Arbitrarily selected individuals (N=296) in Nebraska and Boston are asked to generate acquaintance chains to a target person in Massachusetts, employing "the small world method" (Milgram, 1967). Sixty-four chains reach the target person. Within this group the mean number of intermediaries between starters and targets is 5.2. Boston starting chains reach the target person with fewer intermediaries than those starting in Nebraska; subpopulations in the Nebraska group do not differ among themselves. The funneling of chains through sociometric "stars" is noted, with 48 per cent of the chains passing through three persons before reaching the target. Applications of the method to studies of large scale social structure are discussed.
In chapter 9 Kauffman applies his NK landscape model to explain the evolution seen in the Cambrian explosion and the re-population following the Permian extinction. He then follows it up with some interesting discussion which applies it to technological innovation, learning curves, and growth in areas of economics. The chapter has given me a few thoughts on the shape and structure (or “landscape”) of mathematics. I’ll come back to this section to see if I can’t extend the analogy to come up with something unique in math.
The beginning of Chapter 10 he begins discussing power laws and covering the concept of emergence from ecosystems, coevolution, and the evolution of coevolution. In one part he evokes Adam Smith’s invisible hand which seemingly benefits everyone acting for its own selfishness. Though this seems to be the case since it was written, I do wonder what timescales and conditions it works under. As an example, selfishness on the individual, corporate, nation, and other higher levels may not necessarily be so positive with respect to potential issues like climate change which may drastically affect the landscape on and in which we live.
This book originated from a series of papers which were published in "Die Naturwissenschaften" in 1977178. Its division into three parts is the reflection of a logic structure, which may be abstracted in the form of three theses:
A. Hypercycles are a principle of natural self-organization allowing an integration and coherent evolution of a set of functionally coupled self-replicative entities.
B. Hypercycles are a novel class of nonlinear reaction networks with unique properties, amenable to a unified mathematical treatment.
C. Hypercycles are able to originate in the mutant distribution of a single Darwinian quasi-species through stabilization of its diverging mutant genes. Once nucleated hypercycles evolve to higher complexity by a process analogous to gene duplication and specialization. In order to outline the meaning of the first statement we may refer to another principle of material self organization, namely to Darwin's principle of natural selection. This principle as we see it today represents the only understood means for creating information, be it the blue print for a complex living organism which evolved from less complex ancestral forms, or be it a meaningful sequence of letters the selection of which can be simulated by evolutionary model games.
A mathematical model could lead to a new approach to the study of what is possible, and how it follows from what already exists.
How are the decentralised technologies we're working on going to make people more vulnerable?
A Problem with Transcripts
In the past few weeks, I’ve seen dozens of news outlets publish multi-paragraph excerpts of speeches from Donald Trump and have been appalled that I was unable to read them in any coherent way. I could not honestly follow or discern any coherent thought or argument in the majority of them. I was a bit shocked because in listening to him, he often sounds like he has some kind of point, though he seems to be spouting variations on one of ten one-liners he’s been using for over a year now. There’s apparently a flaw in our primal reptilian brains that seems to be tricking us into thinking that there’s some sort of substance in his speech when there honestly is none. I’m going to have to spend some time reading more on linguistics and cognitive neuroscience. Maybe Stephen Pinker knows of an answer?
The situation got worse this week as I turned to news sources for fact-checking of the recent presidential debate. While it’s nice to have web-based annotation tools like Genius and Hypothes.is to mark up these debates, it becomes another thing altogether to understand the meaning of what’s being said in order to actually attempt to annotate it. I’ve included some links so that readers can attempt the exercise for themselves.
