OREL Medium: Trajectories in Cognitive Science Session @ CogSci 2021

 

This content was originally posted to the Orthogonal Research and Education Lab blog on July 30.

Congratulations to Jesse Parent, Avery Lim, Bradly Alicea, and Anusha Sharma for heading up the “Trajectories in Cognitive Science” discussion group, held during the CogSci 2021  conference. The event happened in six parts (which we will recap) and is now on YouTube. We are also archiving the slides and reference list on the Open Science Framework (in progress).

Part I: Frontier Map and Cognitive Futures. Presenter: Jesse Parent.

The first part of the session involved an overview of Frontier Maps and the role of maps and visualizations in understanding how ideas form fields of study. Frontier Maps also enable casual learners to get an intellectual grasp on a certain area of study by learning its history and current trends.

Part II: Adjacent Futures. Presenter: Bradly Alicea.

The second talk was given by Bradly Alicea, and involved introducing the idea of Adjacent Futures, which is based on the notion of the adjacent possible. Our focus was on both the possibilities that define scientific discovery and interdisciplinary exploration and the factors (sometimes quite practical) that block combinatorial discovery that often define the boundaries of a given field.

Part III: The Place of Development in the History of CogSci. Presenters: Jesse Parent and Anusha Sharma.

This session (brought to us by Jesse Parent and Anusha Sharma) covered the rich history of developmental approaches in Cognitive Science, and how development has served as an alternative to the concept of “static adult minds”. The presentation was an in-depth presentation of the review article “The Place of Development in the History of Psychology and Cognitive Science” by Gabriella Airenti (Frontiers in Psychology, 10, 895). In the session, we explored the foundational concepts of Piaget as well as longstanding debates such as nature vs. nurture and representationalism vs. brain function.

Part IV: Neurodiversity and Cognitive Science. Presenter: Jesse Parent.

The fourth presentation was also by Jesse Parent, and covered the role of Neurodiversity in Cognitive Science. This was a short review of a book called “Neurodiversity Studies: a new critical paradigm”. Neurodiversity covers a number of alternative frameworks for understanding human cognition, including but not limited to queer, feminist, and critical race perspectives. Such perspectives contribute not only to the diversity of views in the field, but also lead to novel intellectual trajectories.

Part V: Trajectories of Interest in Developmental Psychobiology. Presenter: Avery Lim.

Avery Lim presented on a number of possible trajectories for developmental psychobiology considered broadly. This possibility space (discussed in Part II) includes building off of the study of phenomenology such as developmental critical periods or computational models of psychophysiology. A number of open questions were also posed that motivated our discussions in Part VI.

Part VI: Open Discussion!

If you are interested in discussing these topics further, you might be interested in joining the Orthogonal Lab Slack or the Computational Critical Periods Discord. You can also catch Saturday Morning NeuroSim weekly on YouTube, or get in contact to get on our mailing list and join in person.

Deep Reading Brings New Things to Life (Science)

Here is an interesting Twitter thread from Jacquelyn Gill on 'deep reading':

The basic idea is that exploring older literature can lead to new insights, which in turn lead to new research directions. The new research of our era tends to focus on the most relevant and cutting-edge literature [1]. This recency bias excludes many similarly relevant articles, including articles that perhaps inspired the more recent citations to begin with [2]. 

I have my own list of deep reads that have influenced some of my research in a similar fashion. These references can be either foundational or so-called "sleeping beauties" [3]. Regardless, I am doing my part to maintain connectivity [4] amongst academic citation networks:

1) Woodger, J.H. The Axiomatic Method in Biology. 1937.

An argument for biological rules, an influence on cladistics (developed in the 1960s), and a natural bridge to geometric approaches to data analysis and modeling. While there is a strong argument to be made against the axiomatic approach [5], this directly inspired much of my thinking in the biological modeling area. 

2) Davis R.L., Weintraub H., and Lassar A.B. Expression of a single transfected cDNA converts fibroblasts to myoblasts. Cell 51, 987–1000. 1987.

This was the first proof-of-concept for direct cellular reprogramming, and predates the late 2000's Nobel-winning work in stem cells by decades. In this case, a single transcription factor (MyoD) was used to convert a cell from one phenotype to another without a strict regard for function. More generally, this paper helped inspired my thinking in the area of cellular reprogramming to go beyond a biological optimization or algorithmic approach [6].

3) Ashby, W.R. Design for a Brain. 1960.

"Design for a Brain" serves as a stand-in for the entirely of Ashby's bibliography, but this is the best example of how Ashby successfully merged explanations of adaptive behavior [7] with systems models (cybernetics). In fact, Ashby originally coined the phrase "Intelligence Augmentation" [8]. I first discovered Ashby's work while working in the area of Augmented Cognition, and has been more generally useful as inspiration for complex systems thinking.

