Global Brain: Chapter 4 - 5 - 6 Discussion

Chris OConnor

Global Brain consists of 21 chapters total, so I'm creating 7 seperate threads breaking the book into 3 chapter segments. Hopefully this format will keep the discussion somewhat organized and on track. You do not need to keep your discussions within these 7 threads.

Edited by: Chris OConnor at: 10/30/05 4:32 pm

LanDroid

Chapter 4 introduces the elements of a collective learning machine.

- Conformity enforcers
- Diversity generators
- Inner judges
- Resource shifters
- Intergroup tournaments

Bloom uses this five part system to describe how groups learn.

LanDroid

Chapter 4 also introduces the work of Eshel Ben-Jacobs, a physicist who studied bacteria and found they design genome changes to solve problems.

Quote:
Ben Jacobs wrote in the queen of physics journals, Physica A, that the genome can "recognize difficulties and formulate problems," "collect and process information, both about internal state(s) and external conditions (including the state of other bacteria)," and arrive at an evaluation of current needs. What's more, the genetic bundle seemed to accomplish something even compouters cannot achieve. Said Ben-Jacob, "The genome makes calculations and changes itself according to the outcome." It activates genes which were in limbo, deactivates others which had been going full steam, copies some, moves them to new locations, lengthens or shortens old strings, and comes up with fresh combinations of genetc code. It accomplishes a feat analogous to what William Shakespeare did when he rearranged existing words and phrases to write a play. Concluded Ben-Jacob, in bacteria's case "evolutionary progress is not a result of successful accumulation of mistakes, but is rather the outcome of designed creative processes."

Bloom describes Escherichia coli bacteria successfully mutating in order to digest a formerly inedible substance.

Quote:
An individual bacterium can crank nourishment out of this unpalatable medication only if it undergoes a step-by-step sequence of two genetic breakthroughs, one of which entails taking a giant step backward. The odds of pulling this off through random mutation are less than 1 in 10,000,000,000,000,000,000,000 - or, to put it in English, more than 10 billion trillion to one. Yet E. coli consistently manage it.

Ben-Jacob claims the bacteria accomplish this by functioning as a learning machine, basic activites we associate with human beings. (pgs. 45 - 46 in hardback)

Eshel Ben-Jacob and the Bacterial Cybernetics Group

Designed creative processes? Amazing stuff! This could have huge consequences for evolution - I wonder what the biology experts here make of this...

Edited by: LanDroid at: 2/8/03 11:08:03 pm

Jeremy1952

Quote:
Designed creative processes? Amazing stuff! This could have huge consequences for evolution - I wonder what the biology experts here make of this...

I make of it that we're dealing with nuts who make Behe look mainstream. Hackles went up when I referred to Global Brain as religion rather than science; but what exactly does "designed" mean? It means "god did it".

LanDroid

Sheesh, there's no need to jump from "designed" to "God", read it a little closer.

Quote:
A "creative net" of bacteria, unlike a man-made machine, can invent a new instruction set with which to beat an unfamiliar challenge. Some colony members feel out the new environment learning all they can. Others "puzzle" over the genome like race-car designers tinkering with an engine whose power they are determined to increase. Yet others collect the incoming "ideas" passed along by their sisters and work together to alter the use of existing genetic parts or to turn them into something new. The "super-mind", to use Ben-Jacob's term even sucks in lessons from other colonies and, as Ben-Jacob explains, "designs and constructs a new and more advanced genome," thus "performing a genomic leap." Such is the power of what Ben-Jacob calls a bacterial "creative web". (p. 45-46)

The bacterial communication/learning/creative network designed the changes itself. I don't see how one can find religion in a physicist making these statements. On the contrary, if these systemic activities are widespread and more effective than random mutation, it bolsters the theory of evolution.

Edited by: LanDroid at: 2/9/03 5:45:42 pm

LanDroid

Chapter 6 re-introduces the meme. Richard Dawkins describes the meme as "a unit of imitation", which belongs exclusively to human culture. Bloom expands this and states memes can be verbal and behavioral. He makes a strong case that animals learn by imitation and that their behavior can vary from region to region due to local experience.

Let the wordsmithing begin. $:\$ If you take the word "meme" to mean ideas or behavior that is observed and repeated, it isn't so controversial. The argument seems to be whether or not memes have a discrete existence like a virus, which is a bit of a distraction from the discussion.