Recent transcripts (some with highlights/annotations):
- Fact Check: Trump And Clinton Debate For The First Time
- The first Trump-Clinton presidential debate transcript, annotated
- Transcript: The most important exchanges of the presidential debate, annotated
- Transcript: Donald Trump’s full immigration speech, annotated
- Transcript: Here are words Trump just used to talk about ‘the cyber’
Doubletalk and Doublespeech
It’s been a while since Americans were broadly exposed to actual doubletalk. For the most part our national experience with it has been a passing curiosity highlighted by comedians.
n. (NORTH AMERICAN)
a deliberately unintelligible form of speech in which inappropriate, invented or nonsense syllables are combined with actual words. This type of speech is commonly used to give the appearance of knowledge and thereby confuse, amuse, or entertain the speaker’s audience.
another term for doublespeak
see also n. doubletalk 
Since the days of vaudeville (and likely before), comedians have used doubletalk to great effect on stage, in film, and on television. Some comedians who have historically used the technique as part of their acts include Al Kelly, Cliff Nazarro, Danny Kaye, Gary Owens, Irwin Corey, Jackie Gleason, Sid Caesar, Stanley Unwin, and Reggie Watts. I’m including some short video clips below as examples.
A well-known, but foreshortened, form of it was used by Dana Carvey in his Saturday Night Live performances caricaturizing George H.W. Bush by using a few standard catch phrases with pablum in between: “Not gonna do it…”, “Wouldn’t be prudent at this juncture”, and “Thousand Points of Light…”. These snippets in combination with some creative hand gestures (pointing, lacing fingers together), along with a voice melding of Mr. Rogers and John Wayne were the simple constructs that largely transformed a diminutive comedian convincingly into a president.
Doubletalk also has a more “educated” sibling known as technobabble. Engineers are sure to recall a famous (and still very humorous) example of both doubletalk and technobabble in the famed description of the Turboencabulator. (See also, the short videos below.)
Doubletalk comedy examples
Al Kelly on Ernie Kovaks
Rockwell Turbo Encabulator Version 2
And of course doubletalk and technobabble have closely related cousins named doublespeak and politicobabble. These are far more dangerous than the others because they move over the line of comedy into seriousness and are used by people who make decisions effecting hundreds of thousands to millions, if not billions, of people on the planet. I’m sure an archeo-linguist might be able to discern where exactly politicobabble emerged and managed to evolve into a non-comedic form of speech which people manage to take far more seriously than its close ancestors. One surely suspects some heavy influence from George Orwell’s corpus of work:
While politicobabble is nothing new, I did find a very elucidating passage from the 1992 U.S. Presidential Election cycle which seems to be a major part of the Trump campaign playbook:
In the continuation of the article, Jacobs goes on to give a variety of examples of the term as well as a “translation” guide for some of the common politicobabble words from that particular election. I’ll leave it to the capable hands of others (perhaps in the comments, below?) to come up with the translation guide for our current political climate.
The interesting evolutionary change I’ll note for the current election cycle is that Trump hasn’t delved into any depth on any of his themes to offend anyone significantly enough. This has allowed him to stay with the dozen or so themes he started out using and therefore hasn’t needed to change them as in campaigns of old.
Filling in the Blanks
These forms of pseudo-speech area all meant to fool us into thinking that something of substance is being discussed and that a conversation is happening, when in fact, nothing is really being communicated at all. Most of the intended meaning and reaction to such speech seems to stem from the demeanor of the speaker as well as, in some part, to the reaction of the surrounding interlocutor and audience. In reading Donald Trump transcripts, an entirely different meaning (or lack thereof) is more quickly realized as the surrounding elements which prop up the narrative have been completely stripped away. In a transcript version, gone is the hypnotizing element of the crowd which is vehemently sure that the emperor is truly wearing clothes.
In many of these transcripts, in fact, I find so little is being said that the listener is actually being forced to piece together the larger story in their head. Being forced to fill in the blanks in this way leaves too much of the communication up to the listener who isn’t necessarily engaged at a high level. Without more detail or context to understand what is being communicated, the listener is far more likely to fill in the blanks to fit a story that doesn’t create any cognitive dissonance for themselves — in part because Trump is usually smiling and welcoming towards his adoring audiences.
