law states that above a certain point, increasing the
richness of connections between agents freezes adaptation. Nothing gets
done because too many actions hinge on too many other contradictory
actions. In the landscape metaphor, ultra-connectance produces
ultra-ruggedness, making any move a likely fall off a peak of
adaptation into a valley of nonadaptation. Another way of putting it,
too many agents have a say in each other's work, and bureaucratic rigor
mortis sets in. Adaptability conks out into grid-lock. For a
contemporary culture primed to the virtues of connecting up, this low
ceiling of connectivity comes as unexpected news.
We postmodern communication addicts might want to pay attention to
this. In our networked society we are pumping up both the total number
of people connected (in 1993, the global network of networks was
expanding at the rate of 15 percent additional users per month!), and
the number of people and places to whom each member is connected.
Faxes, phones, direct junk mail, and large cross-referenced data bases
in business and government in effect increase the number of links
between each person. Neither expansion particularly increases the
adaptability of our system (society) as a whole.
Immortalist
> > The separated workers then run in a
> > densely packed circle until they all
> > die from exhaustion (Schneirla 1971).
> >
> > As a student in evolutionary biology, I was puzzled by how such an
> > apparently aberrant behavior could have originated and could be
> > maintained during the course of evolution. This natural phenomenon
is
> > reproducible in the laboratory and has recently been shown to
result
> > from a self-organizing pattern (Couzin and Franks 2003
>
> You might be interested in her account of stasis and recruitment.
>
> http://ant.stanford.edu/
http://ant.stanford.edu/research.html
thanx
> D. M. Gordon.1999. Ants at Work: how an insect society is organized.
> Free Press, Simon and Schuster. 2000 paperback, W. W. Norton.
An individual ant, like an individual neuron, is just about as dumb as
can be. Connect enough of them together properly, though, and you get
spontaneous intelligence. Web pundit Steven Johnson explains what we
know about this phenomenon with a rare lucidity in Emergence: The
Connected Lives of Ants, Brains, Cities, and Software. Starting with
the weird behavior of the semi-colonial organisms we call slime molds,
Johnson details the development of increasingly complex and familiar
behavior among simple components: cells, insects, and software
developers all find their place in greater schemes.
Most game players, alas, live on something close to day-trader time, at
least when they're in the middle of a game--thinking more about their
next move than their next meal, and usually blissfully oblivious to the
ten- or twenty-year trajectory of software development. No one wants to
play with a toy that's going to be fun after a few decades of
tinkering--the toys have to be engaging now, or kids will find other
toys.
Emergence: The Connected Lives of
Ants, Brains, Cities, and Software
by Steven Johnson
http://www.amazon.com/exec/obidos/tg/detail/-/0684868768/
"The Myth of the Ant Queen"
Johnson reminds us that despite the title, ant queens are not authority
figures. Ant colonies grow and collectively develop solutions to
environmental challenges, with no one directing anything. Instead,
individual behavior, marked by scent trails that grow stronger with
repeated use, reinforces itself through positive feedback. These scent
trails create the colony's tangible form of cumulative "memory."
For thousands of years, cities (like ant colonies) have grown and
organized themselves into complex arrangements that kept large numbers
fed, housed, protected and employed, all without central planning. In
cities over the centuries, pathways, hubs and trade-specific
marketplaces have emerged from repeated use and persist, again as a
form of cumulative memory that guides old and new dwellers to
resources. He calls a city a "pattern amplifying machine; its
neighborhoods are a way of measuring and expressing the repeated
behavior of larger collectivities -- capturing information about group
behavior, and sharing that behavior with the group." Johnson, p. 40.
http://www.dougsimpson.com/blog/archives/000233.html
Johnson notes that the principle of emergence operates in the natural
world but is not obvious when one looks at systems from the outside. He
begins his study with a description of ants for this reason:
the "myth of the ant queen" invites
us to draw an analogy to human
organizations where centralized
authority is seen to direct the
behavior of individuals (hence the
totalitarian threat of Star Trek's
Borg Queen and Princess Bala's fight
for individuality in Antz).
The skillful, monarch-like insect in her apartment deploying the
necessary number of drones to provide for the colony's supply,
defense, and reproduction is an appealing stage from which moviegoers
can achieve a cathartic sense of freedom. Yet, Johnson writes, this
analogy is false; the ant queen does not direct an army of drones.
Drones take direction from a small set of simple signals released by
other drones. A drone collecting food leaves behind a special scent,
and other drones that pick up that scent will follow the path to the
food source. The most direct path to the food becomes the most
successful and so pragmatic behavior helps drones to "determine"
the best path to take. No one drone knows where the food is or has a
map of the terrain, nor does the queen: the emergent system is smarter
than the individual members of the colony and acts as an effective
decision-making process.
While the example of the ant colony is a fascinating story of the
natural world, it is the potential of such "smart" systems that
interests Johnson. After this skillful survey of emergence in the
natural world, Johnson explains how human systems such as cities are
affected by emergence. He adroitly overviews the relevant sources in
communication theory, computer science, biology, psychology, and urban
studies, making his book a worthwhile survey and a springboard for
further study. Johnson then turns to an examination of the implications
of emergence for new media, and in part three he explicitly
demonstrates how the principles of emergence can be used to improve
existing systems and to survive the onslaught of information that is
likely on the horizon.