4) Schroeder, M. Fractals, Chaos, and Power Laws: minutes from an infinite paradise. W.H. Freeman. 1991. AND Kauffman, S. At Home in the Universe: search for the laws of self-organization and complexity. Oxford University Press. 1995.

Not so much a couple of sleeping beauty as easy reading technical reference guides for all things complexity theory.

5) Bourdieu, P. Outline of a Theory of Practice. Cambridge University Press. 1977 AND Alexander, C., Ishikawa, S., and Silverstein, M. A Pattern Language: towns, buildings, construction. Oxford

University Press. 1977.

This is a bonus, not because the references are particularly obscure or even from the same academic field, but because they partially influenced my own view of cultural evolution. This is yet another piece of advice to young researchers: take things that appear to be disparate on their surface and incorporate them into your mental model. If nothing else, you will gain valuable skills in intellectual synthesis.

UPDATE (11/17):

Here is another example of old (classic, not outdated) work influencing new scholarship.

NOTES:

[1] Evans, J.A. (2008). Electronic Publication and the Narrowing of Science and Scholarship. Science, 321(5887), 395-399 AND Scheffer, M. (2014). The forgotten half of scientific thinking. PNAS, 111(17), 6119.

[2] related topics discussed on this blog include distributions of citation ages and most-cited papers.

[3] van Raan, A.F.J. (2004). Sleeping Beauties in Science. Scientometrics, 59(3), 467–472.

[4] Editors (2010). On citing well. Nature Chemical Biology, 6, 79.

[5] For the semantic approach (which had been influential to my more recent work), please see: Lloyd, E.A. (1994). The Structure and Confirmation of Evolutionary Theory. Princeton University Press, Princeton, NJ.

[6] Ronquist, S. et.al (2017). Algorithm for cellular reprogramming. PNAS, 114(45), 11832–11837.

[7] Sterling, P. and Eyer, J. (1988). Allostasis: A new paradigm to explain arousal pathology. In "Handbook of life stress, cognition, and health". Fisher, S. and Reason, J.T. eds. Wiley, New York. 

[8] Ashby, W.R. (1956). An Introduction to Cybernetics. Springer, Berlin.

Loose Ends Tied, Interdisciplinarity, and Consilience

LEFT: A network of scientific disciplines and concepts built from clickstream data. RIGHT: Science mapping based on relationships among a large database of publications. COURTESY: Figure 5 in [1] (left) and SciTech Strategies (right).

Having a diverse background in a number of fields, I have been quite interested in how people from different disciplines converge (or do not converge) upon similar findings. Given that disciplines are often methodologically distinct communities [2], it is encouraging when multiple disciplines can exhibit consilience [3] in attacking the same problem. For me, it is encouraging because it supports the notion that the phenomena we study are derived from deep principles consistent with a grand theorizing [4]. And we can see this is areas of inquiry such as learning and memory, with potential relevance to a wide variety of disciplines (e.g. cognitive psychology, history, cell biology) and the emergence of common themes according to various definitions of the phenomenon.

Maximum spanning tree of disciplinary interactions based on the Physics and Astronomy Classification Scheme (PACS). COURTESY: Figure 5 in [5].

The ability to converge upon a common set of findings may be an important part of establishing and maintaining coherent multidisciplinary communities. Porter and Rafols [6] have examined the growth of interdisciplinary citations as a proxy for increasing interdisciplinarity. Interdisciplinary citations tend to be less common than within-discipline citations, while also favoring linkages between closely-aligned topical fields. Perhaps consilience also relies upon the completeness of literature inclusion for people from different disciplines in an interdisciplinary context. Another recent paper [7] suggests that more complete literature citation might lead to better interdisciplinary science and perhaps ultimately consilience. This of course depends on whether the set of evidence itself is actually convergent or divergent, and what it means for concepts to be coherent. In the interest of not getting any more abstract and esoteric, I will leave the notion of coherence for another post.


NOTES:
[1] Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M.A., and Balakireva, L. (2009). Clickstream Data Yields High-Resolution Maps of Science. PLoS One, 4(3), e4803. doi:10.1371/journal.pone.0004803.

[2] Osborne, P.  (2015). Problematizing Disciplinarity, Transdisciplinary Problematics. Theory, Culture, and Society, 32(5-6), 3–35.

[3] Wilson, E.O. (1998). Consilience: the unity of knowledge. Random House, New York.

[4] Weinberg, S. (1993). Dreams of a Final Theory: the scientist's search for the ultimate laws of nature. Vintage Books, New York.

