Showing posts with label economics. Show all posts
Showing posts with label economics. Show all posts

February 20, 2026

10^6 All Time Pageviews: why all the attention?

"If I had a mutant strain for every misattributed quote on the internet, I'd have over a million mutant strains" -- Salvator Luria

Since the above quote is obviously misattributed, a trend that will no doubt get worse before it gets better [1], it's worth reflecting on how pageviews get counted and the workings of the attention economy.

As of February 10, Synthetic Daisies blog has 1,000,000 page views. One million! Ten to the sixth power! After 17 years, 1 month, and 16 days of posting, then not posting, and finally doing a lot of cross-posting, all while specializing in nothing. All the while not (as far as I know) having been mentioned in the Epstein files [2]. In fact, being skeptical of tech hype and dismayed by scientific racism. And with a nearly unbroken streak of Darwin Day posts since 2009, and many posts on science communication and open access. In terms of volume, this blog peaked in the 2012-2014 period and have declined since, as did a lot of science-oriented blogging enterprises.

In the past 2 years or so, there has been a great uptick in pageviews [3]. I am not sure why, although word on the light cycle trail is that most internet traffic is the product of automation. Nevertheless, a million pageviews of content is something of a milestone. Not least of which is that I can continue my pageview milestone/solar system metaphor. So here it is, "Synthetic Daisies blog" (a.k.a. Voyager 1) leaving the solar system.

As for the attention economy, it does seem that things with deep academic content do not get the numbers of views or followers that shallower content or (especially) conspiracy theories do [4]. In my mind, this is baked in: for YouTube content, this can be demonstrated. So much for monetization! Although revisiting an early post on the arcanocracy might address some of this.  

References:

[1] Garry et.al (2024). Large language models (LLMs) and the institutionalization of misinformation. Trends in Cognitive Science, 28(12), 1078-1088.

[2] a) apparently, this blog is officially run by a "loser", b) we really need to revisit the relationship between Epstein, John Brockman, Adam Bly, and the popular science of late 2000s vintage. I was a big fan of Seed Magazine and Edge.org, which were apparently edgy in more ways than one.  

[3] The 500,000 pageviews milestone was reached in December of 2022.

[4] While a lot of this is platform-dependent, some of this is affinity scam dependent. See Joe Rogan and his relationship with Spotify.

January 31, 2026

What if there had been a Chaos/Fractals bubble?

 

Why does one research culture drive a bubble while another does not? COURTESY: Soap Bubbles and Chaos, Journal of Pneumatic Adventures Medium.

 

Modern Artificial Intelligence (AI) research is currently at the heart of a massive financial bubble. It might pop soon, and it might not. "AI" is seemingly everywhere, although its actual value is yet to be determined. I remember learning how to program GPUs in the period around 2010 for applications to computational biology, never thinking that an esoteric research topic could become elemental in propping up the tech economy.

This got me to thinking about what it would look like if we switched out one research area for another, just to highlight any potential absurdities of the situation. So imagine if the field of Chaos and Fractals, quite popular to the point of cliche in the 1980s, was the subject of a financial bubble. It is of note that chaos and fractals were definitely hyped in their time, being featured in movies such as Star Trek: the Wrath of Khan (the Genesis Effect scene) and Ian Malcolm's rhetoric in Jurassic Park. Interestingly, advances in chaos and fractals in particular relied on advances in computing power, initially with supercomputing, and later with GPUs, parallel computing, and quantum computing [1].

In the formative years of chaos, people such as the Eds (Ott and Lorenz) [2, 3] produced a paradigm shift in how complex systems were viewed. The visualization of chaos in the form of fractals were advanced by Benoit Mandelbrot [4]. Fractals were likewise a paradigm shift in how complex phenomena were visualized. In particular, fractals visualize various aspects of chaos using non-Euclidean geometries and relatively simple sets of equations. Their popularity was advanced by a convenient shorthand: visualizations that captured the imagination. While slogans and pretty pictures captured the imagination, the popular imagination got quite far ahead of methodological rigor. This is reminiscent of claims that ascribe properties like sentience or superintelligence to AI systems.

Is this science, or inspiration, or both? Please don't financialize this. COURTESY: Moss and Fog blog.

Eventually, enthusiasm for chaos and fractals regressed back into the fields of physics and mathematics, while also becoming specialized tools for fields like finance. In short, the field matured without the irrational influx of cash, roughly following a Gartner hype cycle. This is curious in light of the limits of AI that people discuss today: regardless of whether or not AI exhibits "true" intelligence, AI systems require intense computational resources to merely be evocative of biological intelligence. But what if there is not a missing component of the intelligence simulation, but of the way in which the underlying system is modeled? Chaos and fractals are not the product of reductionist relationships (as science had been done before), but rather the product of system dynamics, recursivity, and a sensitivity to initial condition. This was the main insight of chaos and fractals, but apparently those insights are not worth a large-scale financial bubble [5].