Edited by: LanDroid at: 2/9/03 9:33:14 pm

rielmajr · Almost a regular Joined: 25 Dec 2002 Posts: 47 Gender:

The whole Global Brain idea, while interesting in a kind of abstractly sci-fi kind of way, does strike me as metaphysical (how can its central thesis ever be tested, falsified?) or, in the alternative, quasi-religious (design indeed! Has he forgotten that "natural selection" includes the word "selection"? -- meaning that fitness is a process of successive change that mimics, but is fundamentally different from, design?).

I think that pieces like the one below are better starts to understanding complex systems:

From the issue dated February 14, 2003

Unraveling the Mysteries of the Connected Age
By DUNCAN J. WATTS

What is it about complex, connected systems that makes them so hard to understand? How is it that assembling a large collection of components into a system results in something altogether different from a large collection of components? How do populations of fireflies flashing, crickets chirping, or pacemaker cells beating all manage to synchronize their rhythms without the aid of a central conductor? How do small outbreaks of disease become epidemics, or new ideas become crazes? How do wild speculative bubbles emerge out of the investment strategies of otherwise sensible individuals, and when they burst, how does their damage spread throughout the financial system? How vulnerable are large infrastructure networks like the power grid or the Internet to random failures, or even deliberate attack? And do norms and conventions evolve and sustain themselves -- or alternatively get replaced -- in human societies?

As different as all these questions appear, they are all versions of the same question -- how does individual behavior aggregate to collective behavior? As simply as it can be asked, this is one of the most fundamental and pervasive questions in all of science. A human brain, for example, is in one sense a trillion neurons connected in a big electrochemical lump. But to each of us who has one, a brain is clearly much more, exhibiting properties like consciousness, memory, and personality, whose nature cannot be explained simply in terms of aggregations of neurons.

What makes the problem hard, and what makes complex systems complex, is that the parts making up the whole don't sum up in any simple fashion. Rather, they interact with each other, and in interacting, even quite simple components can generate bewildering behavior. The recent sequencing of the human genome revealed that the basic code of all human life consists of only about 30,000 genes -- many fewer than anyone had guessed. So whence comes all the complexity of human biology? Clearly it is not from the complexity of the individual elements of the genome, which could scarcely be any simpler; nor does it come from their number, which is barely any greater than it is for the humblest of organisms. Rather it derives from the simple fact that genetic traits are rarely expressed by single genes, but by combinations.

What then of human systems? If the interactions of mere genes can confound the best minds in biology, what hope do we have of understanding combinations of far more complex components like people in a society or companies in an economy? Surely the interactions of entities which are themselves complex would produce complexity of a truly intractable kind. Fortunately, as capricious, confusing, and unpredictable as individual humans typically are, when you put many of them together, it is sometimes the case that we can understand the basic organizing principles while ignoring many of the complicating details.

Sometimes, therefore, the interactions of individuals in a large system can generate greater complexity than the individuals themselves display, and sometimes much less. Either way, the particular manner in which they interact can have profound consequences for the sorts of new phenomena that, from population genetics to global synchrony and political revolutions, can emerge at the level of groups, systems, and populations. It is one thing to say this, however, and quite another matter altogether to understand it precisely. In particular, what is it about the patterns of interactions between individuals in a large system that we should pay attention to? No one has the answer yet, but in recent years a growing group of researchers has been chasing a promising new lead. And out of this work, which in itself builds upon decades of theory and experiment in every field from physics to sociology, is coming a new science, the science of networks.

In a way, nothing could be simpler than a network. Stripped to its bare bones, a network is nothing more than a collection of objects connected to each other in some fashion. On the other hand, the sheer generality of the term network makes it slippery to pin down precisely, and this is one reason why a science of networks is an important undertaking. We could be talking about people in a network of friendships, or a large organization, routers along the backbone of the Internet, or neurons firing in the brain. All these systems are networks, but all are completely distinct in one sense or another. By constructing a language for talking about networks that is precise enough to say not only what a network is, but what kinds of different networks there are in the world, the science of networks is lending the concept real analytic power.

Understanding networks, however, is an extraordinarily difficult task, not just because it is inherently complicated, but because it requires different kinds of specialized knowledge that are usually segregated according to academic specialty and even discipline. Physicists and mathematicians have at their disposal mind-blowing analytical and computational skills, but typically they don't spend a whole lot of time thinking about individual behavior, institutional incentives, or cultural norms. Sociologists, psychologists, and anthropologists, on the other hand, do. And in the past half-century or so they have thought more deeply and carefully about the relationship between networks and society than anyone else -- thinking which is now turning out to be relevant to a surprising range of problems, from biology to engineering. But lacking the glittering tools of their cousins in the mathematical sciences, the social scientists have been more or less stalled on their grand project for decades.

