📑 #LoveBombs for Thimble: Saying Goodbye to Teacher, Mentor, Friend| INTERTEXTrEVOLUTION

Replied to #LoveBombs for Thimble: Saying Goodbye to Teacher, Mentor, Friend by J. Gregroy McVerry (jgmac1106homepage.glitch.me)
Pointing someone to a README.md does not lead to learning.  

This reminds me of an interesting study from MIT relating to collective learning that I heard about from Cesar Hidalgo recently.

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🔖 The influence of collaboration networks on programming language acquisition by Sanjay Guruprasad | MIT

Bookmarked The influence of collaboration networks on programming language acquisition by Sanjay Guruprasad (Massachusetts Institute of Technology)

Many behaviors spread through social contact. However, different behaviors seem to require different degrees of social reinforcement to spread within a network. Some behaviors spread via simple contagion, where a single contact with an "activated node" is sufficient for transmission, while others require complex contagion, with reinforcement from multiple nodes to adopt the behavior. But why do some behaviors require more social reinforcement to spread than others? Here we hypothesize that learning more difficult behaviors requires more social reinforcement. We test this hypothesis by analyzing the programming language adoption of hundreds of thousands of programmers on the social coding platform Github. We show that adopting more difficult programming languages requires more reinforcement from the collaboration network. This research sheds light on the role of collaboration networks in programming language acquisition.

[Downloadable .pdf]

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 26-28).

Advisor: César Hidalgo.

URI: http://hdl.handle.net/1721.1/119085

I ran across this paper via the Human Current interview with Cesar Hidalgo. In general they studied GitHub as a learning community and the social support of people’s friends on the platform as they worked on learning new programming languages.

I think there might be some interesting takeaways for people looking at collective learning and online pedagogies as well as for communities like the IndieWeb which are trying to not only build new technologies, but help to get them into others’ hands by teaching and disseminating some generally tough technical knowledge. (In this respect, the referenced Human Current podcast episode may be a worthwhile overview.)

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🎧 Episode 085 How Networks Learn An Interview with Cesar Hidalgo | Human Current

Listened to Episode 085 How Networks Learn An Interview with Cesar Hidalgo by Haley Campbell-GrossHaley Campbell-Gross from HumanCurrent

In this episode, Haley talks with physicist, complexity scientist, and MIT professor, Cesar Hidalgo. Hidalgo discusses his interest in the physics of networks and complex system science and shares why he believes these fields are so important. He talks about his book, Why Information Grows: The Evolution of Order, from Atoms to Economies, which takes a scientific look at global economic complexity. Hidalgo also shares how economic development is linked to making networks more knowledgeable.

Cesar Hidalgo

Quotes from this episode:

“Thinking about complexity is important because people have a tendency to jump into micro explanations for macro phenomenon.” — Cesar Hidalgo

“I think complex systems give you not only some practical tools to think about the world, but also some sort of humbleness because you have to understand that your knowledge and understanding of how the systems work is always very limited and that humbleness gives you a different attitude and perspective and gives you some peace.” — Cesar Hidalgo

“The way that we think about entropy in physics and information theory come from different traditions and sometimes that causes a little bit of confusion, but at the end of the day it’s the number of different ways in which you can arrange something.” — Cesar Hidalgo

“To learn more complex activities you need more social reinforcement.” — Cesar Hidalgo

“When we lead groups we have to be clear about the goals and the main goal to keep in mind is that of learning.” — Cesar Hidalgo

“Everybody fails, but not everyone learns from their failures.” — Cesar Hidalgo

“Learning is not just something that is interesting to study, it is actually a goal.” — Cesar Hidalgo

A solid interview here with Cesar Hidalgo. His book has been incredibly influential on my thoughts for the past two years, so I obviously highly recommend it. He’s got a great description of entropy here. I was most surprised by his conversation about loneliness, but I have a gut feeling that’s he’s really caught onto something with his thesis.

I also appreciated about some of how he expanded on learning in the last portion of the interview. Definitely worth revisiting.

