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Linked: The New Science of Networks
It turns out that in the past few years, a decent amount of progress has been made on this front, largely thanks to the Internet. The Internet allows scientists to exchange information and speed up research, but more pertinently it is a test subject for these kinds of large-scale interaction problems. Linked: The New Science of Networks presents both the story of how the science has developed, and what it means. Unlike much popular scientific literature, the author himself is an active participant in the field.
The biggest surprise and most important lesson of the book is that the Internet, cellular biology, society, matter, and an incredible array of other seemingly unrelated things all form a particular type of structure called a scale-free network. These types of networks have only been described in detail recently, and their study promises to be as fundamental and rewarding as, for instance, waves or diffusion. The presence of the same structure in many unrelated situations suggests that there is a deep physical or mathematical principle which governs them.
The discovery of this principle is the subject of the first half of the book, which is a sort of detective story that leads from the most primitive concepts of graphs, as pioneered by Euler, to the state of the art. It is very interesting in itself to see how inconsistencies in mathematical models have led people to develop more and more accurate ideas of how such networks function. There is a tiny amount of math in the footnotes available for those who want it, but generally no prior knowledge is required. The author writes with plenty of anecdotes, especially in the beginning starting out with such introductions as this one of Paul Erdos:
"One afternoon in late 1920s Budapest, a seventeen-year-old youth cantered with a weird gait through the streets and stopped in front of an elegant shoe shop that sold custom-made shoes ... After knocking on the store's door-an act that would have seemed just as odd back then as today-he entered, ignoring the saleswoman at the counter, and went up to a fourteen-year-old boy in the back of the shop.'Give me a four digit number,' he said.
'2,532,' came the wide-eyed boy's reply . . .
'The square of it is 6,441,024,' he continued. 'Sorry, I am getting old and I cannot tell you the cube.'"
For another example of both the writing style and the unusual content, the author humorously describes the discovery of a similarity between Bose-Einstein condensation and economic monopoly:
"Essentially Microsoft takes it all. As a node, it is not just slightly bigger than its next competitor. In the number of its consumers it simply cannot be compared. We all behave like extremely social Bose particles, convenience condensing us into a faceless mass of Windows users. As we purchase new computers and install Windows, we carefully feed and maintain the condensate developed around Microsoft. The operation systems market carries the basic signatures of a network that has undergone Bose-Einstein condensation, displaying clear winner-takes-all behavior."
The rest of the book devotes a chapter to a particular example of a network: epidemics, the Internet, economics, etc. One thing is abundantly clear: the more we know about how these things work, the better we'll be able to curb DDOS attacks, stop disease, and control economic failures. An unlikely example of a scale-free network is the cell. It turns out that the interactions among a cell's proteins can be modeled this way, and if we could only understand it, we would be able to come up with treatments analytically, instead of by trial and error as it is done now.
It seems to me that with a greater understanding of networks, we will be able to finally advance in many fields in which progress is currently stalled. From firefly research to AIDS treatment, this is the Next Big Thing.
You can purchase Linked from bn.com. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.
Here is the book's official site.
This is the photos page, with photos like.. umm... this.
CS Monitor (thumbs-up)
Nature (ho-hum)
Computer User (thumbs-way-up)
Ahhh... Duhhh... the higher the level of abstraction the more complex the problems become because we don't really COMPLETELY understand the lower levels of abstraction. The errors in our assumpts manifest themselves in more unpredicable ways as we base higher and higher level concepts on those models.
One of the required classes for my Engineering degree was a course in the mathematics behind networks. It was without a doubt one of the most difficult coursework I have ever experienced. Even with all of the work we performed to create mathematical models of network nodes, etc., they were still unrealistic due to the overall complexity of "real" networks. For example, basic router queuing assumes the packets have an incoming probability Poisson distribution and outgoing has an exponential distribution. This is just an approximation used to allow us to get our arms around the problem. If you examine this model closely, you will find out that it implies that the packets that enter aren't necessarily the same size when they leave! Other issues like probabilistic routing rather than trying to model "smart" routers that adjust based on traffic patterns, etc. aren't usually modeled either.
... Wolfram's, "New Kind of Science" and Fritjof Capra's, "The Web of Life" to get a tremendous sense of convergence of many fields and principles. The incredible interconnectedness of things makes you wonder how anyone can claim to have " ... found the gene for ..." or dare to think that their actions only have local repurcussions. You listening, George?