If it is to succeed, the new science of networks must bring together from all the disciplines the relevant ideas, and the people who understand them. The science of networks must become, in short, a manifestation of its own subject matter, a network of scientists collectively solving problems that cannot be solved by any single individual or even any single discipline. It's a daunting task, made all the more awkward by the longstanding barriers separating scientists themselves. Our languages are very different, and we often have difficulty understanding one another. Our approaches are different too, so each of us has to learn not only how the others speak, but how they think. But it is happening, and the past few years have seen an explosion in research and interest across the world in search of a new paradigm with which to describe, explain, and ultimately understand the networked world. We are not there yet, not by a long shot, but we are making some exciting progress.

The Island of Manhattan. Twenty-two miles long and less than five miles wide, it is, on the grand scale of the world, a speck, a jewel in the mouth of the Hudson River as it pours into the North Atlantic. Up close, it is more like a vast, roaring playground. Home to nearly a million people and host to millions more every day, it is, and has been for more than a century, Gotham, the quintessential metropolis, the city that never sleeps.

But from a scientific point of view, it is something of an enigma. Even on a daily basis, millions of people, along with the private and commercial activity they generate, consume an awful lot of stuff -- food, water, electricity, gas, and a vast range of materials from plastic wrapping to steel girders and Italian fashion. They also discharge an enormous quantity of waste in the form of garbage, recyclables, sewage, and wastewater; collectively they emit so much raw heat energy, they create their own microclimate. Yet almost nothing that the city requires in order to sustain itself is actually produced, or even stored, within its own precincts; nor can it satisfy any of its own disposal needs.

Another way to understand Manhattan, therefore, is as a nexus of flows, the swirling convergence of people, resources, money, and power. And if those flows stop, even temporarily, the city starts to die, starved for nourishment or choking on its own excrement. New Yorkers are renowned for their brash confidence, projecting an air of capability even in the most trying circumstances. But really they are captives of the very systems that make life in the city so convenient.

What would happen if this infrastructure, or even part of it, were to stop functioning? Can it stop functioning? And who is in a position to ensure that it doesn't? Who, in other words, is in charge? Like many simple questions to do with complex systems, this one lacks a definitive answer, but the short version is no one. In reality, there is not even such a thing as a single infrastructure to be in charge of. Rather what exists is a Byzantine mishmash of overlapping networks, organizations, systems, and governance structures, mixing private and public, economics, politics, and society.

No single entity coordinates this bewilderingly complicated system, and no one understands it. Frankly, it is a miracle that it works at all. If this is not a nerve-wracking thought, it really should be. Complex, connected systems can sometimes display tremendous robustness in the face of adversity and sometimes display shocking fragility. And when the system is as complicated as a large, densely populated, heavily built-up city, as vital to the lives of millions of people, and as central to the economy of a global superpower, contemplating its potential break points is more than idle speculation. So how robust is New York?

On September 11, 2001, we began to find out. The events of that day illustrate many of the paradoxes encountered in the study of networks: how it is that connected systems can be at once robust and fragile; how apparently distant events can be closer than we think; how, at the same time, we can be insulated even from what is happening nearby; and how the routine can prepare us for the exceptional. The attacks of September 11 exposed, in a way that only true disasters can, the hidden connections in the complex architecture of modern life. And from that perspective, we still have some lessons to learn.

One important lesson emerged from the severe organizational crisis that was precipitated by what was essentially a physical attack. The mayor's emergency command bunker was destroyed when Number 7 World Trade Center collapsed soon after the twin towers, and by 10 a.m. the nearby police command center had lost every single phone line, along with its cellular-phone, e-mail, and pager service. Faced with a completely unexpected and unprecedented catastrophe, with almost no reliable information available, and with the threat of subsequent attacks looming large, the city needed to coordinate two enormous operations -- one rescue, and the other security -- simultaneously. And less than an hour after the emergency began, the very infrastructure that had been designed to manage emergencies had been thrown into disarray.