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🔖 The building blocks of economic complexity | César A. Hidalgo and Ricardo Hausmann| PNAS

Bookmarked The building blocks of economic complexity by César A. Hidalgo and Ricardo Hausmann (PNAS)
For Adam Smith, wealth was related to the division of labor. As people and firms specialize in different activities, economic efficiency increases, suggesting that development is associated with an increase in the number of individual activities and with the complexity that emerges from the interactions between them. Here we develop a view of economic growth and development that gives a central role to the complexity of a country's economy by interpreting trade data as a bipartite network in which countries are connected to the products they export, and show that it is possible to quantify the complexity of a country's economy by characterizing the structure of this network. Furthermore, we show that the measures of complexity we derive are correlated with a country's level of income, and that deviations from this relationship are predictive of future growth. This suggests that countries tend to converge to the level of income dictated by the complexity of their productive structures, indicating that development efforts should focus on generating the conditions that would allow complexity to emerge to generate sustained growth and prosperity.

h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

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🔖 A Dynamic Network Approach for the Study of Human Phenotypes | PLOS Computational Biology

Bookmarked A Dynamic Network Approach for the Study of Human Phenotypes by César A. Hidalgo , Nicholas Blumm, Albert-László Barabási, Nicholas A. Christakis (PLOS Computational Biology)
Author Summary: To help the understanding of physiological failures, diseases are defined as specific sets of phenotypes affecting one or several physiological systems. Yet, the complexity of biological systems implies that our working definitions of diseases are careful discretizations of a complex phenotypic space. To reconcile the discrete nature of diseases with the complexity of biological organisms, we need to understand how diseases are connected, as connections between these different discrete categories can be informative about the mechanisms causing physiological failures. Here we introduce the Phenotypic Disease Network (PDN) as a map summarizing phenotypic connections between diseases and show that diseases progress preferentially along the links of this map. Furthermore, we show that this progression is different for patients with different genders and racial backgrounds and that patients affected by diseases that are connected to many other diseases in the PDN tend to die sooner than those affected by less connected diseases. Additionally, we have created a queryable online database (http://hudine.neu.edu/) of the 18 different datasets generated from the more than 31 million patients in this study. The disease associations can be explored online or downloaded in bulk.

h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

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👓 Disconnected, fragmented, or united? a trans-disciplinary review of network science | Applied Network Science | César A. Hidalgo

Read Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. HidalgoCésar A. Hidalgo (Applied Network Science | SpringerLink)
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.

Highlights, Quotes, Annotations, & Marginalia

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.  

August 25, 2018 at 10:18PM

Science and Complexity (Weaver 1948); explained the three eras that according to him defined the history of science. These were the era of simplicity, disorganized complexity, and organized complexity. In the eyes of Weaver what separated these three eras was the development of mathematical tools allowing scholars to describe systems of increasing complexity.  

August 25, 2018 at 10:19PM

Problems of disorganized complexity are problems that can be described using averages and distributions, and that do not depend on the identity of the elements involved in a system, or their precise patterns of interactions. A classic example of a problem of disorganized complexity is the statistical mechanics of Ludwig Boltzmann, James-Clerk Maxwell, and Willard Gibbs, which focuses on the properties of gases.  

August 25, 2018 at 10:20PM

Soon after Weaver’s paper, biologists like Francois Jacob (Jacob and Monod 1961), (Jacob et al. 1963) and Stuart Kaufmann (Kauffman 1969), developed the idea of regulatory networks. Mathematicians like Paul Erdos and Alfred Renyi, advanced graph theory (Erdős and Rényi 1960) while Benoit Mandelbrot worked on Fractals (Mandelbrot and Van Ness 1968), (Mandelbrot 1982). Economists like Thomas Schelling (Schelling 1960) and Wasily Leontief (Leontief 1936), (Leontief 1936), respectively explored self-organization and input-output networks. Sociologists, like Harrison White (Lorrain and White 1971) and Mark Granovetter (Granovetter 1985), explored social networks, while psychologists like Stanley Milgram (Travers and Milgram 1969) explored the now famous small world problem.   

Some excellent references
August 25, 2018 at 10:24PM

First, I will focus in these larger groups because reviews that transcend the boundary between the social and natural sciences are rare, but I believe them to be valuable. One such review is Borgatti et al. (2009), which compares the network science of natural and social sciences arriving at a similar conclusion to the one I arrived.  

August 25, 2018 at 10:27PM

Links are the essence of networks. So I will start this review by comparing the mechanisms used by natural and social scientists to explain link formation.  

August 25, 2018 at 10:32PM

When connecting the people that acted in the same movie, natural scientists do not differentiate between people in leading or supporting roles.  

But they should because it’s not often the case that these are relevant unless they are represented by the same agent or agency.
August 25, 2018 at 10:51PM

For instance, in the study of mobile phone networks, the frequency and length of interactions has often been used as measures of link weight (Onnela et al. 2007), (Hidalgo and Rodriguez-Sickert 1008), (Miritello et al. 2011).  