[5] Pan, R.J., Sinha, S., Kaski, K., and Saramaki, J. (2012). The evolution of interdisciplinarity in physics research. Scientific Reports, 2, 551. doi:10.1038/srep00551.

[6] Porter, A.L. and Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81, 719.

[7] Estrada, E. (2017). The other fields also exist. Journal of Complex Networks, 5(3), 335-336.

A Tree of Deeper Experiences -- the Authorship Tree

One of the most difficult aspects of academic publishing with multiple authors is in determining the order of authorship. In many fields, authorship order is the key to job promotion. Unfortunately, these conventions vary field, while the criteria for authorship slots often varies by research group. Since a responsible accounting of contributions are key to determining authorship and authorship order [1], it is worth considering multiple possibilities for conveying this information.

Example of an Authorship list (with affiliations)

A mathematics or computer science researcher might also see the problem as one of choosing the proper representational data structure. The authorship order, no matter how determined, is a 1-dimensional queue (ordered list). Even though some publishers (such as PLoS) allow for footnotes (an inventory of author contributions), there is still little room for nuance.

Example from "The Academic Family Tree"

But is there a better way? Academic genealogies provide one potential answer. A typical genealogy can be thought of as a 1-dimensional order, from mentor to student. In reality, however, an academic have multiple mentors, influenced by a number of predecessors. The construction of academic family trees [2] is one step in this direction, turning the 1-dimensional graph into a 2-dimensional one.

Picture of the Authorship tree cover. COURTESY: "The Giving Tree" by Shel Silverstein

This is why Orthogonal Lab has just published a hybrid infographic/paper called the The Authorship Tree [3]. This is a working document, so suggestions are welcome. The idea is to not only determine the relative scope of each contribution, but also to graphically represent the interrelationships between authors, ideas, and scope of the contributions.

As we can see from the example below, this includes not only our authors, but also people from the acknowledgements, funders, reviewers, authors of important papers/methods, and funders. While the ordering of branches along the stem suggests an authorship order, they are actually ranked according to their degree of contribution [4]. To this end, there can be equivalent amounts of contribution, as well as inclusion of minor contributors not normally included in an authorship list.

Example of an authorship tree (derived from original 1-D author list).

NOTES:
[1] Cozzarelli, N.R. (2004). Responsible authorship of papers in PNAS. PNAS, 101(29), 10495.

[2] David, S.V. and Hayden, B.Y. (2012). Neurotree: A Collaborative, Graphical Database of the Academic Genealogy of Neuroscience. PLoS One, 7(10), e46608. doi:10.1371/journal.pone.0046608.

[3] Orthogonal Lab (2017). The Authorship Tree. Figshare, doi:10.6084/m9.figshare.4731913.

[4] For more on the point system convention, please see: Venkatraman, V. (2010). Conventions of Scientific Authorship. Science Issues and Perspectives, doi:10.1126/science.caredit.a1000039.

Be as Brief as Possible but no Briefer

Nature Highlights article on the Journal of Brief Ideas, which itself is brief.

No, this is not an Einstein quote. But Einstein very well may have submitted to the Journal of Brief Ideas [1], an open access version of Occam's razor. I just submitted a brief paper called "Playing Games with Ideas: when epistemology pays off", which is the equivalent of a fully-indexed abstract [2]. While some people might find 200 words to be too brief, the Journal allows for attachments to be submitted, thus allowing a bit of circumventing with regard to the word limit [3].

According to the Journal FAQ, submitting such brief reports is part of establishing something below the current standard for the minimal publishable unit. It is also important for enforcing good scientific citizenship practices [4]. Very short papers have occasionally been published in regular journals. Mathematics papers by Lander and Parkin [5] and Conway and Soifer [6] accomplished mathematical proofs in less than a paragraph (but with multiple figures). Other than these rather mythical examples, it is quite the challenge to integrate a well-formulated idea into the Journal of Brief Ideas' 200 word limit.


NOTES:
[1] Woolston, C. (2015). Journal publishes 200-word papers. Nature, 518, 277.

[2] Indexing done via document object identification on Zenodo, doi:10.5281/zenodo.167647

[3] If a picture is worth 1000 words, then the Journal of Brief Ideas become less brief than its name implies.

[4] Neisseria (2015). All you need to publish in this journal is an idea. Science Made Easy blog, February 13.

[4] Lander, L.J. and Parkin, T.R. (1966). Counterexample to Euler's Conjecture on sums of like powers. Bulletin of the American Mathematical Society, 72(6), 1079.

[5] Conway, J.H. and Soifer, A. (2004). Can n² + 1 unit equilateral triangles cover an equilateral triangle of side > n, say n + ɛ? American Mathematical Monthly, 1.