One will surely recall that Trump even wanted Secretary Clinton to be happy during the debate when he said, “Now, in all fairness to Secretary Clinton — yes, is that OK? Good. I want you to be very happy. It’s very important to me.” (This question also doubles as an example of a standard psychological sales tactic of attempting to get the purchaser to start by saying ‘yes’ as a means to keep them saying yes while moving them towards making a purchase.)
His method of communicating by leaving large holes in his meaning reminds me of the way our brain smooths out information as indicated in this old internet meme :
I cdn’uolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg: the phaonmneel pweor of the hmuan mnid. Aoccdrnig to a rseearch taem at Cmabrigde Uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The rset can be a taotl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Scuh a cdonition is arpppoiatrely cllaed typoglycemia.
I’m also reminded of the biases and heuristics research carried out in part (and the remainder cited) by Daniel Kahneman in his book Thinking, Fast and Slow  in which he discusses the mechanics of how system 1 and system 2 work in our brains. Is Trump taking advantage of the deficits of language processing in our brains in something akin to system 1 biases to win large blocks of votes? Is he creating a virtual real-time Choose-Your-Own-Adventure to subvert the laziness of the electorate? Kahneman would suggest the the combination of what Trump does say and what he doesn’t leaves it up to every individual listener to create their own story. Their system 1 is going to default to the easiest and most palatable one available to them: a happy story that fits their own worldview and is likely to encourage them to support Trump.
Ten Word Answers
As an information theorist, I know all too well that there must be a ‘linguistic Shannon limit’ to the amount of semantic meaning one can compress into a single word.  One is ultimately forced to attempt to form sentences to convey more meaning. But usually the less politicians say, the less trouble they can get into — a lesson hard won through generations of political fighting.
I’m reminded of a scene from The West Wing television series. In season 4, episode 6 which aired on October 30, 2002 on NBC, Game On had a poignant moment (video clip below) which is germane to our subject: 
Moderator: Governor Ritchie, many economists have stated that the tax cut, which is the centrepiece of your economic agenda, could actually harm the economy. Is now really the time to cut taxes?
Governor Ritchie, R-FL: You bet it is. We need to cut taxes for one reason – the American people know how to spend their money better than the federal government does.
Moderator: Mr. President, your rebuttal.
President Bartlet: There it is…
That’s the 10 word answer my staff’s been looking for for 2 weeks. There it is.
10 word answers can kill you in political campaigns — they’re the tip of the sword.
Here’s my question: What are the next 10 words of your answer?
“Your taxes are too high?” So are mine…
Give me the next 10 words: How are we going to do it?
Give me 10 after that — I’ll drop out of the race right now.
Every once in a while — every once in a while, there’s a day with an absolute right and an absolute wrong, but those days almost always include body counts. Other than that there aren’t very many un-nuanced moments in leading a country that’s way too big for 10 words.
I’m the President of the United States, not the president of the people who agree with me. And by the way, if the left has a problem with that, they should vote for somebody else.
As someone who studies information theory and complexity theory and even delves into sub-topics like complexity and economics, I can agree wholeheartedly with the sentiment. Though again, here I can also see the massive gaps between system 1 and 2 that force us to want to simplify things down to such a base level that we don’t have to do the work to puzzle them out.
(And yes, that is Jennifer Anniston’s father playing the moderator.)
One can’t but wonder why Mr. Trump doesn’t seem to have ever gone past the first ten words? Is it because he isn’t capable? interested? Or does he instinctively know better? It would seem that he’s been doing business by using the uncertainty inherent in his speech for decades, but always operating by using what he meant (or thought he wanted to mean) than what the other party heard and thought they understood. If it ain’t broke, don’t fix it.
Idiocracy or Something Worse?