And they probably shouldn’t because typically different levels of people are making these decisions. Studio brass and producers typically have more to say about the lead roles and don’t care as much about the smaller ones which are overseen by casting directors or sometimes the producers. The only person who has oversight of all of them is the director, and even then they may quit caring at some point.
August 25, 2018 at 10:52PM

Social scientists explain link formation through two families of mechanisms; one that finds it roots in sociology and the other one in economics. The sociological approach assumes that link formation is connected to the characteristics of individuals and their context. Chief examples of the sociological approach include what I will call the big three sociological link-formation hypotheses. These are: shared social foci, triadic closure, and homophily.  

August 25, 2018 at 10:55PM

The social foci hypothesis predicts that links are more likely to form among individuals who, for example, are classmates, co-workers, or go to the same gym (they share a social foci). The triadic closure hypothesis predicts that links are more likely to form among individuals that share “friends” or acquaintances. Finally, the homophily hypothesis predicts that links are more likely to form among individuals who share social characteristics, such as tastes, cultural background, or physical appearance (Lazarsfeld and Merton 1954), (McPherson et al. 2001).  

definitions of social foci, triadic closure, and homophily within network science.
August 26, 2018 at 11:39AM

Yet, strategic games look for equilibrium in the formation and dissolution of ties in the context of the game theory advanced first by (Von Neumann et al. 2007), and later by (Nash 1950).  

August 25, 2018 at 10:58PM

Preferential attachment is the idea that connectivity begets connectivity.  

August 25, 2018 at 10:59PM

Preferential attachment is an idea advanced originally by the statisticians John Willis and Udny Yule in (Willis and Yule 1922), but has been rediscovered numerous times during the twentieth century.  

August 25, 2018 at 11:00PM

Rediscoveries of this idea in the twentieth century include the work of (Simon 1955) (who did cite Yule), (Merton 1968), (Price 1976) (who studied citation networks), and (Barabási and Albert 1999), who published the modern reference for this model, which is now widely known as the Barabasi-Albert model.  

August 25, 2018 at 11:01PM

preferential attachment. In the eyes of the social sciences, however, understanding which of all of these hypotheses drives the formation of the network is what one needs to explore.  

For example what drives attachment of political candidates?
August 26, 2018 at 08:15AM

Finally it is worth noting that trust, through the theory of social capital, has been connected with long-term economic growth—even though these results are based on regressions using extremely sparse datasets.  

And this is an example of how Trump is hurting the economy.
August 26, 2018 at 08:33AM

Nevertheless, the evidence suggests that social capital and social institutions are significant predictors of economic growth, after controlling for the effects of human capital and initial levels of income (Knack and Keefer 1997), (Knack 2002).4 So trust is a relevant dimension of social interactions that has been connected to individual dyads, network formation, labor markets, and even economic growth.  

August 26, 2018 at 08:35AM

Social scientist, on the other hand, have focused on what ties are more likely to bring in new information, which are primarily weak ties (Granovetter 1973), and on why weak ties bring new information (because they bridge structural holes (Burt 2001), (Burt 2005)).  

August 26, 2018 at 09:45AM

heterogeneous networks have been found to be effective promoters of the evolution of cooperation, since there are advantages to being a cooperator when you are a hub, and hubs tend to stabilize networks in equilibriums where levels of cooperation are high (Ohtsuki et al. 2006), (Pacheco et al. 2006), (Lieberman et al. 2005), (Santos and Pacheco 2005).  

August 26, 2018 at 09:49AM

These results, however, have also been challenged by human experiments finding no such effect (Gracia-Lázaro et al. 2012). The study of cooperation in networks has also been performed in dynamic settings, where individuals are allowed to cut ties (Wang et al. 2012), promoting cooperation, and are faced with different levels of knowledge about the reputation of peers in their network (Gallo and Yan 2015). Moreover, cooperating behavior has seen to spread when people change the networks where they participate in (Fowler and Christakis 2010).  

Open questions
August 26, 2018 at 09:50AM

References

1.
Hidalgo CA. Disconnected, fragmented, or united? a trans-disciplinary review of network science. ANS. 2016;1(1). doi:10.1007/s41109-016-0010-3
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📑 Community, Privatization, Efficiency | Kathleen Fitzpatrick

Annotated Community, Privatization, Efficiency by Kathleen FitzpatrickKathleen Fitzpatrick (Kathleen Fitzpatrick)
Reversing the trend toward privatization will thus require not just massive public mobilization and demand of elected officials, but also a hard turn away from efficiency as a primary value, a recognition that the building of relationships and the cultivation of care is slow and difficult and of necessity inefficient. In fact, that its value lies in its inefficiency — but making the case for such inefficiency as a necessary value requires a lot of effort, and a lot of caution.