Learning by Doing, Where Doing is Earning Badges

As a member of the OpenWorm Foundation community committee (see previous post), we have been trying to find a means of engaging potential contributors within the context of the various projects. One type of activity is the Badge, a bite-sized [1] learning opportunity that we plan to use as both certifications of competency and concrete goals for the various projects. The OpenWorm Badge System is being spearheaded by Chee-Wai Lee, and is an emerging method in Educational Technology [2]. More details about this will be shared to the community by Chee-Wai in the form of a tutorial at the upcoming OpenWorm Open House.

An example of how semantic data on phenotypes can be extracted from the scientific literature. PICTURE: Tagxedo.com, BLOGPOST: Phenoscape blog. 

Each badge is designed to impart a specific skill. The OpenWorm badge system currently covers scientific topics (Muscle Model Builder, Hodgkin-Huxley) and research skills (Literature Mining). My contribution is the Literature Mining (LM) series. Literature mining is a technique used to organize the scientific literature, extract useful metadata (e.g. semantic data) from these sources, and identify secondary datasets for re-analysis [3]. Learning skills in Literature Mining will be useful to a wide range of badge earners, particularly those interested in Bioinformatics and Open Science research. These are skills used extensively in the DevoWorm project, and we will be planning more badges on related topical areas in the future.

The first LM badge is focused on working with the scientific literature, while the second (LMII) badge introduces learners to open-access secondary datasets. The only prerequisite is that you must earn Badge I in order to earn Badge II. Both of these badges recently went live, and you may start working on them immediately.

Example of the badge curriculum for LMI. The badgelist system requires learners to complete each step one at a time, and then request feedback (if applicable) from the Admin (e.g. instructor).

NOTES:
[1] why not "byte-sized", you say? Well, the Literature Mining badges are almost byte-sized (seven requirements apiece), so you could say that we are headed in that direction!

[2] Ferdig, R. and Pytash, K. (2014).  There's a badge for that. Tech and Learning, February 26.

[3] For examples of how Literature Mining can be useful, please see the Nature site for news on literature mining research.

The Data of Stories, Recent Developments

The following features are cross-posted on Tumbld Thoughts. The first featuee is a nice set of resources on the shape of stories. The first one is a lecture (video) by Kurt Vonnegut [1], circa 1985 on the qualitative shape of various narratives.

An Infographic [2] can also be used to show Vonnegut’s story shapes in more detail. As we can see, there are a limited number of story motifs (the function), each with an associated emotional state (the amplitude of the function). In Vonnegut's formulation, these functions are largely qualitative, with no clear statistical validity.

A new paper [3] on the computational study of storytelling makes a more quantitative attempt to characterize the shape and statistics of Vonnegut's functions using a large dataset (over 1700 narratives from Project Gutenberg) and data mining techniques to quantitatively uncover these patterns.

The two images above are from Figures 2 (an illustration with Harry Potter) and 4 (the full Support Vector Machine -- SVM -- Analysis) in [3], respectively.

Tangentially, we also have a dataset that describes the career of Robert DeNiro. In fact, we can characterize the self-imposed timelessness of Robert DeNiro in two images [4, 5]. Taken together, these images suggests there are actually two points in time (July 1999 and August 2002) at which Robert DeNiro stopped caring [4].

NOTES:
[1] Kurt Vonnegut on the shape of stories, YouTube.

[2] Infographic by mayaeilam, visual.ly.

[3] Reagan, A.J., Mitchell, L., Kiley, D., Danforth, C.M., and Dodds, P.S. The emotional arcs of stories are dominated by six basic shapes. arXiv, 1606. 07772 (2016).

[4] SOURCE: Reddit’s dataisbeautiful

[5] Heisler, Y.   Nine ancient and abandoned websites from the 1990s that are still up and running. BGR, July 24, 2015.

Social Capital Meets Social Media in the Service of Peer Review

What is the proper reward for serving as a peer reviewer? Until now, the reward has been increased social capital [1] in the academic community. Yet like everything else, social media has served to quantify and formalize these relationships.

Regardless of their potential for success [2 3], two new services have attempted to "give credit" for the act of peer reviewing. While not explicity monetary, the idea is to formalize credit for an often thankless task that is a vital part of the academic community.

The first of these services is Publons. Named after the "least publishable unit", Publons allows you to formally publish and cite your peer reviews [4]. While the most prolific reviewers seem to be doing their work purely for within-site prestige, treating peer reviews like published manuscripts is an intriguing idea. Publons is also integrated with select proprietary and open-access publishers, making the service most than merely a self-contained curiosity.