Using an analysis of TiVo as a guide, Johnson suggests that
"clusters" of consumer interest will emerge as a force more
powerful than media monopolies. The result, Johnson writes, will be
that users will create their own systems of understanding:
http://www.cwrl.utexas.edu/currents/fall04/leslie.html
Dave Sims. What is emergence?
Steven Johnson. Emergence is what happens when the whole is smarter
than the sum of its parts. It's what happens when you have a system of
relatively simple-minded component parts -- often there are thousands
or millions of them -- and they interact in relatively simple ways. And
yet somehow out of all this interaction some higher level structure or
intelligence appears, usually without any master planner calling the
shots. These kinds of systems tend to evolve from the ground up.
The book spends a lot of time with the ants as a great example of this.
Colonies having this miraculous ability to pull off complex engineering
feats or resource management feats without an actual leadership
dictating what any ants should be doing at any time. They just follow a
lot of local rules, and through those rules the intelligence of the
colony comes into being.
Sims. It was interesting, the sort of built-in need of observers to try
to find the leader or master planner in those systems.
Johnson. It seems like in many cases it's a useful strategy. The
systems actually work as though they had leaders. So even if you're
wrong in your assessment of them, it's not a bad guess to assume that
the queen ant is in charge of the whole thing. Because they're so
organized, they look like systems that have leaders.
So it may not be that there's a neural mechanism to find leaders, but
our brains may be skewed to look at things in top-down ways, because we
grew up as social, hierarchical primates, or whatever the evolutionary
psychology explanation of it is. You have to sometimes kind of push
your head to think about things in an emergent way, or in a bottom-up
way. Once you do it, it can be very illuminating.
Sims. It reminded me in some ways of the Manhattan consulting firms who
try to find fashion leaders before the trends are well identified, so
they can pass that information back to their clients. It's another
place where people are trying to find leaders where it's not at all
clear who the leaders are.
Johnson. Yeah, it's one of the ways the book connects to Malcolm
[Gladwell]'s book, The Tipping Point, which talked about that. In that
context, leader is probably the wrong word for people who start trends,
early adopters. It's not leader in the sense of a top-down broadcast
role, where they have a big megaphone and they sit there and they say,
"Okay, hooded sweatshirts, now! Everybody put them on!" They just are
somewhere at a key point in the overall system of fashion, wherever
that is, where they're connected to the right people, and what that
core group decides ripples out very quickly through the whole system.
So they're leaders in the sense that their ideas emanate from them, but
they emanate in a much more distributed-network kind of way.
Sims. You write about four stages of emergence. What are they?
Johnson. I was trying to avoid the question of whether there would be
sentient networks in our future.
The first stage was people working on the problem without realizing
they were working on the problem -- people like [Alan] Turing, working
on his morphogenesis paper, and to some extent, the story I tell about
Engels in Manchester. Because the field hadn't really coalesced, it
wasn't clear that they were working on a field that had more general
applicability.
The second stage is when it becomes a field in itself, and people
understand that there is a connection between ants and cities, that you
can study them both under this general interpretive rubric.
The third stage is where people actually go out understanding the laws
that run through these systems and start building things in a conscious
awareness of those laws. Sim City is the first example of that, in some
ways: "We understand how little self-organizing systems work, so I can
create a little software program that will simulate that on the screen,
and it'll be fun, and I'll do it as a deliberate work of culture, not
as a model of doing it in a lab."
And then the fourth stage was conceivably down the line when computer
networks get to actually start having kind of a mind of their own --
about which I try to be pretty much agnostic throughout the book.
Rael Dornfest. Probably a smart idea, given that the road to AI always
wanders out into the wilderness.
Johnson. Yeah. I wrote a long piece for The Nation this summer about
AI, the Spielberg movie, using it as a launching pad to say that one of
the most misleading things about all of the popular representations of
AI is the idea that what will be so unsettling and uncanny will be how
close it is to human intelligence. From Frankenstein up through
Bladerunner, all the anxiety comes from the fact that the robot or
whatever looks too much like a human, so much that you can't quite tell
the difference.
In fact what's much more likely to happen is that you will end up
evolving the intelligence because you can engineer it. You'll wind up
growing it from below rather than from above, and it'll evolve in ways
you won't totally be in control of. It'll end up being intelligence
that won't quite look like human intelligence. It'll have other
properties in it, and it may be hard for us to pick up on the fact that
it is intelligent because our criteria is different. The uncanny thing
about it will be that it doesn't look like us, it looks like something
radically different.
In a way, all of the sci-fi films have it the wrong way around.
Sims. You wrote about Danny Hillis and the genetic programs he evolved
to solve a math problem.
Johnson. He didn't understand them. It's one of the great stories. He
looked at the code, and it may well have been that there was no actual
explanation of what they did in that there was no description of what
they did that was actually shorter than the actual code itself.
Sims. It's still hard for me to understand how he could look at the
results of a program he wrote and not understand how it works.
Johnson. Yeah. It gets to the question of, you know, he didn't quite
write it. He created an environment in which that program could grow.
If he'd written it, he would have understood it. But he didn't. He set
up the kind of bylaws that enabled it to evolve.
Sims. Did you come out of this project with a definition of what
emergent software is?