"Consensus" in science is _always_ a political construct.
We do know the answer to everything is 42. That's one unknown variable down and how many trillion to go?
Wouldn't that be Nexialism?
the preceding comment is my own and in no way reflects the opinion of the Joint Chiefs of Staff
The science of networks is not so new, but it is gaining importance rapidly. I'm interested in the application of network theory to the flow of information in structured populations. Network theory would be part of this, but so would other social theories (kinship, information, psychology, etc.)
for interesting papers on networks go to:
http://www.santafe.edu
the center for the science of complexity
Logic, macros, and more
"societies", and you don't even have to know how the individuals act individually.
I agree with him, we knew how the solarsystem (society)worked long before we knew how atoms (individuals) worked.
You cannot use the knowledge of individuals to analyze society, just as you cannot use the knowledge of society to analyze individuals.
If you want to know how society works, study society, not individuals.
These are just my opinions though.
(Don't call me redundant if somebody else wrote something similar while I wrote this =) )
"GNU's not Unix....it's Linux" / Kami "kokamomi" Petersen
"We know how people act individually, and yet we can't extrapolate the behavior of entire societies from this."
There just happens to be an entire discipline dedicated to exploring the behavior of entire societies. It's called sociology.
Within society, there's an entire sub field that's been studying social networks for years. Things like how information is spread, how people get jobs, how diseases like AIDS spread, all have been explored using social network analysis.
If you want a mathematical description of "tipping points", take a look at Mark Granovetter's work on threshold models of collective behavior. Gladwell's book is based his work (though he only references Granovetter's work on how people get jobs).
How does information spread through society?
Rumors.
could you give a more thorough description of sociological threshold models? thanks
Logic, macros, and more
We know a great deal about how atoms interact, but we aren't so sure about how to combine them to make a 'big picture' of matter.
Uh... you mean... uh... chemistry?
'Cause if you're talking about some other big picture of matter, fill us in.
There are no trails. There are no trees out here.
According to chaos theory, infinitely small changes can cause ripples that echo through iterative equations so that there are infinite number of "strange attractors" near which all other results of the equation are, as long as you keep re-iterating. With infinite re-iterations, there are infinite strange attractors. We can only go forward and see where the road takes us, because it is not possible to take into account everything, unless you accept the fact that the whole universe is an equation, re-iterating itself infinitely... oh damn, I don't know what I'm writing. Shoot me!
Please stop drawing analogues between socioeconomical politics and physics.
Wasn't it enough that darwinism was used to promote fascism and ultraliberal capitalism and Einstein's relativity was used to promote moral relativism. All out of context, of course, but still bought by the people and - even worse - the politicians.
The owls are not what they seem
We don't know how to build large informational networks? I'd say the internet is a smashing success....
I liked Stephenson's idea of information as a virus. The "tipping point" was when the virus had reached a critical mass and became part of the basic store of information. Some info-virii, like Ford is better than Chevy doesn't infect enough people to tip society one way or the other. Other virii like the Earth revoles around the Sun, has infected basically the entire planet, and as such is passed from generation to generation.
I really don't think there isn't much complexity that can't be explained by the mere fact that we are all actually living on top of a Giant's head
"This isn't a study in computer science, its a study in human behavior"
It turns out the all the mathematical deductions weren't valid, since observed network traffic consists of bursts of emission, instead of regular streams. So the author suggested that a whole different theory needed to be developped to understand networks better.
So I think assuming a Poisson distribution falls in the 'theoric' branch... or does it?
Bose and Einstein are added to the black list of the OSS/linux zealot guild...
The author seems to make a claim, then dwell on it for entirely too long -- boring you to tears. If the book was rewritten, I imagine it'd be half as long, if not shorter. Honestly, how long does it take to explain the kevin bacon theory? He seems to think its profound that the "degrees of seperation" are getting smaller as we become more interconnected. I appreciate the effort, but it's common friggin' sense. All in all? An interesting read if all your other books are finished and theres nothing on TV. Even in that scenerio, I'd still probably end up reading (okay okay, looking at the pictures) in Wolfram's book instead ;)
I won't research for you, but if you're interested, the preprints archive at LANL has a lot of relevant theory. Basically, the current research is trying to come with a unified framework for so-called "phase transitions" in stochastic discrete processes. One of the most studied problems is the transition between "easy" and "hard" problems in 3-SAT (three-satisfiability). Brian Hayes has a very readable article about this phenomenon, with references. The authority in this field seems to be Gabriel Istrate.