The second is Academic Karma. As with Publons, peer reviews are made to be creditable and archivable. In addition, reviewers are unbundled from specific journals, which can either be a good thing or a bad thing depending on the context. The accounting system is linked to your ORCID account (almost every University-based academic is likely to have one), which makes the crediting system portable.

UPDATE (5-19): In keeping with the theme (in an appropriately timely manner), I was mentioned in a new PLoS One feature [5] as one of many reviewers who kept PLoS One publishing for the year of 2014.

NOTES:
[1] Social capital can be defined as social benefits derived from one's social network. Units of social capital are often derived from providing public goods, gifting, or the exchange of favors. However, social capital accumulation can also be an indicator of reputation (e.g. the more social capital one holds, the greater their reputation).

For a less-than-idyllic example from an academic context, please see: Graur, D.   Payback time for referee refusal. Nature, 505, 483 (2014).

[2] Hossenfelder, S.   Publons. Backreaction blog, April 17 (2015).

[3] Saunders, N.   Academic Karma: a case study in how not to use open data. What You're Doing is Rather Desperate blog, February 19 (2015).

[4] Van Noorden, R.   The Scientist Who Get Credit for Peer Review. Nature News, October 9 (2014).

[5] PLOS ONE 2014 Reviewer Thank You. PLoS One, 10(2), e0121093 doi:10.1371/journal. pone.0121093 (2015).

A Modest Framework for Scientific Transparency

Here are six points for the integration of open-access science publishing and open data. This was developed from personal practice and research in addition to interactions with the Research Data Service (University of Illinois) and the SciFund challenge. This pipeline begins at the write-up stage, but some points rely on practice prior to analysis and write-up.

A)   Preprint (e.g. kernel of hypothesis- or question-driven results).

A number of options exist for this, including arXiv, bioRxiv, PLoS One, or another permanent location that provides a formal archival address or digital object identifier (doi). The core paper should be brief (6-12 pgs) and formal.

B)   Advanced methods/theory.

These can be submitted as supplemental materials, either in the same repository as the preprint itself or on another permanent server. As opposed to simple auxillary files, this should be set up more along the lines of an iPython notebook.

C)   Advanced Analysis.

This can be treated in the same manner as the advanced methods/theory. This will include transformational datasets (e.g. time-frequency decompositions, log transforms, combinations of data from multiple sources in a common framework) and the associated data tables and figures/graphs.

D)   Datasets.

1)   Raw Data: images, unprocessed vectorial or matricial output.

These will be stored as formatted image files, ASCII files, or tabular files.

2)   Processed Data: numeric variables, simple annotation.

These will be appended to the raw data either in the file or as linked files in the same directory.

3)   Higher-level Data: correlational, data fusion, decompositional.

These will include the transformational datasets mentioned in the section on Advanced Analysis. These datasets are to be linked to the raw and processed data directory. Simple annotation methods will confirm the identity.

4) Higher-level Representation: RDF/XML descriptive models, algorithmic (e.g. data landscapes, possibility spaces).

These types of representations can help us go beyond the typical reliance on “statistical significance” and “future directions” to provide a rigorous approach to guide future investigations. An example of this is parameterization models from existing data.

E)   Blogging Publicity.

All materials should be promoted through a blog post. This can be in the form of a feature article, or as a series of annotated links. This can be followed up with reposting key features of the initial post to a social blog like Tumblr or sharing a link via Twitter.

F)   Peer Commentary.

While this is typically kept confidential, there are so-called post-peer-review venues that provide a means to review work (e.g. PeerJ, F1000). This includes both formal (actionable) statements and informal statements in the form of critiques. 

This outline represents the entirely of a scientific reporting pipeline (from formal write-up to published items), although I am no doubt missing something. I will be fleshing each of these points out in future posts with real data and examples from Orthogonal Research and my work at the University of Illinois.

SciNote: the science blog

I would like to announce the ramping up of a new science blog I am involved in called SciNote. Initially hosted (and still active) on Tumblr, SciNote is a collection of submissions by contributors, editors, and content discoverers. As of this month, I will be serving as the editorial supervisor. So check out the SciNote blog today. Perhaps you will be interested in submit content, supporting the blog's mission, or even joining the staff (on a voluntary basis).

Piketty Reviews: the year in review

Thomas Piketty's book "Capital in the Twenty-first Century" became quite the phenomenon this year. Originally published in French, it was translated into English in 2014 and has since elicited a large amount of feedback. I have collected a series of book reviews over the course of this year that provide a bit of perspective on the book. This could either prove to be prophetic, or another "End of History and the Last Man". The diversity of responses presented here suggests that the relationship between inequality and economic growth will become a defining social issue in years to come.