Johnson. No, because there are different kinds. On the one hand, you
have something like Sim City, which is ... the software itself was
written in a top-down way. Somebody -- Wil Wright or whoever -- sat
down and programmed a little game that is a complex system that has all
these little interacting agents that are looking at each other and
changing, based on what they see. The software itself was created in a
more traditional way, but the tool that ends up being created is a
little emergent, self-organizing system.
Then you have what Danny Hillis did, where you're literally growing the
program itself. That model is really closer to natural selection than
to emergent software.
And then you have this idea of emergent networks. The best way to
describe that is networks that get more organized with use, that
naturally structure themselves into orderly categories the more people
use them. Which is sort of the opposite of networks that just get more
chaotic.
That's why the city example is a really important one, because cities
have this great ability, if they're set up right, to organize
themselves into neighborhoods as they get bigger. So they get these
useful categories without anybody actually planning that; it makes it
easier to navigate, better for storing and retrieving information, and
all the things that cities have done so well over history.
Dornfest. In the same manner, the mayor of that city would not
necessarily know how some of that information crosses from place to
place, just as a programmer of emergent software may not know where
those connections are made.
Johnson. Hopefully the mayor or the programmer can set up an
environment where those connections can be made, but the actual
connections are being made by the users of the system or the people in
the city.
Think about the way that the Amazon user-recommendation engines work.
They've gotten really astonishingly good, if you have a long purchasing
history with Amazon. It now has enough resolution to create these
little galaxies of related books, and relationships between books, and
it's gotten really refined. And it's been cultivated entirely by the
purchase decisions of users.
Sims. That's a situation where the cumulation of interactions benefits
you, the user. But in Emergence, you point out the example of sidewalk
interactions, which may or may not make life better for the individual,
but certainly make things better for the larger system, the city.
Johnson. I think the individual benefits, but it's a little more
indirect. I was trying to make the point that the importance of the
random interactions on the sidewalk, passing things on your way from X
to Y, swerving into Z on your way there -- which is how the links of
the city get more intelligent and more sophisticated -- that that
traditionally has been thought of by people who read [author and urban
critic Jane] Jacobs as being an aesthetic or multicultural response to
what makes cities great. You walk out on the street, and you see a lot
of diversity, and that's exciting. And that's directly good for you, as
a person. And that's a good point, but there's another point, which is
a more subtle one that I think Jacobs is trying to make, which is
public space and those interactions are what enable cities to develop
the neighborhoods and self-organizing clusters of like-minded people
that give cities such great personality, give them their texture and
flavor that are indescribable, and that make a city great.
Dornfest. In New York, no matter what guidebook you use, it's often
sort of hard to find what you're looking for, until you find the street
that feels right. Then you can pretty much stay with it, or let it
amble through the neighborhood that also has the right feel to you.
Johnson. It has an incredible resolution to it. If you're on the wrong
street, you're totally on the wrong street. And you go over five blocks
and find, "This is exactly what I'm looking for, everything I want is
right here. I'm guaranteed that the next 10 stores or five restaurants
are all going to be in the general zone that I want."
And I think the point is that this is ultimately good for the people in
the city as well. The individual's interactions create the higher level
shape of the city, which turns out to be enjoyable for the individual.
Sims. You write that more is different, and that seems to be the case
with a city the scope of New York. With smaller cities you don't really
get those districts.
Johnson. It's another way that it connects to Amazon. Early on there
were a bunch of reviews of Amazon or systems like that where the
reviewer wrote, "I went to Amazon and I told it I liked these two books
and asked it to recommend a few more to me, and it was totally wrong."
Well, there just wasn't enough data at that point. And Jacobs' refrain
in her book, where she says, big cities are not like towns only bigger;
they're a totally different beast. So more is different in both those
cases.
Sims. Slashdot seems like another example. It filled a niche, to become
a sort of watering hole for the open source community. But you wrote
about how, once it grew too big to fill its role, Rob Malda implemented
a solution that was partly top-down, but partly community managed.
Johnson. Yeah, when you get to a certain threshold of size, you start
having signal-to-noise problems in an online community, particularly in
an online community where you have people who have lots of opinions and
... some younger people [laughs]. And you can effectively solve that
problem two ways. You can hire a lot of people to patrol the boards and
delete spam and other useful information, or you can have the community
do it for you.
Malda and his crew didn't have the luxury of putting a bunch of people
on staff to do it, and I don't think they were temperamentally inclined
to do that anyway. They thought it would be better to let the community
do it, and follow an open source model in developing a community
itself. And so they built the karma system where everything was
evaluated by other members of the community, and if you contributed a
lot your karma increases. Moderation filters enable you to look at
highly rated things and eliminate things that are not highly rated by
the community. And it created a kind of currency within the system that
enabled quality contributions to rise to the surface.
It really worked, that was what was amazing about it. When you read
Slashdot at the highest filter level, it's as good as many
professionally edited tech sites.
Dornfest. What's interesting is that Slashdot worked for me to a
certain degree, and then to a certain degree it worked for me only to
the extent that I was an average Slashdot reader -- which I'm not. Even
if I turned off everything but the highest rating posting, I still find
a lot of noise to signal. What has emerged in the weblog community is
that I don't have to become an average Slashdot reader, I can say, I'm
kind of like Cory, and I'm kind of like Steven, and I'm kind of like
Dave Winer in a certain sort of way. I'll read their things, and
they'll point me to the appropriate things, including Slash articles.