The emergence of the giant component in random networks is a mature field of research, of course pioneered by Erdös, and with players of the likes of Don Knuth and Doron Zeilberger.
From a mathematical standpoint, Graph Theory per se is not really complicated, what actually is is the asymptotic analysis of stochastic processes.
HTH,
Matas
BTW, what's this book about?
I don't need no instructions to know how to rock!!!!
If "Linked" sounds interesting, check out "Six Degrees; the Science of a Connected Age" by Duncan J Watts. Watts covers the nuts-&-bolts of fractal networks much better than Barabasi, plus he's a lot less conceited & a better read to boot.
S/N:R
Amazon link
From the Amazon reviews:
Duncan Watts uses this intriguing phenomenon--colloquially called "six degrees of separation"--as a prelude to a more general exploration: under what conditions can a small world arise in any kind of network?
The networks of this story are everywhere: the brain is a network of neurons; organisations are people networks; the global economy is a network of national economies, which are networks of markets, which are in turn networks of interacting producers and consumers.
Food webs, ecosystems, and the Internet can all be represented as networks, as can strategies for solving a problem, topics in a conversation, and even words in a language. Many of these networks, the author claims, will turn out to be small worlds.
There's a place in bifurcation when diversity rapidly advances. In the formula:
k=>0-4
a= >0, 1
a=k*a*(1-a)
The diversity of (a) "tips" when the value of k is
somewhere around 3.4 or something.
But your question is not very clear to me. Many things have some measurable aspect that bifuractates. If you try to rephrase your question, I will try to rephrase an answer.
This is true from a practical standpoint, but not necessarily true from a theoretical standpoint. It is, given enough knowledge, and barring too much non-deterministic misbehavior from quantum mechanics, to derive behaviors of larger systems from their constituent parts. However, it takes a lot of very careful calculations, and thus is usually infeasible, and is often prone to error. Thus, it's only used where it's both really absolutely necessary and where there's a lot of money available to fund the simulations. Thus only a few isolated things -- explosions of nuclear weapons, for example -- are simulated by individually modeling the constituent microscopic parts.
Now with societies, this might be the only way to go. We don't really have enough examples of societies to be able to glean at a macroscopic level the abstract features of societies while not being tripped up by merely accidental and inconsequential features. Thus modeling individual behavior might give more insights. However, constructing such a model accurately is likely to be even more difficult than constructing an accurate model of a nuclear explosion, since people tend to behave in less predictable ways than atoms and electrons do.
10 PRINT CHR$(205.5+RND(1)); : GOTO 10
If there was to be a big brake through in this feild then it would positive (and much of it is) there is also a down side. If some of the sciences that evolved from this were usefull in understanding how large groups of people behaved, then this extra information would be used to control us.
Sorry, I'm tired. That should be:
where a is a number greater than 0 and less than 1
where k is a number greater than 0 and less than 4
a=k*a*(1-a)
Oh that's already underway. DARPA's using scale-free networking principles to enhance Total Information Awareness (the better to track you with, my dear). Seel e/?ar t=9056282&pg=0 (reg req'd) for all the gory details.
http://www.strategy-business.com/press/artic
S/N:R
Barabasi and co-author Albert are literarily inventing a new field of physics/math; I'm not even quite sure of what to call it. However, they are very much in touch with current research in the field, and their work is very timely (who else could tell you that the "degree of separation" on the web is 19 and not 6?)
As for Wolfram, however, I cannot say the same. I've seen Wolfram present his book in a special seminar (but haven't read it), and my impression is this: he is an exceptionally bright guy, but not in touch with current research. Wolfram is able to explain a wide variety of fields within physics and mathematics with great confidence, and I would be the last to call him un-educated (no two-week crash course in particle physics on his behalf! Actually, I think he was the only grad-student that Richard Feynman supervised!). I realize that when you "invent paradigm-changing science", you will necessarily meet some opposition from other researchers, but Wolfram's problem is this: he had a good idea some 20 years ago (cellular automata), secluded himself in a room since then developing his idea (as well as various sales-pitches for Mathematica), and forgot to consult with the rest of the scientific community. I understand very well why he's being critizied by his peers.