But somehow they did it. In what was, under the circumstances, an incredibly orderly response, the mayor's office, the police and fire departments, the Port Authority, the various state and federal emergency agencies, dozens of hospitals, hundreds of businesses, and thousands of volunteers, turned lower Manhattan from a war zone into a recovery site in less than 24 hours. In the rest of the city, meanwhile, everything continued to operate in a way that was so normal, it was eerie. The power was still on, the trains still ran, and up at Columbia, you could still go and have a nice lunch at one of the restaurants on Broadway. For all the lockdown security on the island that day, nearly everybody outside the immediately devastated area got home that night, and deliveries of supplies and collection of garbage resumed almost as normal the next day.

A few months after September 11, I heard a remarkable story told by a woman from Cantor Fitzgerald -- the debt-trading firm that lost 700 of its 1,000 employees in the collapse of the south tower. Despite (or perhaps because of) the unfathomable trauma they had just suffered, the remaining employees decided by the next day that they would try to keep the firm alive -- a decision made all the more incredible by the daunting practical hurdles they needed to overcome. First, unlike the equity markets, the debt markets were not based at the Stock Exchange and had not closed. So if it was to survive, Cantor Fitzgerald needed to be up and running within the next 48 hours. Second, while their carefully constructed contingency plan had called for remote backups of all their computer and data systems, there was one eventuality they had not anticipated: Every single person who knew the passwords had been lost. And the reality is that if no one knows the passwords, the data are as good as gone, at least on the time scale of two days.

So what they did was this: They sat around in a group and recalled everything they knew about their colleagues, everything they had done, everywhere they had been, and everything that had ever happened between them. And they managed to guess the passwords. This story is a little hard to believe, but it is true. And it illustrates, in a particularly dramatic way, that recovery from a disaster is not something that can be planned for in an event-specific manner; nor can it be centrally coordinated at the time of the disaster itself. Just as with the mayor's office, in a true disaster, the center is the first part of the system to be overwhelmed. The system's survival therefore depends on a distributed network of pre-existing ties and ordinary routines that binds an organization together across all its scales.

What was really so remarkable about the robustness of downtown New York was that the survival and recovery mechanisms used by people, companies, and agencies alike were not remarkable at all. In the immediate aftermath, nobody knew what was going on, and nobody knew how they were supposed to respond. So they did the only thing they could do: They followed their routines, and adapted them as best they could to allow for the dramatically altered circumstances. From an organizational perspective, therefore, what we should learn from the recovery effort is that the exceptional is really all about the routine.

What can the science of networks tell us about the properties of complex systems, and especially their strengths and weaknesses? The honest answer, unfortunately, is not too much -- yet. It is important to recognize that, despite 50 years of percolating in the background, the science of networks is only just getting off the ground. If this were structural engineering, we would still be working out the rules of mechanics -- the basic equations governing the bending, stretching, and breaking of solids. The vast storehouse of applied knowledge to which professional engineers have access -- the tables, handbooks, computer-design packages, and heavily tested rules of thumb -- are at best on the distant horizon. But what the science of networks can do is give us a new way of thinking about familiar problems -- a way that has already yielded some surprising insights.

First, the science of networks has taught us that distance can be deceiving. The first evidence in support of this observation came in the late 1960s in the form of a remarkable experiment conducted by the social psychologist Stanley Milgram. Milgram devised an innovative message-passing technique in which he gave a few hundred randomly selected people from Boston and Omaha letters to be sent to a single target person -- a stockbroker who worked in Boston. But the letters came with an unusual stipulation: They could only be sent to a personal friend, preferably one "closer" to the target than the current holder. Each subsequent recipient received the same instructions, thereby forcing the letters to traverse a chain of social acquaintances from initial sender to target. Milgram's question was, how many people would be in a typical chain? The answer was six -- a surprising result that led to the famous phrase (and John Guare's 1990 play) "Six Degrees of Separation."

That someone on the other side of the world, with little in common with you, can be reached through a short chain of network ties -- through only six degrees -- is an aspect of the social world that has fascinated generation after generation. Now the science of networks gives us an explanation in terms of the multidimensional nature of social identity -- we tend to associate with people like ourselves, but we have multiple, independent ways of being alike. And because we know not only who our friends are, but also what kind of people they are, even very large networks can be navigated in only a few links.