So you have this wonderful after-market community. And if I decide, for
example, that Dave Winer's focusing too much on politics, I may stop
reading his blog, but I'll still get stories from him, via somebody
else.
The result is that when I wake up in the morning, I get to see a lot of
the stories that come through Slashdot or from the New York Times that
are interesting to me, without having to wade through Slashdot to find
them.
Johnson. That's a great point. I know people are working on creating
the meta-blogs, and I feel there's an incredible opening to create that
-- the thing like Slashdot that sits on top of all the blogs, and is
collectively filtered by all those bloggers and their readers. There
are a lot of different versions of that, but I don't see one that's
really solved the problem. To me, the thing that has to happen to the
individual blogs is that they're still too centered around the
personality of the blogger him- or herself. They're still too limited
to emailing the blogger, or a crude bulletin board. What I would love
to see is, one way or another, by force of personality or whatever, to
have these clusters of 100 or 200 or maybe 1,000 people who offered
real contributions and collectively owned the thing.
Sims. A blog tribe.
Johnson. Yeah.
Dornfest. Metafilter comes closest to that of the things I've seen. It
still has a personality, but it's very much a group dynamic.
Sims. There are all these little pieces being figured out -- whether
it's Slashdot's rating system or meta-blogs or the way file-sharing
systems make the files that are most in demand easiest to get -- but
can you see a point where all these little things add up to a system
that wasn't planned, but fixes the problems by grouping together.
Johnson. I had an interesting idea about that today, which is kind of a
metaphor of where we need to go. I'm writing a new book that's entirely
about the brain. Our frontal lobes differ dramatically from those of
the other primates. It's disproportionately large, and one of the
things that happens there is all the different specialized data
processing going on through the rest of the brain gets brought there
and kind of synthesized -- what's going on in the visual cortex, the
audio realm, the emotional realm. All that stuff is brought together.
I was thinking that what the Web needs is a big neo-cortex. There are
all these very specialized smart, focused tools being developed, and
data that's being mined, and collective intelligence on specific
problems. But we're not as good yet at, not just filtering all that
stuff, but figuring out what belongs connected to what else. Google is,
in a way, the beginning of that. It's letting the Web solve that
pattern itself, looking at patterns and links of what should be
connected to other things. But we need more of that kind of synthesis
going on. I think XML is going to be a great platform for that. Once
you have clear, simple markup for describing big chunks of data, it
should be easier to do that as well.
Sims. And it offers the potential of two-way linking.
Johnson. Yeah, two-way linking is kind of essential to letting the Web
evolve in an organic way.
Dornfest. After reading Emergence, I went back and read Interface
Culture again.
Johnson. How does it hold up?
Dornfest. It holds up well, actually. The thing that struck me was the
talk of exaptations. I'm wondering as I look at the Web today, what are
the interesting exaptations that are coming from the way people are
trying to extend the network, in ways that no one expected. What
interesting exaptation are you seeing in the Internet today, the last
six months, or year?
Johnson. That's an excellent question, Rael. Do you have an answer?
Dornfest. I don't yet. There was a real flurry between 1993 and 1996 in
online communities, as well as how the Web was used. But I don't see, I
have to admit, in the last year, much in terms of exaptations. I see in
the Web services world people trying to control the evolution --
Microsoft, IBM -- trying to put more intelligence into the network. But
I don't see a lot of exaptation coming from that, or coming at all. I
worry that the Internet is becoming more like, it's so big we can do
whatever we want with it. And the result is nothing evolving out of the
Internet doing what it wants to do.
Johnson. One place where I feel there's an interesting question -- and
in Interface Culture I was pretty skeptical about this idea, but maybe
I'm coming around to it -- is what's evolving out of the gaming
community in terms of virtual worlds.
Isn't Everquest supposed to have this economy that's the size of a
small country? As these worlds that were designed to let people play
Dungeons and Dragons in a virtual, networked environment, as they get
more and more users, and as they develop an actual economy within that
world, and as it translates into real-world dollars -- as people sell
their characters on eBay and stuff like that -- are we actually,
through that gaming design, is there an exaptation that leads to what
the Internet was supposed to look like, a virtual world with commerce,
a William Gibson-like system?
A few years ago, people were saying that Quake was going to turn into a
platform for virtual worlds, with little poetry readings in Quakespace.
But I think something interesting may evolve in some of these other
games.
Dornfest. There were a lot of attempts to create destination spots
around '94, '95, '96. What ended up happening is that Slashdot and
places like that became the destination spots, but they weren't as
planned. They ended up evolving. We're planning a lot today for web
services, but I wonder what the next exaptations are of that. Blogging
is obviously one piece of that. But it's more of a community around
trading stories, rather than tapping the intelligence of the community
as a whole.
Johnson. Yeah, there needs to be some other thing that comes along that
holds all of that information and turns it into some higher level
structure that can actually make sense of it all.
But there's enough innovation going along at the base level, and enough
interesting people contributing to that, that I feel kind of optimistic
that we're going to figure out interesting things to do with the
tremendous amount of data that's being produced by all those people.
David Sims was the editorial director of the O'Reilly Network.