In our increasingly specialized world, people eventually have to give in and quit doing some tasks that everyone used to do for themselves. Yesterday I saw a lifeworn woman in her 70s pushing a wheeled wire basket with a 5 gallon container of water from the store to her home. As she shuffled along, I contemplated Thracian people from fourth century BCE doing the same thing except they likely carried amphorae possibly with a yoke and without the benefit of the $10 manufactured custom shopping cart. 20,000 years before that people were still carrying their own water, but possibly without even the benefit of earthenware containers. Things in human history have changed very slowly for the most part, but as we continually sub-specialize further and further, we need to remember that we can’t give up one of the primary functions that makes us human: the ability to think deeply and analytically for ourselves.
I suspect that far too many people are too wrapped up in their own lives and problems to listen to more than the ten word answers our politicians are advertising to us. We need to remember to ask for the next ten words and the ten after that.
Otherwise there are two extreme possible outcomes:
We’re either at the beginning of what Mike Judge would term Idiocracy. 
Or we’re headed to what Michiko Kakutani is “subtweeting” about in her recent review In ‘Hitler’ an Ascent from ‘Dunderhead’ to Demagogue  of Volker Ulrich’s new book Hitler: Ascent 1889-1939. 
— Mark Harris (@MarkHarrisNYC) September 28, 2016
Here, one is tempted to quote George Santayana’s famous line (from The Life of Reason, 1905), “Those who cannot remember the past are condemned to repeat it.” However, I far prefer the following as more apropos to our present national situation:
If Cliff Navarro comes back to run for president, I hope no one falls for his joke just because he wasn’t laughing as he acted it out. If his instructions for fixing the wagon (America) are any indication, the voters who are listening and making the repairs will be in severe pain.
If you’re not already doing so, you should follow Barabási on Twitter.
— Laszlo Barabasi (@barabasi) August 3, 2016
During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.
Complex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this book I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as self-organizing. Starting from the agent metaphor, the methodology proposes a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by actively interacting among themselves.
One thing I will mention is that it’s got quite a bit more philosophy in it than most popular science books with such a physics bent. Those who aren’t already up to speed on the math and science of modern physics can certainly benefit from the book (like most popular science books of its stripe, it doesn’t have any equations — hairy or otherwise), and it’s certain to help many toward becoming members of both of C.P. Snow’s two cultures. It might not be the best place for mathematicians and physicists to start moving toward the humanities with the included philosophy as the philosophy is very light and spotty in places and the explanations of the portions they’re already aware of may put them out a bit.
I’m most interested to see how he views complexity and thinking in the final portion of the text.
More detail to come…
The Theory of Everything and Then Some: In complexity theory, physicists try to understand economics while sociologists think like biologists. Can they bring us any closer to universal knowledge?
A discussion of complexity and complexity theorist John H. Miller’s new book: A Crude Look at the Whole: The Science of Complex Systems in Business, Life, and Society.
I’m interested in some of the information theoretic aspects of this as well as the relation of this to the area of corpus linguistics. I’m also curious if one could build worthwhile datasets like this for the ancient world (cross reference some of the sources I touch on in relation to the Dickinson College Commentaries within Latin Pedagogy and the Digital Humanities) to see what influences different language cultures have had on each other. Perhaps the historical record could help to validate some of the predictions made in relation to the future?
The paper “Global distribution and drivers of language extinction risk” indicates that of all the variables tested, economic growth was most strongly linked to language loss.
This research also has some interesting relation to the concept of “Collective Learning” within the realm of a Big History framework via David Christian, Fred Spier, et al. I’m curious to revisit my hypothesis: Collective learning has potentially been growing at the expense of a shrinking body of diverse language some of which was informed by the work of Jared Diamond.
Some of the discussion in the video is reminiscent to me of some of the work Stuart Kauffman lays out in At Home in the Universe: The Search for the Laws of Self-Organization and Complexity (Oxford, 1995). Particularly in chapter 3 in which Kauffman discusses the networks of life. The analogy of this to the networks of language here indicate to me that some of Cesar Hidalgo’s recent work in Why Information Grows: The Evolution of Order, From Atoms to Economies (MIT Press, 2015) is even more interesting in helping to show the true value of links between people and firms (information sources which he measures as personbytes and firmbytes) within economies.