The second major insight we can gain from the science of networks is that, in connected systems, cause and effect are related in a complicated and often quite misleading way. Sometimes small shocks can have major implications. Just as a single skier can unleash an avalanche in the mountains, so too can influences that are initially small trigger, in just the right network, a cascade of events that can propagate essentially without bound. Other times even major shocks can be absorbed with remarkably little disruption. In 1997, for example, a fire destroyed a key plant of the Toyota company, halting the production of more than 15,000 cars a day and affecting more than 200 companies whose job it is to supply Toyota with everything from electronic components to seat covers. Without question, this was a first-class catastrophe. But what happened next was every bit as dramatic as the disaster itself. In an astonishing coordinated response, and with very little direct oversight by Toyota, those same companies managed to reproduce -- in several completely different ways -- the lost components, and did so within three days of the fire. A week after that, the volume of cars rolling off the production line was back at its pre-disaster level. Because Toyota managed to escape the crisis relatively unscathed, the whole incident was largely forgotten. But it could easily have failed, as could the next company faced with a similar crisis. By accounting for the networks of connections between individual decisions or events, we can see that predicting the future based on previous outcomes -- even in situations that appear indistinguishable from those in the past -- is an unreliable business.

Finally, by helping us to understand better the relationship between cause and effect that pertains to complex, connected systems, the science of networks teaches us a third lesson: that such systems, from power grids to businesses, and even entire economies, are both more vulnerable and more robust than populations of isolated entities. Networks share resources and distribute loads, but they also spread disease and transmit failure -- they are both good and bad. But unless we can understand exactly how connected systems are connected, we cannot predict how they will behave. And unless we know what kind of behavior we are trying to understand, we don't even know what it is about the network that is supposed to matter. In this manner, the science of networks may not only provide deep theoretical insight, but also yield practical solutions to currently intractable problems.

Duncan J. Watts is an assistant professor of sociology at Columbia University and an external faculty member of the Santa Fe Institute. This essay is adapted from Six Degrees: The Science of a Connected Age, to be published this month by W.W. Norton & Co.

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Section: The Chronicle Review
Volume 49, Issue 23, Page B7

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Jeremy1952 · Posted: Fri Feb 14, 2003 5:21 pm Post subject: Networks

Cool

Chris OConnor · Posted: Sun Feb 16, 2003 10:50 pm Post subject: Re: Networks

rielmajr

from the article you posted...

Quote:
...constructing a language for talking about networks that is precise enough to say not only what a network is, but what kinds of different networks there are in the world, the science of networks is lending the concept real analytic power.

Blooms seems to opt for langauge that is not precise and almost begs to be challenged. Once again, had Bloom used network or web of life or system many people on this board would have had less of an issue with his general thesis.

Quote:
Understanding networks, however, is an extraordinarily difficult task, not just because it is inherently complicated, but because it requires different kinds of specialized knowledge that are usually segregated according to academic specialty and even discipline.

LanDroid made this point in the thread about Chapters 1, 2 and 3 quite clearly. No one scientist can be expected to fully understand all of the fields or areas of study necessary to understand a network or global brain. Bloom stands on the shoulders of those giants in thier respective specialities.

Again...

Quote:
The science of networks must become, in short, a manifestation of its own subject matter, a network of scientists collectively solving problems that cannot be solved by any single individual or even any single discipline.

When the author of this article refers to the "science of networks" I'm wondering if this could also be called "systems theory." I mentioned this somewhere on BookTalk in the past, but I'll do so again. There is an incredible movie called "MindWalk" that I highly recommend to you all. The movie is about 3 very different people from varied backgrounds having a fascinating conversation as they walk along the beach. One is a poet, one a politician and one a scientist. This is the only movie I've ever seen in my life that intrigued me enough to watch two times in the very same day.

Chris

Edited by: Chris OConnor at: 10/30/05 4:32 pm

Chris OConnor · Posted: Sun Feb 16, 2003 11:05 pm Post subject: Re: Networks

From Chapter 6:

The very first paragraph in Ch. 6 holds an amazing story of how a bird species in England rapidly learned how to solve the puzzle of how to penetrate aluminum caps on milk containers.

Quote:
A few blue tits figured out how to pierce the flimsy metal so they could sip the liquid's crown of unhomogenized cream. This innovation spread so rapidly that seemingly overnight tits the length and breadth of the British Isles were fattening their bellies via dairy robbery.

Does anyone else find that fascinating? I imagine some high-IQ'd bird slamming his beak through the aluminum in front of his buddies while they took notes and then flew off to share the nifty trick with the rest of the gang. LOL

Edited by: Chris OConnor at: 10/30/05 4:32 pm


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