Rael Dornfest is Chief Technology Officer at O'Reilly Media.
http://www.oreillynet.com/pub/a/network/2002/02/22/johnson.html?page=1
Immortalist
An individual consists of a "herd" of neural events, there is no known
escape from the mob rule;
The idea of the collective hive as an animal was an idea late in
coming. The Greeks and Romans were famous beekeepers who harvested
respectable yields of honey from homemade hives, yet these ancients got
almost every fact about bees wrong. Blame it on the lightless
conspiracy of bee life, a secret guarded by ten thousand fanatically
loyal, armed soldiers. Democritus thought bees spawned from the same
source as maggots. Xenophon figured out the queen bee but erroneously
assigned her supervisory responsibilities she doesn't have. Aristotle
gets good marks for getting a lot right, including the semiaccurate
observation that "ruler bees" put larva in the honeycomb cells. (They
actually start out as eggs, but at least he corrects Democritus's
misguided direction of maggot origins.) Not until the Renaissance was
the female gender of the queen bee proved, or beeswax shown to be
secreted from the undersides of bees. No one had a clue until modern
genetics that a hive is a radical matriarchy and sisterhood: all bees,
except the few good-for-nothing drones, are female and sisters. The
hive was a mystery as unfathomable as an eclipse.
I've seen eclipses and I've seen bee swarms. Eclipses are spectacles I
watch halfheartedly, mostly out of duty, I think, to their rarity and
tradition, much as I might attend a Fourth of July parade. Bee swarms,
on the other hand, evoke another sort of awe. I've seen more than a few
hives throwing off a swarm, and never has one failed to transfix me
utterly, or to dumbfound everyone else within sight of it.
A hive about to swarm is a hive possessed. It becomes visibly agitated
around the mouth of its entrance. The colony whines in a centerless
loud drone that vibrates the neighborhood. It begins to spit out masses
of bees, as if it were emptying not only its guts but its soul. A
poltergeist-like storm of tiny wills materializes over the hive box. It
grows to be a small dark cloud of purpose, opaque with life. Boosted by
a tremendous buzzing racket, the ghost slowly rises into the sky,
leaving behind the empty box and quiet bafflement. The German
theosophist Rudolf Steiner writes lucidly in his otherwise kooky Nine
Lectures on Bees: "Just as the human soul takes leave of the body...one
can truly see in the flying swarm an image of the departing human
soul."
For many years Mark Thompson, a beekeeper local to my area, had the
bizarre urge to build a Live-In Hive-an active bee home you could visit
by inserting your head into it. He was working in a yard once when a
beehive spewed a swarm of bees "like a flow of black lava, dissolving,
then taking wing." The black cloud coalesced into a 20-foot-round black
halo of 30,000 bees that hovered, UFO-like, six feet off the ground,
exactly at eye level. The flickering insect halo began to drift slowly
away, keeping a constant six feet above the earth. It was a Live-In
Hive dream come true.
Mark didn't waver. Dropping his tools he slipped into the swarm, his
bare head now in the eye of a bee hurricane. He trotted in sync across
the yard as the swarm eased away. Wearing a bee halo, Mark hopped over
one fence, then another. He was now running to keep up with the
thundering animal in whose belly his head floated. They all crossed the
road and hurried down an open field, and then he jumped another fence.
He was tiring. The bees weren't; they picked up speed. The
swarm-bearing man glided down a hill into a marsh. The two of them now
resembled a superstitious swamp devil, humming, hovering, and plowing
through the miasma. Mark churned wildly through the muck trying to keep
up. Then, on some signal, the bees accelerated. They unhaloed Mark and
left him standing there wet, "in panting, joyful amazement."
Maintaining an eye-level altitude, the swarm floated across the
landscape until it vanished, like a spirit unleashed, into a somber
pine woods across the highway.
"Where is 'this spirit of the hive'...where does it reside?" asks the
author Maurice Maeterlinck as early as 1901. "What is it that governs
here, that issues orders, foresees the futureÉ?" We are certain now it
is not the queen bee. When a swarm pours itself out through the front
slot of the hive, the queen bee can only follow. The queen's daughters
manage the election of where and when the swarm should settle. A
half-dozen anonymous workers scout ahead to check possible hive
locations in hollow trees or wall cavities. They report back to the
resting swarm by dancing on its contracting surface. During the report,
the more theatrically a scout dances, the better the site she is
championing. Deputy bees then check out the competing sites according
to the intensity of the dances, and will concur with the scout by
joining in the scout's twirling. That induces more followers to check
out the lead prospects and join the ruckus when they return by leaping
into the performance of their choice.
It's a rare bee, except for the scouts, who has inspected more than one
site. The bees see a message, "Go there, it's a nice place." They go
and return to dance/say, "Yeah, it's really nice." By compounding
emphasis, the favorite sites get more visitors, thus increasing further
visitors. As per the law of increasing returns, them that has get more
votes, the have-nots get less. Gradually, one large, snowballing finale
will dominate the dance-off. The biggest crowd wins.
It's an election hall of idiots, for idiots, and by idiots, and it
works marvelously. This is the true nature of democracy and of all
distributed governance. At the close of the curtain, by the choice of
the citizens, the swarm takes the queen and thunders off in the
direction indicated by mob vote. The queen who follows, does so humbly.