Sociology attempts to study societal behavior by empirical means. This is impossible if you assume humanity comes down to more than definable variables.
Philosophy is the only way to make progress in understanding human behavior, but because post-modern thought equates rationality with empirical logic this is an increasingly scoff at field of thought.
On the other hand, to assume that humanity has meaning (of any kind), is to assume more than cause-and-effect relationships. To disagree with this point leaves you in a state of nonexistance.
Kind of amusing with the individual and society analogy to computers and large networks. If you ask a person when no one is around about something you will get one answer but put him around a group of his peers and his answer changes because of peer pressure. Kind of amusing how this can be placed into computers as well. You have dominent members of society which form and shape it just like you do in the network community (Servers).
Yes but...
I took two Sociology courses at Humber College and York University and both times my teachers and advanced students were ardent Marxists.
There was no math to speak of either. I think Sociology is a bogus discipline designed to get communists into our school system.
It's Christmas everyday with BitTorrent.
I'm reading this at the moment. I'm a bit of junky for universality and complexity.
Most of these books are journalistic endeavours indulging in overcooked analogies. They all drivel on - its like revelationary religious evangelism as each book includes the phrase "and suddenly I looked at X understanding its full Y for the first time" - be it chaos, complexity, self-organisation, wolfram, barabasi...
Curious that these books including the damn Wolfram tome typically just rephrase computer science. It should have been called information science and then peeps might realise that its fundamental.
Read 'em by all means but keep your scepticism until they actually say something useful.
Zu
We don't need to "really COMPLETELY understand the lower levels" to work at higher levels. Surgeons don't reason at the level of metabolic pathways. Architects don't work out the quantum chemistry of their concrete. You might remember gas laws from high school physics (PV=nRT) that gives good predictions for the behavior of a gas without mentioning the behavior of the constituent atoms.
This field is difficult because it's new. Give us some time to have insights, build models, and teach them; only then can we get back to the state where everything is clear and obvious and research is a waste of time, money, and effort.
No, I'm not bitter about being a grad student, why do you ask?
that society is not an entity, thus cannot be judged. Society is only a collection of individuals, nothing more.
- bill x .15 for good service .20 for great service
- bill x
- $.01 for crappy service
http://www.missionfaces.com/
Heard of emergent behaviours?
This is behaviour that only arises through the complex interactions of many components, and it is not deducable from analysing a single component in isolation.
OK, maybe collect these behaviours and look for similarities between components and emergent behaviours. But it cannot be determined analytically beforehand.
Quite an intersting topic in relation to these network issues. Is Intelligence emergent etc.
The square of 2,532 is actually 6411024. The cube is 16232712768.
Yep, catastrophe theory
Comment removed based on user account deletion
And you've just agreed with author. Sociology doesn't study individuals. It studies flocks and swarms. Sociology does not study large numbers of individuals, then try to predict how those individuals will react socially. Instead, it looks for trends in societal behavior without much weight being given to the individual units.
The book is good, but it doesn't get into the really hard stuff - how does something get from point A to point B in a loosely coupled network, or any network?
His description of the neuron network in the brain, for example, talks about how some neurons link some parts of the brain with others, and that random links help the brain (and networks) function. But nowhere does he say how a signal actually gets from point A to point B - just that the loose coupling and random connections between brain areas make everything closer together.
. Maybe he doesn't know? Maybe nobody knows? But the whole point of the book is "connect tightly at the micro level, connect the micro groups with their immediate neighbors, and connect each micro grouping randomly with other non-local micro groupings for better connectivity."
We understand how an individual computer works, but how to build large informational networks with computers is another thing entirely.
Have you heard of this "internet" thing yet? Al Gore created it and all your friends are doing it.
We know how people act individually, and yet we can't extrapolate the behavior of entire societies from this.
Its called "demographics". Yep, you're part of it.
How does information spread through society?
People have been reading and writing for centuries now.
One thing is abundantly clear: the more we know about how these things work, the better we'll be able to curb DDOS attacks, stop disease, and control economic failures
Alright! Now that's my idea of a good time... I'd hate to come down with the flu, lose my stocks, and suffer a DDOS attack all on the same day. Oh, AIDS too? Even better.