Even at 696 pages and a large number of graphs, it is quite a captivating read. Piketty synthesizes data from multiple sources and arrives at a fundamental set of relationships between concentrations of capital (e.g. inherited wealth) and economic growth (e.g. the diffusion of capital into the broader economy). Based on this intellectual synthesis, Piketty's presents two laws of inequality [1, 2]. These laws are drawn from the cross-national and historical data analyses. In particular, the second law serves as shorthand for the book's main thesis. While people might debate how exactly to define "wealth" and "growth" or how well this framework describes the macroeconomic present, Piketty's book gives us the conceptual tools to discuss these issues more clearly.

Piketty's insight is quite simple: there is a proportional and often unbalanced relationship between wealth and growth that transcends both nation and historical era. When the returns on inherited wealth exceeds income growth generated by resource exploitation, entrepreneurship, or innovation, high degrees of social and economic inequality result (W > G -- see Figure 1). This often occurs when growth is slow or nonexistant, and the rate of return on inherited capital exceeds growth by default. In terms of social relations, the W > G scenario allows for inherited wealth to triumph over social mobility and new wealth creation. By limiting social mobility, a host of related factors act to reinforce income inequality [3]. Yet this relationship does not always hold. For example, historical periods during which opportunities for economic expansion and social mobility exceed the power of inherited wealth (such as the latter half of the 20th century) tend to be characterized by high rates of conventional growth (e.g. increases in GDP). While the power of inherited wealth is curbed by growth, it might also be curbed by taxation policy. In any case, the second half of the 20th century scenario can be formulated as G > W, or growth exceeding wealth.

Figure 1. Extreme inequality, shown in both artistic and symbolic logical form.

Piketty arrives at this conclusion by using historical data. These data suggest that the slow growth and high levels of inequality which characteerize the early 21st century will recapitulate a pattern typical of the 19th century or even the European middle ages. The predominance of rentier behavior amongst the 21st century elite is indeed reminiscent of the medivel era, where the primary source of wealth generation came from rents paid to a landed gentry [4]. While the mode of wealth generating is variable from century to century, the basic tension between inherited versus newly-generated wealth is predicted to govern economic dynamics. And in this context, inequality can inflence a host of societal characteristics, from social stratification to technological innovation [5].

Perhaps these consequences of inequality are simply a consequence of an over-domineering financial industry, which provides massive returns to investment income relative to labor productivity. In this sense, history is more contextual than cyclical. But history can also parallel broadly-stated theoretical predictions. This state of affairs can be compared with the prediction made by Karl Marx with respect to the end of capitalism itself [6]. As capitalism matures (so-called "late stage" capitalism), we can expect most forms of labor to become devalued. While this is not something that Piketty predicts for the future, this devaluation is due to both various resource consolidations promulgated by the owners of capital and a by-product of technological innnovation (particularly automation -- see [7]). Piketty's solution to countering this type of structural inequality is wealth redistribution, which is something America pioneered [8], but is needed on a global scale to avoid the predicted negative consequences of economic growth stagnation [9].

Here is my collection of Piketty reviews

Introducing Piketty:

Galbraith, J.K.   Kapital for the Twenty-first Century? Institute for New Economic Thinking blog, March 31 (2014).

Frankel, J.   Piketty's Fence. Jeffrey Frankel's blog, September 22 (2014).

Yglesias, M.   The Short Guide to Capital in the 21rst Century. Vox blog, April 8 (2014).

Dorman, P.   Piketty for Dummies. EconoSpeak blog, April 26 (2014).

Wolf, M.   "Capital in the Twenty-first Century", by Thomas Piketty. FinancialTimes.com, April 15 (2014).

R.A.   Thomas Piketty's "Capital", summarized in four paragraphs. Economist, May 4 (2014).

Cowen, T. and de Rugy, V.   Why Piketty's Book Is a Bigger Deal in America Than in France. NYTimes The Upshot, April 29 (2014).

Eakin, E.   Capital Man. Chronicle of Higher Education, April 17 (2014).

Broader Economic Implications:
An Interview with Adair Turner: "Which Capitalism for the 21rst Century?". Institute for New Economic Thinking blog, November 12 (2013).

Hutton, W.   Capitalism simply isn't working and here are the reason why. The Guardian, April 12 (2014).

Boucoyannis, D.   Adam Smith is not the antidote to Thomas Piketty. WaPo Monkey Cage blog, April 22 (2014).

Cassidy, J.   Forces of Divergence: is surging inequality endemic to capitalism? The New Yorker, March 31 (2014).

Krugman, P.   Why we're in a New Gilded Age. New York Review of Books, April 10 (2014).

Shenk, T.   Thomas Piketty and Millenial Marxists on the Scourge of Inequality. The Nation, April 14 (2014).