If she could think, she would remember that she is but a mere peasant
girl, blood sister of the very nurse bee instructed (by whom?) to
select her larva, an ordinary larva, and raise it on a diet of royal
jelly, transforming Cinderella into the queen. By what karma is the
larva for a princess chosen? And who chooses the chooser?
"The hive chooses," is the disarming answer of William Morton Wheeler,
a natural philosopher and entomologist of the old school, who founded
the field of social insects. Writing in a bombshell of an essay in 1911
("The Ant Colony as an Organism" in the Journal of Morphology), Wheeler
claimed that an insect colony was not merely the analog of an organism,
it is indeed an organism, in every important and scientific sense of
the word. He wrote: "Like a cell or the person, it behaves as a unitary
whole, maintaining its identity in space, resisting
dissolution...neither a thing nor a concept, but a continual flux or
process."
It was a mob of 20,000 united into oneness.
http://www.kk.org/outofcontrol/ch2-a.html
Immortalist
But the function of a component in a hierarchical system can be seen
clearly only in cases of breakdown of the system in which that
component is embedded. In such cases, we follow a localized diagnostic
procedure to isolate the cause of the breakdown and then perform one of
two types of operation to get the system up and running again: We
either replace or repair a system component or restore lost or damaged
connections among components. And when we do not fix a component it is
because, although the system as a whole has broken down, that component
has not stopped performing its function. Only a component that requires
replacement or repair in cases of embedding system breakdown has
stopped performing its function. To illustrate, if my car sputters and
stalls when I press the accelerator, but diagnosis reveals a faulty
distributor or mistimed engine, the carburetor does not require repair,
since it is still performing the function it was designed to perform of
vaporizing gasoline. Similarly, if the lights do not come on when I
flip the switch, but diagnosis reveals a bad bulb or loose connection
somewhere between the switch and the light, the switch is still
performing the function it was designed to perform of channeling
current to the proper wire when open. Thus, a breakdown in the system
that contains a component does not (necessarily) involve that
component's not performing its function. These facts imply the
following principle of functional isolation (POFI):
The function of a component is to produce the effect that it produces
in all possible cases of breakdown of its embedding system in which
that component does not require repair.
...Note that POFI does not identify the function of a mechanism via
conditions of the mechanism's breakdown, but via conditions of its
embedding system breakdown. Thus, it does not entail that the function
of a carburetor, say, is to produce the effect that it produces when it
does not require repair. A principle entailing that would engender
fallacious inferences. For example, if the carburetor requires repair,
the engine may sputter; so we would be led to conclude that the
function of the carburetor is to produce a smoothly running engine (or
worse, that its function is to suppress the sputter). POFI licenses
only the following type of inference: if the car engine is broken down,
and if the carburetor does not require repair in order to fix the
engine, then the carburetor is performing its function. Further, POFI
requires that we focus on all possible ways in which the engine can
break down without the carburetor requiring repair. So, to apply POFI
to the case of the carburetor, we must:
(i) consider all possible cases of engine breakdown;
(ii) determine the type of repair required in each case to get the
engine running again;
(iii) ignore those cases in which the engine is fixed by (a) repairing
the carburetor or (b) repairing some part or connection the breakdown
of which resulted in the carburetor's non-operation (since in these
cases the carburetor will produce no effect at all, but due to no
problem with it);
(iv) determine the effect that the carburetor produces in all the
remaining cases.
http://cogprints.org/328/00/indy&ep.htm
Geographic isolation of functionaries-e.g. central, regional and
local-is not good enough. What is to keep the regional and local
authorities from going their own way? Functional isolation is also
necessary.
The two main functions of any government are civil (tax collection,
justice) and military (internal policing, external defense).
Unfortunately, the two must often be coordinated (e.g. the police must
link to the judicial power), but if bureaucrats can get together for
wholesome reasons, they will also potentially be able to get together
to conspire against the central authority so as to overthrow or at
least gain independence from it. Hence a viable bureaucracy will set up
a third, coordinative, set of functionaries whose sole function will be
to serve as the only sanctioned link between the military and civilian
functionaries so that they can be kept isolated from each other.
THE EVOLUTION OF BUREAUCRACY
DURING THE WARRING STATES ERA
http://www.ac.wwu.edu/~kaplan/H370/ap13.pdf
Separation of powers
One of the most important of the basic principles that guided the
framers of the US Constitution in their design for America's future
governance was the idea that the root cause and essence of tyrranical
government is the concentration of control over all the powers and
functions of government in the hands of the same individual or narrow
political faction. The corollary the Framers drew from this was the
separation of powers principle: that free popular government can best
be sustained by dividing the various powers and functions of government
among separate and relatively independent governmental institutions
whose officials would be selected at different intervals and through
different procedures by somewhat different constituencies so as to make
it unlikely that the same small faction could gain control of them all
at the same time. Thus, in the American federal republic the Framers
designed, "the power surrendered by the people is first divided between
two distinct governments [the Federal government and the governments of
the several states], and then the portion allotted to each subdivided
among distinct and separate departments [the executive, the
legislative, and the judicial]." [Madison, The Federalist #51]
The idea that concentrated political power is a mortal danger to civil
liberties and popular rights remains to this day one of the most
persistent and characteristic features of American ideologies and
popular thinking about politics. In comparison with other advanced
industrial countries, the United States possesses one of the most
complex governmental structures and perhaps the most broadly diffused
distribution of governmental authority among independent agencies. Not
only do American governmental arrangements still allocate power to
separate executive, legislative and judicial branches at both the state
and federal levels, but they also feature a great variety of forms of
relatively autonomous and geographically overlapping governmental
bodies at the local level -- including not only general purpose county
and municipal governments but also a wide variety of functionally
specialized mini-governments such as elected district school boards,
flood control district boards, water resource planning boards, transit
authority boards and the like.
http://www.auburn.edu/~johnspm/gloss/separation_of_powers.html
(then again we can have fun with complexity, right?)