Skiers and Riders -- http://www.snowjournal.com
Calling it a "science" is sort of a misnomer. Sure there are people studying networks scientificly, but Linked is a lot of metaphors and comparisons rather than a quantitative or modelling approach. It runs in the same vein as Wolfram's ANKOS, in that they are both missing critical intermediaries needed to qualify them (to me) as a science. My studies have been in geography, which presents a whole different level of network behavior and construction. The book is good, but a little light on science.
People who think they know everything really piss off those of us that actually do.
There is a book by Dr. Kennedy from purdue that studies things along these lines. I am over halfway done with it, and its excellent. Social intellegence is the basic gist, exhange of ideas/memes/data at the local level with very simple rules creating a global intellignce (dont flame me, thats just one general idea in the book). Another model they cover is the Axelrod Cultural Modal, which shows how complex problems can be solved via a social model via very simple rules.
s ea rch/isbnInquiry.asp?userid=51BG85LTOC&isbn=1558605 959&TXT=Y&itm=1
obligatory linkage:
http://search.barnesandnoble.com/textbooks/book
Its funny you mention social structures and darwinian evolution. They go together nicely. I am part of research team at the University of Maryland (sorry, no public website) that is currently working on the emergence of social heirarchies, like centralized control (fascism) in multi-agent systems. And guess what we are using to direct the emergence? Genetic Programming.
We do? I don't think so. In order to extrapolate societal behaviour, one needs to completely subscribe to human behaviouralism. Networks of humans are less deterministic than their particles precisely because human behaviour isn't predictable. Our current error in describing human behaviour quickly compounds when describing several or many interacting humans.
Our lack of progress in sociology is a testament to our lack of understanding of the individual.
is this science, or the occult? or the science of the occult?
try to tell someone the cure for cancer lies in foreign policy and they'll burn you for heracy.
tell them quantam mechanics can be described by studying the cold war and the space race and they'll have you committed, then burnt for heracy.
or even worse.... they'll call you a pseudoscientist or 'new age'.
yes friends, the universal lifeforce exists and permeates through your spleen as well as it does through your pet rock.
"as above, so below"
Here's a couple of examples of networks that exhibit a scale-free topology.
WikiWiki.
This shows that Wiki sites are characterized by the Pareto distribution (a.k.a. power law distribution).
RPM dependency graphs.
Out of curiousity, I wrote a quick script to compute the distribution of the number of links in the RPM dependency graph. It does seem to follow the Pareto distribution.
Slashdot
Although I have no easy way of verifying this, my gut feeling is that the network of Slashdot users is also scale-free, if we define the notion of a link between two users as follows. User bobdc is linked to user bugbear, if bobdc has replied to any of bugbear's post (or submissions) at least once.
This definition allows us to introduce the notion of a CmdrTaco number, similar to the Kevin Bacon number. Specifically, user Joe Schmoe has the CmdrTaco number of 1, if CmdrTaco has replied to any of Joe's comments. If Joe responded to wuliao's post, then wuliao has the CmdrTaco number of no greater than 2, and so on.
Pareto distributions are pretty common. For example, the number of downloads on SourceForge follows the Pareto distribution.
This page provides a fairly comprehensive list of further reading on the subject.
A closely related field, where there is probably lots of overlap, is Chaos Theory.0 092501/qid=1043352869
For a good starter on that I recommend "Chaos" by James Gleick, a most excellent book. It both describes chaos theory extremely well and is engaging and readable.
Gleick's site is here:
http://www.around.com/
His page on the book is here:
http://www.around.com/chaos.html
And here is an Amazon.com link:
http://www.amazon.com/exec/obidos/tg/detail/-/014
Happy reading and thinking.
"I don't know half of you half as well as I should like, and I like less than half of you half as well as you deserve."
That's why Asimov didn't suggest that psychohistory could predict events on a single planet - in fact, he explicitly said it could not. It was only in his galactic empire, comprising millions of worlds and quadrillions of individuals, that he suggested (fictionally) that individual actions might average out.
Yeah, don't call me redundant either. But for the record Asimov was himself a scientist who hosted a radio show, and wrote or collaborated on a great number of text books.