There’s a kernel here of something about the value of links (social, business, etc.) as put forward by Cesar Hidalgo in Why Information Grows. Where is the real value? How can it best be extracted? Built up? Having a more direct means of valuing these otherwise seeming intangibles will be important in the future.

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🎧 Episode 06 My Little Hundred Million | Revisionist History

Listened to Episode 06 My Little Hundred Million by Malcolm GladwellMalcolm Gladwell from Revisionist History


In the early ’90s, Hank Rowan gave $100 million to a university in New Jersey, an act of extraordinary generosity that helped launch the greatest explosion in educational philanthropy since the days of Andrew Carnegie and the Rockefellers. But Rowan gave his money to Glassboro State University, a tiny, almost bankrupt school in South Jersey, while almost all of the philanthropists who followed his lead made their donations to elite schools such as Harvard and Yale. Why did no one follow Rowan’s example?

“My Little Hundred Million” is the third part of Revisionist History’s educational miniseries. It looks at the hidden ideologies behind giving and how a strange set of ideas has hijacked educational philanthropy.

The key idea laid out stunningly here is strong links versus weak links.

I’m generally flabbergasted by the general idea proposed here and will have to do some more research in the near future to play around further with the ideas presented. Fortunately, in addition to the education specific idea presented, Gladwell also comes up with an additional few examples in sports by using the differences between soccer and basketball to show the subtle differences.

If he and his lab aren’t aware of the general concept, I would recommend this particular podcast and the concept of strong and weak links to César Hidalgo (t) who might actually have some troves of economics data to use to play around with some general modeling to expand upon these ideas. I’ve been generally enamored of Hidalgo’s general thesis about the overall value of links as expressed in Why Information Grows: The Evolution of Order, from Atoms to Economies1. I often think of it with relation to political economies and how the current administration seems to be (often quietly) destroying large amounts of value by breaking down a variety of economic, social, and political links within the United States as well as between our country and others.

I wonder if the additional ideas about the differences between strong and weak links might further improve these broader ideas. The general ideas behind statistical mechanics and statistics make me think that Gladwell, like Hidalgo, is certainly onto a strong idea which can be continued to be refined to improve billions of lives. I’ll have to start some literature searches now…

References

1.
Hidalgo C. Why Information Grows: The Evolution of Order, from Atoms to Economies. New York: Basic Books; 2015.
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👓 Encouraging individual sovereignty and a healthy commons by Aral Balkan

Read Encouraging individual sovereignty and a healthy commons by Aral Balkan (ar.al)
Mark Zuckerberg’s manifesto outlines his vision for a centralised global colony ruled by the Silicon Valley oligarchy. I say we must do the exact opposite and create a world with individual sovereignty and a healthy commons.

The verbiage here is a bit inflammatory and very radical sounding, but the overarching thesis is fairly sound. The people who are slowly, but surely building the IndieWeb give me a lot of hope that the unintended (by the people anyway) consequences that are unfolding can be relatively quickly remedied.

Marginalia

We are sharded beings; the sum total of our various aspects as contained within our biological beings as well as the myriad of technologies that we use to extend our biological abilities.

To some extent, this thesis could extend Cesar Hidalgo’s concept of the personbyte as in putting part of one’s self out onto the internet, one can, in some sense, contain more information than previously required.

Richard Dawkin’s concept of meme extends the idea a bit further in that an individual’s thoughts can infect others and spread with a variable contagion rate dependent on various variables.

I would suspect that though this does extend the idea of personbyte, there is still some limit to how large the size of a particular person’s sphere could expand.


While technological implants are certainly feasible, possible, and demonstrable, the main way in which we extend ourselves with technology today is not through implants but explants.


in a tiny number of hands.

or in a number of tiny hands, as the case can sometimes be.


The reason we find ourselves in this mess with ubiquitous surveillance, filter bubbles, and fake news (propaganda) is precisely due to the utter and complete destruction of the public sphere by an oligopoly of private infrastructure that poses as public space.

This is a whole new tragedy of the commons: people don’t know where the commons actually are anymore.