Finally, I can also only think about how this research may help to temper some of the xenophobic discussion that occurs in American political life with respect to fears relating to Mexican immigration issues as well as the position of China in the world economy.
Those intrigued by the video may find the website set up by the researchers very interesting. It contains links to the full paper as well as visualizations and links to the data used.
Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language’s centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce.
Citation: Ronen S, Goncalves B, Hu KZ, Vespignani A, Pinker S, Hidalgo CA
Links that speak: the global language network and its association with global fame, Proceedings of the National Academy of Sciences (PNAS) (2014), 10.1073/pnas.1410931111
- Why learning Spanish, not Mandarin, is the best way to globalize your ideas | Quartz
- Want to influence the world? Map reveals the best languages to speak | Science
“A language like Dutch — spoken by 27 million people — can be a disproportionately large conduit, compared with a language like Arabic, which has a whopping 530 million native and second-language speakers,” Science reports. “This is because the Dutch are very multilingual and very online.”
Information is a precise concept that can be defined mathematically, but its relationship to what we call "knowledge" is not always made clear. Furthermore, the concepts "entropy" and "information", while deeply related, are distinct and must be used with care, something that is not always achieved in the literature. In this elementary introduction, the concepts of entropy and information are laid out one by one, explained intuitively, but defined rigorously. I argue that a proper understanding of information in terms of prediction is key to a number of disciplines beyond engineering, such as physics and biology.
Comments: 19 pages, 2 figures. To appear in Philosophical Transaction of the Royal Society A
Subjects: Adaptation and Self-Organizing Systems (nlin.AO); Information Theory (cs.IT); Biological Physics (physics.bio-ph); Quantitative Methods (q-bio.QM)
Cite as:arXiv:1601.06176 [nlin.AO] (or arXiv:1601.06176v1 [nlin.AO] for this version)
These two historical references predate Claude Shannon’s mathematical formalization of information in A Mathematical Theory of Communication (The Bell System Technical Journal, 1948) and even Erwin Schrödinger‘s lecture (1943) and subsequent book What is Life (1944).
For those interested in reading more on this historical tidbit, I’ve dug up a copy of the primary Forsdyke reference which first appeared on arXiv (prior to its ultimate publication in History of Psychiatry [.pdf]):
🔖 [1406.1391] ‘A Vehicle of Symbols and Nothing More.’ George Romanes, Theory of Mind, Information, and Samuel Butler by Donald R. Forsdyke 
Submitted on 4 Jun 2014 (v1), last revised 13 Nov 2014 (this version, v2)
Abstract: Today’s ‘theory of mind’ (ToM) concept is rooted in the distinction of nineteenth century philosopher William Clifford between ‘objects’ that can be directly perceived, and ‘ejects,’ such as the mind of another person, which are inferred from one’s subjective knowledge of one’s own mind. A founder, with Charles Darwin, of the discipline of comparative psychology, George Romanes considered the minds of animals as ejects, an idea that could be generalized to ‘society as eject’ and, ultimately, ‘the world as an eject’ – mind in the universe. Yet, Romanes and Clifford only vaguely connected mind with the abstraction we call ‘information,’ which needs ‘a vehicle of symbols’ – a material transporting medium. However, Samuel Butler was able to address, in informational terms depleted of theological trappings, both organic evolution and mind in the universe. This view harmonizes with insights arising from modern DNA research, the relative immortality of ‘selfish’ genes, and some startling recent developments in brain research.
Comments: Accepted for publication in History of Psychiatry. 31 pages including 3 footnotes. Based on a lecture given at Santa Clara University, February 28th 2014, at a Bannan Institute Symposium on ‘Science and Seeking: Rethinking the God Question in the Lab, Cosmos, and Classroom.’
The original arXiv article also referenced two lectures which are appended below:
[Original Draft of this was written on December 14, 2015.]