I think you have a good point going though. Yet it seems to me that eberytying that happens is based on the layer below that. We can see this in atomic structure because we can roughly predict how atoms will behave based on their configuration, and from that we can find out how molecules will behave, and there we can move up the chain making one series of conclusions to the next. After a certain point though, if you made one mistake or one wrong assumption then the whole thing is bunk. THis is sort of like Human societies, you can predict general trends but not specific directions and ideas, you can tell if there is going to be an economic recession or if there is going to be a boom because of certain factors, but you can't tell what will be the latest craze with the Kiddies.
However, a knowledge of how something behaves will only allow you to deconstruct an objet into smaller parts, it is then important to figure out how each of those pieces work.
Sorry about the anonymous account but the account password hasnt' been e-mailed out to me yet.
Sociology is full of these things. There is simply no one view of how we humans work by design, not to mention to which extent we are affected by external circumstances. One of the most significant splits lie in the idea of the "natural" behaviour of man. There is still a great many people, mainly in social sciences, who believe in the ideas mainly formulated by Rousseau, that all the moral faults of man, all vice and egoism, can be blamed on society itself (an idea that I believe helped form the ideologies of the anarchist movements). Experiments (and quite heartless experiments by todays standards btw) conducted as early as during the 1700s, failed ingraciously to support this idea. (Example: A young native child was taken from his mother in a colony (I believe it must have been the island St. Bartholomy, the only real colony ever held by the Swedish crown) and taken to Stockholm to be brought up at the court without any moral guidance or rules of any kind. The idea was that if the ideas of Rousseau were correct, he would grow up something of a saint with an unquelchable thirst for knowledge, moral enlightnement etc. As the common sense of most people would dictate, the kid grew up a total pest, pulling evil little pranks on everybody in his surroundings and eventually had to be sent home).
Gee. That was a long post. But this kind of topic really gets me going.
"Everyone who believes in telekinesis, raise my hand..." - James Randi
If you're interested in learning more about the large body of literature in this area, be sure to visit the INSNA web site. I think you'll find it much more informative than reading popular books on the subject.
-Carter
If you examine this model closely, you will find out that it implies that the packets that enter aren't necessarily the same size when they leave!
Isn't that the case whan fragmentation happens?
.. is that Wolfram is an asshole who gives himself personal credit for thinking up chaos theory by a different name, and Capra is a quack who likes to try mating science with mysticism, and then assumes the result can realistically be considered science by any stretch of the word.
Barabási is worth taking seriously.
It may be a radical change in thought, but it's a radical change in thought that already occurred while Wolfram was composing his precious book. It's called chaos theory. Study it. I recommend the writings of Gleick, Briggs, Peat, Prigogine (for a primary source), a few different Lorentzes, Henri Poincare. These are just a few people who Wolfram wrote out of existence when he decided to give himself a nice big pat on the back for "inventing" a "new" kind of science.
Try to understand the theory of the cosmic gravity compression wave and it is explains the dynamics of the many absolutely perfect. Unfortunately it is still rare..
regards
Gerald
Most of the "tipping point" theory (which goes back at least to Erdos' 1960 random-networks paper) looks at how gradual accumulation can lead to sudden shifts in system properties. Good stuff, and relevant to situations from Darwinian evolution to traffic-jam analysis, but not really new.
However, the work of Sante Fe researcher Stuart Kaufmann (The Origins of Order, etc.) gives a whole new direction, showing how complex, interlocked systems can arise in some circumstances by winnowing a more complex chaotic system that arises naturally. It sounds circular until you look at it carefully, but Kaufmann backs up his analysis with extensive computer simulation as well as a deep analysis of genetic control processes (Kaufmann's original specialty).
These ideas can be used far beyond the biological settings for which they were first developed. Examples range from the crystallization of activity patterns in a new organization or cultural area to the process of learning itself, where the "aha" experience marks the emergence of a set of coherent concepts from the overflowing cloud of ideas that sets the stage for it.
Adding Kaufmann's ideas to your set of explanatory tools will permit you to resolve many complex-systems questions that are otherwise intractable. And computer types are particularly well-situated to understand and use his arguments.
-Carter
then you didn't take the right classes. Many sociologists use mathematical and statistical models to understand human societies/groups. Perhaps there are lots of Marxists because it appeals to those who see injustice in society, but it is just as diverse in political opinion as any disipline (at least, measured as a range, rather than a variance ;)
I vote the "Off-topic" moderation comment be removed.