Faux, J.   Thomas Piketty Undermines the Hallowed Tenets of the Capitalist Catechism. The Nation, April 18 (2014).

Rosenberg, P.   Thomas Piketty terrifies Paul Ryan: Behind the right’s desperate, laughable need to destroy an economist. Salon, April 30 (2014).

Ritholtz, B.   Piketty vs John Stuart Mill’s Marketplace of Ideas. The Big Picture blog, May 1 (2014).

Kaminska, I.   Inequality and Hyperinflation. Dizzynomics blog, April 25 (2014).


Criticisms: In May, there was a post on the Financial Times' Money Supply blog that claimed to find flaws in Piketty's data analyses and basic aproach to studying inequality. The following articles are a rebuttal to these claims.

Piketty, T.   Appendix to Chapter 10. Inequality of capital ownership. Addendum: response to FT. May 28 (2014).

Buchanan, M.   Economists, Show your Assumptions. Bloomberg View, May 6 (2014).

Irwin, N.   Everything You Need to Know About Thomas Piketty vs. The Financial Times. NY Times The Upshot, May 30 (2014).

Winship, S.   Financial Times vs. Piketty on US. Smoke, No Fire. Forbes, June 2 (2014).

Return to an Ancien Regime?

So is growth truly over? Or are we transitioning to a new mode of production? Perhaps it is not the nature of growth that guides thinking about this but the existential need for a powerful ruling class. Such a desire for oligarchy mirrors many popular interpretations of Piketty's main thesis, but in a more fatalistic manner. This hidden cultural theme might explain the recent (and disturbing) trend towards neo-reactionary thought amongst certain segments of Western society [10, 11]. So-called neo-reactionary thinking involves a combination of radical libertarianism with dictatorship. In and of itself, this would be a fairly predictable reaction to a period of great social and economic change. Yet this movement even has legions in the technology industry, a social milieu that a) represents the "new" economy and a prime source of future economic growth, and b) represents an industry that could help us overcome the limitations of traditional growth. This might reflect an inability to think innovatively about social and cultural change, or perhaps it shows how inerred we truly are to old ideas.


NOTES:
[1] Galbraith, J.K.   Unpacking the First Fundamental Law. Economist's View blog, May 25 (2014). AND Krussell, P. and Smith, T.   Is Piketty's "Second Law of Capitalism" Fundamental? Vox blog, June 1 (2014).

[2] von Schaik, T.   Piketty's laws with investment replacement and depreciation. Vox blog, July 6 (2014).

[3] Krugman, P.   Piketty Day Notes. Conscience of a Liberal blog, April 16 (2014).

[4] Kaminska, I.   The Tyrrany of Land. Dizzynomics blog, February 5 (2014).

[5] Hanlon, M.   Why has human progress ground to a halt? Aeon Magazine, December 3 (2014).

[6] Jeffries, S.   Karl Marx's guide to the end of capitalism: a primer. The Guardian, October 20 (2008).

[7] Gordon, R.J.   Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds. NBER Working Paper No. 18315 (2012).

[8] Geier, K.   Taking Aim at Inequality. Blog of the Century, March 12 (2014) AND Yglesias, M.   If growth is dead, we need radical redistribution. Moneybox blog, October 7 (2013).

[9] Cowen, T.   "Unified Growth Theory" by Oded Galor. Marginal Revolution blog, June 9 (2011) AND Kuznets Curve. Wikipedia. December 30 (2013).

[10] Pein, C.   Mouthbreathing Machiavellians Dream of a Silicon Reich. The Baffler, May 19 (2014).

[11] Brin, D.   "Neo-Reactionaries" drop all pretense: End democracy and bring back lords! Contrary Brin blog, November 26 (2013).

Starstuff Squared, Rubik's Cubed

Welcome to the 250th Synthetic Daisies post! This post consists of three subthemes cross-posted from Tumbld Thoughts. The first is in honor of the Google Doodle for the 40th Anniversary of the Rubik's Cube, while the latter two are the supplemental readings for the tenth and eleventh episodes of the Cosmos reboot. 

I. Rubik's 3-D CSS Cubes

Today is the 40th anniversary of the Rubik's Cube. Aside from an invention that sold 350 million copies, Rubik's Cube is also an example of a permutation puzzle that contains an interesting problem related to group theory. The doodle itself is unique in that it utilizes a technology called CSS 3-D transforms [1]. Naturally, there is a Google Doodle.

[1] Edidin, R.   How Google Built Its 3-D Interactive Rubik’s Cube Doodle. May 19 (2014). Also check out the Chrome Cube Lab, which uses this technology to render interactive cube-based puzzles beyond Rubik's namesake.