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🔖 Linking Economic Complexity, Institutions and Income Inequality

Bookmarked Linking Economic Complexity, Institutions and Income Inequality by Dominik Hartmann, Miguel R. Guevara, Cristian Jara-Figueroa, Manuel Aristarán, César A. Hidalgo (arxiv.org)
A country's mix of products predicts its subsequent pattern of diversification and economic growth. But does this product mix also predict income inequality? Here we combine methods from econometrics, network science, and economic complexity to show that countries exporting complex products (as measured by the Economic Complexity Index) have lower levels of income inequality than countries exporting simpler products. Using multivariate regression analysis, we show that economic complexity is a significant and negative predictor of income inequality and that this relationship is robust to controlling for aggregate measures of income, institutions, export concentration, and human capital. Moreover, we introduce a measure that associates a product to a level of income inequality equal to the average GINI of the countries exporting that product (weighted by the share the product represents in that country's export basket). We use this measure together with the network of related products (or product space) to illustrate how the development of new products is associated with changes in income inequality. These findings show that economic complexity captures information about an economy's level of development that is relevant to the ways an economy generates and distributes its income. Moreover, these findings suggest that a country's productive structure may limit its range of income inequality. Finally, we make our results available through an online resource that allows for its users to visualize the structural transformation of over 150 countries and their associated changes in income inequality between 1963 and 2008.

MIT has a pretty good lay-person’s overview of this article. The final published version is separately available.

 

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Income inequality linked to export “complexity” | MIT News

Read Income inequality linked to export “complexity” (MIT News)
The mix of products that countries export is a good predictor of income distribution, study finds.

Continue reading “Income inequality linked to export “complexity” | MIT News”

📖 5.27% done with American Amnesia by Jacob S. Hacker and Paul Pierson

📖 Read loc 1-682 of 12932 (5.27%) of American Amnesia by Jacob S. Hacker and Paul Pierson

This portends to be very interesting in that they plan to show what has changed over much of the 1900’s to indicate the drastic evolution in American politics, life, and philosophy over the recent decades. In light of the political battles between the left and the right over the past several years, this could provide some much needed help and guidance.

Their basic thesis seems to be that a shift away from a mixed economy has slowed American growth and general prosperity. While they do seem to have a pointed (political) view, so far it’s incredibly well documented and footnoted for those who would like to make the counter-argument. They’ve definitely got some serious evidence to indicate how drastic the situation is, but I’m curious if they can directly tie their proposed cause to the effect. If nothing else, they’ve created a laundry list of problems in America which need to be addressed by some serious leadership soon.

In some sense I’m torn about what to think of a broader issue this touches upon and which I mentioned briefly while reading At Home in the Universe. Should we continue on the general path we’ve struck out upon (the mixed economy with government regulation/oversight), or should we continue evolving away? While we can’t see the complexity effects seven levels further in, they may be more valuable than what we’ve got now. For example Cesar Hidalgo looks at the evolution along a continuum of personbyte to larger groups: firms (firmbyte), governments, and mega-corporations in Why Information Grows, so I can easily see larger governments and corporations like Google drastically changing the world in which we live (operating at a level above what most humans can imagine presently), but the complexity of why and how they operate above (and potentially against) the good of the individual should certainly be called into question and considered as we move forward.

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Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science

Bookmarked Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science by César A. HidalgoCésar A. Hidalgo (Applied Network Science | SpringerLink)

Applied Network Science

Abstract

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.

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Global Language Networks

Recent research on global language networks has interesting relations to big history, complexity economics, and current politics.

Yesterday I ran across this nice little video explaining some recent research on global language networks. It’s not only interesting in its own right, but is a fantastic example of science communication as well.

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.

Abstract

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

Related posts:

“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.”

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César Hidalgo on Why Information Grows | The RSA

I’ve just recently finished the excellent book Why Information Grows by César Hidalgo. I hope to post a reasonable review soon, but the ideas in it are truly excellent and fit into a thesis I’ve been working on for a while. For those interested, he does a reasonable synopsis of some of his thought in the talk he gave the the RSA recently, the video can be found below.

The underlying mathematics of what he’s discussing are fantastic (though he doesn’t go into them in his book), but the overarching implications of his ideas with relation to the future of humankind as a function of our economic system and society could have some significant impact.

“César visits the RSA to present a new view of the relationship between individual and collective knowledge, linking information theory, economics and biology to explain the deep evolution of social and economic systems.

In a radical rethink of what an economy is, one of WIRED magazine’s 50 People Who Could Change the World, César Hidalgo argues that it is the measure of a nation’s cultural complexity – the nexus of people, ideas and invention – rather than its GDP or per-capita income, that explains the success or failure of its economic performance. To understand the growth of economies, Hidalgo argues, we first need to understand the growth of order itself.”

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