Self-organization
Self-organization is a process where the organization (constraint,
redundancy) of a system spontaneously increases, i.e. without this
increase being controlled by the environment or an encompassing or
otherwise external system
--------------------------------------------------------------------------------
Self-organization is a basically a process of evolution where the
effect of the environment is minimal, i.e. where the development of
new, complex structures takes place primarily in and through the system
itself. As argued in the section on evolutionary theory,
self-organization can be understood on the basis of the same variation
and natural selection processes as other, environmentally driven
processes of evolution. Self-organization is normally triggered by
internal variation processes, which are usually called "fluctuations"
or "noise". The fact that these processes produce a selective retained
ordered configuration has been called the "order from noise" principle
by Heinz von Foerster, and the "order through fluctuations" mechanism
by Ilya Prigogine. Both are special cases of what I have proposed to
call the principle of selective variety.
The increase in organization can be measured more objective as a
decrease of statistical entropy (see the Principle of Asymmetric
Transitions). This is again equivalent to an increase in redundancy,
information or constraint: after the self-organization process there is
less ambiguity about which state the system is in. A self-organizing
system which also decreases its thermodynamical entropy must
necessarily (because of the second law of thermodynamics) export
("dissipate") such entropy to its surroundings, as noted by von
Foerster and Prigogine. Prigogine called systems which continuously
export entropy in order to maintain their organization dissipative
structures.
Self-organization is usually associated with more complex, non-linear
phenomena, rather than with the relatively simple processes of
structure maintenance of diffusion. All the intricacies (limit cycles,
chaos, sensitivity to initial conditions, dissipative structuration,
...) associated with non-linearity can simply be understood through the
interplay of positive and negative feedback cycles: some variations
tend to reinforce themselves (see Autocatalytic Growth), others tend to
reduce themselves. Both types of feedback fuel natural selection:
positive feedback because it increases the number of configurations (up
to the point where resources become insufficient), negative feedback
because it stabilizes configurations. Either of them provides the
configuration with a selective advantage over competing configurations.
The interaction between them (variations can be reinforced in some
directions while being reduced in others) may create intricate and
unpredictable patterns (chaos), which can develop very quickly until
they reach a stable configuration (attractor).
http://pespmc1.vub.ac.be/SELFORG.html
Immortalist
I would say that since the poles, going into the election, were so even
that any percentage advantage would have been comfortable to an
electee. I don't think the author was refering to "margin of error" but
simply a percieved margin of value. I would have to look closer at the
article to determine the choice of meaning while using the term margin;
The margin of error is an estimation of the extent to which a poll's
reported percentages would vary if the same poll were taken multiple
times. The larger the margin of error, the less confidence one has that
the poll's reported percentages are close to the "true" percentages,
i.e. the percentages in the whole population. The margin of error can
be calculated directly from the sample size (the number of poll
respondents) and is commonly reported at one of three different levels
of confidence. The 99 percent level is the most conservative, the 95
percent level is the most widespread, and the 90 percent level is
rarely used. Formally, if the level of confidence is 99 percent, one is
99 percent certain that the "true" percentage in a population is within
a margin of error of a poll's reported percentage for a reported
percentage of 50 percent. Equivalently, the margin of error is the
radius of the 99 percent confidence interval for a reported percentage
of 50 percent.
http://encyclopedia.laborlawtalk.com/margin_of_error
Margin of Error deserves better than the throw-away line it gets in the
bottom of stories about polling data. Writers who don't understand
margin of error, and its importance in interpreting scientific
research, can easily embarrass themselves and their news organizations.
Check out the following story that moved in the summer of 1996 on a
major news wire:
WASHINGTON (Reuter) - President Clinton, hit by bad publicity recently
over FBI files and a derogatory book, has slipped against Bob Dole in a
new poll released Monday but still maintains a 15 percentage point
lead.
The CNN/USA Today/Gallup poll taken June 27-30 of 818 registered voters
showed Clinton would beat his Republican challenger if the election
were held now, 54 to 39 percent, with seven percent undecided. The poll
had a margin of error of plus or minus four percentage points.
A similar poll June 18-19 had Clinton 57 to 38 percent over Dole.
Unfortunately for the readers of this story, it is wrong. There is no
reasonable statistical basis for claiming that Clinton's lead over Dole
has slipped.
Why? The margin of error. In this case, the CNN et al. poll had a four
percent margin of error. That means that if you asked a question from
this poll 100 times, 95 of those times the percentage of people giving
a particular answer would be within 4 points of the percentage who gave
that same answer in this poll.
(WARNING: Math Geek Stuff!)
Why 95 times out of 100? In reality, the margin of error is what
statisticians call a confidence interval. The math behind it is much
like the math behind the standard deviation. So you can think of the
margin of error at the 95 percent confidence interval as being equal to
two standard deviations in your polling sample. Occasionally you will
see surveys with a 99 percent confidence interval, which would
correspond to 3 standard deviations and a much larger margin of error.