II. All I Want For Christmas is an Electric Charge

Here are the supplemental readings for the tenth episode of the Cosmos reboot ("The Electric Boy"). Readings are organized by theme.

The Electric Boy and his Legacy:

Michael Faraday (1791-1867). BBC History.

History of the Christmas Lectures. The Royal Institution.

Cody, D.   Social Class. The Victorian Web. July 12 (2002).

Burgess, M.P.D.   Semiconductor History: Faraday to Shockley. Transistor History (2008).

Williams, A.T.   Faraday vs. Maxwell and Faraday and the Ether. Consciousness, Physics, and the Holographic Paradigm (2010).

Electromagnetic Spectrum. NASA Goddard Space Flight Center.

Frog Legs and Televisions:

Galvani's animal electricity experiments. Institute of Engineering and Technology.

Luigi Galvani (1737-1798). Center for Integrating Research + Learning, Magnet Lab, Florida State University.

Borgens, R.B., Vanable, J.W., and Jaffe, L.F.   Bioelectricity and regeneration. I. Initiation of frog limb regeneration by minute currents. Journal of Experimental Zoology, 200(3), 403–416 (1977).

Iconoscope. Wikipedia, March 20 (2014).

Philo Farnsworth (1906-1971), Electronic Television. Inventor of the Week Archive, Lemelson-MIT (1999).

Researching Faraday Cages and Electromagnetic Fields on Google is a sad statement on Internet culture:

Chandler, N.   How Faraday Cages Work. How Stuff Works.

Trottier, L.   A Growing Hysteria. Committee for Skeptical Inquiry, CFI. October (2009).

Hruska, J.   New research confirms that our electronics and radio waves disrupt migratory birds. Extremetech, May 8 (2014).

Magnetic Fields of the Earth. HyperPhysics.

Inventions/Discoveries of the Electric Boy:

Faraday's Inventions. Michael Faraday's World.

Homopolar Generator, Wikipedia. April 1 (2014).

Electrolysis, Wikipedia. May 7 (2014).

Faraday Cage, Wikipedia. March 19 (2014).

Electric Motor, Wikipedia. May 10 (2014).

Static Electricity, Wikipedia. May 5 (2014).

III. Leaving Nothing but Footprints, but Still Living On.

Here are the supplemental readings for the eleventh installment of the Cosmos reboot ("The Immortals"). As usual, readings are organized by theme.

Entropy Is Not Immortality, Time Can Be Written Down:

Matson, J.   What Keeps Time Moving Forward? Blame It on the Big Bang. Scientific American, January 7 (2010).

Mlodinow, L. and Brun, T.A.   Relation between the psychological and thermodynamic arrows of time. Physical Review E, 89, 052102 (2014).

Wall, M.   Video Tour of Alien Planets Shows How Time Flies on Strange New Worlds. Space.com, May 07 (2014).

Jones D.L.   Aging and the germ line: where mortality and immortality meet. Stem Cell Reviews, 3(3), 192-200 (2007).

Barksdale, M.   10 Methods of Measuring Time. Discovery TV: Relativity and Time.

Origins of Writing Systems. AncientScripts.com.

Fun With the Origins of DNA:

Akst, J.   RNA World 2.0. The Scientist, March 1 (2014).

Moran, L.A.   Changing Ideas About the Origin of Life. Sandwalk blog, August 7 (2012).

Joshi, S.S.   Origin of Life: the Panspermia Theory. December 2 (2008).

Klyce, B.   Cosmic Ancestry. 

Saenz, A.   Venter creates first synthetic self-replicating bacteria from scratch. SingularityHub, May 20 (2010).

Moving Life (via Dispersal):

Levin, S.A., Muller-Landau, H.C., and Nathan, R.   The Ecology and Evolution of Seed Dispersal: a theoretical perspective. Annual Review of Ecology, Evolution, and Systematics, 34, 575-604 (2003).

Gronstal, A.   Space Rocks Could Reseed Life on Earth. Astrobiology Magazine, May 15 (2008).

Interstellar Dust Could Have Building Blocks Of Life – On Science. RedOrbit, January 28 (2014).

Can our TV signals be picked up on other planets? BBC News, August 6 (2008).

Civilization is (not) Forever:

Chandler, G.   Desertification and Civilization. Saudi Aramco World, 58, 6 (2007).

Arbesman, S.   210 Reasons for the Fall of the Roman Empire. Social Dimension blog, June 26 (2013).

Kunzig, R.   Geoengineering: How to Cool Earth--At a Price. Scientific American, November (2008).

Duncan, R.C.   The Olduvai Theory: sliding towards a post-industrial Stone Age. Institute on Energy and Man, June 27 (1996).

Math Program Cracks Cause of Venus Hell Hole. Space Daily, March 21 (2001).