Yeah, let's not try to understand how epidemics spread, let's just appreciate it.
There's another book on the subject of networking... Nexus: The science of Small Worlds.... This book basically talks about the archetecture of efficiently connected networks and topics such as the "6-degrees of separation" phenomena. The easy explanation for the 6-degree problem (that we're all connected by about 6-degrees... you know somebody who knows somebody....). The simple answer is that networks that are randomly connected tend to have small degree separation. But in reality, most networks (social etc.) have an organizing principle (you tend to know more people in your area or interest group than totally random people). Any attempt to mathematically model a network with an organizing principle quickly reveals that there are many points with huge numbers of degrees between them. As it turns out that introducing just a small number of random points (small enough so that the network is still statistically ordered by the principle and not considered "random" overall) all of a sudden shrinks the number of degrees. What's cool about this is that examples of such networks turn up in brains, swarms of lightning bugs that synch. their flashes... etc. I'm sure that we are reaching a tipping point in the science of tipping points!
'Give me a four digit number,' he said.
.
:)
'2,532,' came the wide-eyed boy's reply . .
'The square of it is 6,441,024,' he continued. 'Sorry, I am getting old and I cannot tell you the cube.'"
Actually the square of it is 6,411,024
In Asimov's Foundation Series, a principle of psychohistory is that one cannot predict the future based off of individual behaviour but only on its statistical aggregates. It is interesting therefore that Seldon is the individual which single-handedly transforms the galaxy, and I wonder, could psychohistory have predicted what Seldon accomplished?
Logic, macros, and more
they have such fancy cool words that don't mean anything.
ok maybe i'm trolling, of is it flamebaiting?
i'm not sure what you mean by empirical logic. but I am personally critical with the post-modern critique in anthropology of rationality. they seem to assume that rationality means profit maximization and vulgar utilitarianism. but i think that people can rationally pursue irrational ends. utility is a function of cultural and personal values. i want to smoke. it gives me a utility. i value it. i pursue it in a rational manner by lighting up. but of course, if i value health, then maybe i shouldn't light up. the rationality of any given activity is determined partially by the questions frame of reference. perhaps then we should talk about actions as being made up of the physical and the intentional. wait! isn't this Hegel? bah i'm not up on my philosophy.
Logic, macros, and more
Besides being a great read, Barabási filled in a tremendiously important gap in computing graph theory which started with Milgram's Small Worlds work, continued with Watts & Strogatz book on Small Worlds and 6 degrees of separation, and ended up with Huberman and Watts & Newman's great work in power law search and dynamics. Check out (blatant self serving) http://backspaces.net/PLaw/ which discusses what these guys went thru. Barabási and his folks figured out, among other things, how small worlds can be power law worlds. They are definitely changing our world.
Thanks for the steer. I have read Tipping Point and was inspired by it. I was therefore thinking about buying Barabási's book, I checked our library and it is on loan and has three holds on it. So there must be a buzz.
INSNA site is good... but like all new subjects I need a pioneer or a guide to lead the way and this is where "popular writers" do the trick. Many academic journals are written to further a debate on small element of theory. Which to the general reader - myself - are not the way in.
marxism does not equal communism (though communism is founded on many of the principle's of marxism)
Got an ISBN, or a link at Amazon?
Thanks!
If you want a more technical (but still approachable) introduction to social network analysis, you might want to look at Wasserman and Faust's 1994 Social Network Analysis: Methods and Applications. This one is a getting a little dated, but it's a still the broadest methods text available. John Scott wrote a little book simply called Social Network Analysis some years back (don't recall the publication year) which may be more approachable yet, although it is much more limited. Really good, up-to-date texts are hard to find in such a rapidly evolving field, but these are adequate to get you sufficiently prepared to start reading the scientific literature (which is where the real action is).
-Carter
It seems that this book is trying to show people what the government has known for years and what big business is becoming experts on.
If you know how the network works, you can make very high level decisions based on calculated cause and effect. For example, what might seem like a bad decision at first may eventually give you the outcome you desire.
In a world where all avenues seem tapped out and it's hard to get ahead, I believe networks are one of the keys to breaking through.