(End of Math Geek Stuff!)
So let's look at this particular week's poll as a repeat of the
previous week's (which it was). The percentage of people who say they
support Clinton is within 4 points of the percentage who said they
supported Clinton the previous week (54 percent this week to 57 last
week). Same goes for Dole. So statistically, there is no change from
the previous week's poll. Dole has made up no measurable ground on
Clinton.
And reporting anything different is misleading.
Don't overlook that fact that the margin of error is a 95 percent
confidence interval, either. That means that for every 20 times you
repeat this poll, statistics say that one time you'll get an answer
that is completely off the wall.
You might remember that just after Dole resigned from the U.S. Senate,
the CNN et al. poll had Clinton's lead down to six points. Reports
attributed this surge by Dole to positive public reaction to his
resignation. But the next week, Dole's surge was gone.
Perhaps there never was a surge. It very well could be that that week's
poll was the one in 20 where the results lie outside the margin of
error. Who knows? Just remember to never place too much faith in one
week's poll or survey. No matter what you are writing about, only by
looking at many surveys can you get an accurate look at what is going
on.
©Copyright, Robert Niles. All rights reserved.
http://www.robertniles.com/stats/margin.shtml
tooly
original idea. I've often looked down upon 'US' from the confines of an
airplane dangling 30,000 feet in the air...and it has seemed quite proper to
realize we are a 'beast' of another kind 'collectively'. The Hive is one
conceptual word that has value. I also like to think of an 'embodiment' of
some kind where many of the functions actually emulate true 'organic
processes'. Each 'cell' [human being] specializes to perform a tiny part of
each function that goes to make up this greater beast in our collectivity.
We sprawl below like a giant ameoba, searching and connecting with water,
food, resources of all kinds...whereupon we stage giant centers of activity
we call our cities...but more like 'nodes' where the flows coincide like a
spoked hub.
Anyone who has observed a sports event in a large colliseum, can readily
'sense' this aggregate mind ebbing and flowing, back and forth with what we
call 'momentum' of the game. The players and observers are interconnected,
one feeding the other, with performance, back with acknowledgement, back
again to further performance. And it can go in a negative direction as
well, where it seems almost as if the very 'energy' of a team can be drained
from it.
But as an intelligence, this mass mind must be seen as too chaotic to have
much 'usefulness' to us on individual levels; as you say, it is more or less
a 'mob'. Thusly, I still wonder if there is a 'short term' event, whereby,
we need strong leaders to somehow inspire the mob toward more 'useful'
outcomes. But in the long run, the ultimate choices of that mob would know
best. The thought is fuzzy...but interesting.
Chris Degnen
> "Where is 'this spirit of the hive'...where does it reside?" asks the
> author Maurice Maeterlinck as early as 1901.
I would say the spirit only emerges as necessity requires. When there's
no problem no attention is required. Just when an anomaly develops is
attention drawn until a solution is achieved.
Chris Degnen
> ... as an intelligence, this mass mind must be seen as too chaotic to have much 'usefulness' to us on individual levels; as you
> say, it is more or less a 'mob'. Thusly, I still wonder if there is a 'short term' event, whereby, we need strong leaders to
> somehow inspire the mob toward more 'useful' outcomes. But in the long run, the ultimate choices of that mob would know best.
> The thought is fuzzy...but interesting.
Wilfred Trotter wrote about herd direction after his experience of the first world
war. He said the war drew the British society together but the benefit was not
fully exploited because of the low quality of the information both communicated
in rumour and passed down from the government. Apparently, had there been
clearer direction the phenomenon of herd cohesion could have been better used
to make the society more efficient.
It seems though, that there are some matters that are too complicated be
explained to every member of society, so the fully intercommunicating herd
ideal of Trotter's cannot materialise for every instance. On those occasions the
hope is that those people who are qualified to decide are fully intercommunicating.
Trotter envisioned a cooperative ideal, and to be sure it sounds better than
having our experts fighting each other behind closed doors.
Chuck Brown
So, all of this in light of the fact that the vast majority of educated
people in the industrialized nations of the world consider a mixed
economic system with a decidedly strong socialistic/welfare state bent
the best way to organize the workings of a society means that roughly
51% of voting Americans probably voted UNWISELY in the November
elections? hehehehehehe
tooly
"Chris Degnen" <til...@windmills.freeserve.co.uk> wrote in message
news:d1cimg$27t$1...@newsg2.svr.pol.co.uk...
You know, I have wondered upon my own if the 'root of all evil' might be an
economic concept, 'Imperfect Information'. Even a rational person with
sound judgement would be inclined to choose wrongly if given incorrect
information; and imperfect information seems to be the more natural state of
affairs. I suppose I agree with Immortalist's proposition here about the
aggregate mind being superior...but I think a component for 'imperfect
information' has to be included in the equation. Too often, we have the
unscrupuluous, greedy, and politically minded who feed the aggregate beast
'information' for their own personal ends. The single honey bee, after all,
is probably motivated for his own psychological reason first, the hive's
second [perhaps to be recognized, to have some sense of importance, to be
rewarded with a bit more honey himself by being the one discovering a new
resource etc].