Cesar Hidalgo | Chris Aldrich

Listened to Mindscape 72 | César Hidalgo on Information in Societies, Economies, and the Universe by

Sean Carroll from preposterousuniverse.com

Maxwell’s Demon is a famous thought experiment in which a mischievous imp uses knowledge of the velocities of gas molecules in a box to decrease the entropy of the gas, which could then be used to do useful work such as pushing a piston. This is a classic example of converting information (what the gas molecules are doing) into work. But of course that kind of phenomenon is much more widespread — it happens any time a company or organization hires someone in order to take advantage of their know-how. César Hidalgo has become an expert in this relationship between information and work, both at the level of physics and how it bubbles up into economies and societies. Looking at the world through the lens of information brings new insights into how we learn things, how economies are structured, and how novel uses of data will transform how we live.

César Hidalgo received his Ph.D. in physics from the University of Notre Dame. He currently holds an ANITI Chair at the University of Toulouse, an Honorary Professorship at the University of Manchester, and a Visiting Professorship at Harvard’s School of Engineering and Applied Sciences. From 2010 to 2019, he led MIT’s Collective Learning group. He is the author of Why Information Grows and co-author of The Atlas of Economic Complexity. He is a co-founder of Datawheel, a data visualization company whose products include the Observatory of Economic Complexity.

It was interesting to hear Cesar Hidalgo use the concept of “big history” a few times in this episode. I’m not 100% sure he meant it in the David Christian sense of the words, but it at least felt right.

I was also piqued at the mention of Lynne Kelly’s work, which I’m now knee deep into. I suspect it could dramatically expand on what we think of as the capacity of a personbyte, though the limit of knowledge there still exists. The idea of mnemotechniques within indigenous cultures certainly expands on the way knowledge worked in prehistory and what we classically think of and frame collective knowledge or collective learning.

I also think there are some interesting connections with Dr. Kelly’s mentions of social equity in prehistorical cultures and the work that Hidalgo mentions in the middle of the episode.

There are a small handful of references I’ll want to delve into after hearing this, though it may take time to pull them up unless they’re linked in the show notes.

hat-tip: Complexity Digest for the reminder that this is in my podcatcher. 🔖 November 22, 2019 at 03:28PM

Watched How the medium shapes the message by Cesar Hidalgo from TEDxYouth@BeaconStreet | YouTube

How communication technologies shape our collective memory.

César A. Hidalgo is an assistant professor at the MIT Media Lab. Hidalgo’s work focuses on improving the understanding of systems by using and developing concepts of complexity, evolution, and network science; his goal is to help improve understanding of the evolution of prosperity in order to help develop industrial policies that can help countries raise the living standards of their citizens. His areas of application include economic development, systems biology, and social systems. Hidalgo is also a graphic-art enthusiast and has published and exhibited artwork that uses data collected originally for scientific purposes.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

👓 The Rise of Knowledge Economics | Scientific American

Read The Rise of Knowledge Economics by

César A. Hidalgo (Scientific American Blog Network)

What is knowledge? How does it disseminate? And what’s its value?

A great article outlining several related papers in Dr. Hidalgo’s opus. I like how he pulls together prior research as well as his own in an accessible way.

🔖 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.)

🎧 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-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.

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.

🔖 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)

👓 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. 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

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

🎧 Episode 06 My Little Hundred Million | Revisionist History

Listened to Episode 06 My Little Hundred Million by

Malcolm 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

Hidalgo C. Why Information Grows: The Evolution of Order, from Atoms to Economies. New York: Basic Books; 2015.

👓 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.

🔖 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.

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.