In their time, fractals were derided as "pretty pictures", and eventually, the pretty pictures could not keep up with methodological trends across the different sciences. But fractals did provide at least one serious insight: systems that look regular at one scale exhibit irregularities apparent at other scales. This has been popularized by the Powers of 10 idea and further applied to ideas like the coastline paradox. What is particularly interesting to a person who likes complexity approaches to science is that standard hypothesis testing was exposed to many of the same criticisms as chaos and fractals. This is despite much more serious consequences of the unaddressed issues with NHST and has persisted as the scientific norm in spite of superior methods. Quite an interesting exercise in methodological inertia. 

In the current era, AI has partially been driven by advances in methodology, but also by advances in hardware. Central to this has been NVIDIA and their GPU architecture. While GPUs have done much of the heavy lifting in the current AI summer, it is important to remember the origins of GPUs: as a graphical processing tool. This parallels how advances in computing and computational power suddenly opened up our ability to solve and plot the equations of fractal growth and other structures. Perhaps the experience of chaos and fractals will guide AI research after the bubble bursts.


References:

[1] Kaboudian, A., Cherry, E.M., and Fenton, F.H. (2019). Large-scale interactive numerical experiments of chaos, solitons and fractals in real time via GPU in a web browser. Chaos, Solitons & Fractals, 121, 6-29.

[2] Motter, A. and Campbell, D.K. (2013). Chaos at Fifty. Physics Today, 66(5).

[3] Viswanath, D. (2004). The fractal property of the Lorenz attractor. Physica D, 190, 115–128.

[4] Mandelbrot, B.B. and Blumen, A. (1989). Fractal Geometry: What is it, and What Does it do? Royal Society A, 423(1864), 3–16.

[5] Notice that I said "large-scale", which is the distinction between overeager commercialization and financialization. Perhaps financialization is a feature of 21st century popularity, but there does seem to be a difference that makes its way into scientific practice. Fractals are used extensively in attempts to understand the stochastic nature of markets, and have been commercialized in line with that expectation. 

The connections between fractals, efficient markets, and to a lesser extent chaotic behavior is exemplified in books such as:  Peters, E. (1994). Fractal Market Analysis. Wiley.

October 24, 2024

OAWeek 2024: Intrinsic and Extrinsic Approaches to Open Access

This post is in celebration of Open Access (OA) Week 2024. The theme for this year is "Community over Commercialization". 

How do we incentivize people to adopt Open Access practices? We can take lessons from motivational Psychology to think about routes to better practice. Before doing so, we need to consider the current (and often sorry state) of open access.

It could be argued that in some important ways, Open Access has failed. The system of access to academic goods as currently structured is built on significant benefits to publishers and costs to libraries and authors. This benefit has been accrued by publishers due to reputational benefits: being published in Nature, Science, or Cell is highly prestigious. Yet the benefits of this prestige are necessarily limited to a few groups with lots of resources. And the beneficial attributes of open access have been captured by commercial entities. Similar problems plague the open-source community, a shift to the open ethos is the only way out.  

The different types of open access also play different roles in the marketplace of academic goods. Green open access, or self-archiving artifacts, are community goods. While this can be susceptible to the tragedy of the commons, proper social investment can ameliorate maintenance and growth imperatives. This is often seen as the highest standard for open access but requires community investment. Building a sustainable infrastructure of preprints, open peer review, and overlay journals has been elusive.

The Economic Benefits and Costs of Different Colored Access


Black open access using tools such as Sci-Hub is considered piracy (hence the "black" label). From an economic perspective, piracy is symptomatic of a dysfunctional market. Indeed, part of open access' failure is due to the dominant position of publishers and their own economic imperatives. In fact, black open access can be considered a rational response to closing access to article in a research culture of sharing and finding alternate routes to success [1-3]. 

To focus more on the publisher's advantage, and the failure point of open access more generally, is the current state of Gold, Diamond, and Platinum open access. Gold open access involves payment of an APC (Article Processing Charges) fee to the publisher. This often reduces the burdens on libraries, as they previously paid excessive subscription fees. This is because APCs actually increase the burden on individual authors, with disappointing results on the prestige economy. Without market power for the authors (or home institutions), there is no incentive to build Diamond and Platinum access systems. In such systems, no APC fee is paid, and we get the prestige that people seek. One barrier to this is shifting the burden back to publishers, but with proper management of community resources it is the least bad option.

Up to this point, I have been speaking in economically coded language. Without thinking about various motivations, however, we cannot fully understand ways to move forward. Let's think about various intrinsic/extrinsic motivations of authors and their institutions to reclaim open access. Intrinsic motivations are properties of individual cognition, while extrinsic motivations are things that motivate individual behaviors from the outside world.

Intrinsic motivations

There are many intrinsic motivations that drive acceptance and adoption of open access. But there are many that do not, and these motivations often come into tension. Positive drivers include striving for a better community, an imperative for sharing results with the community, the ability to provide different platforms for scientific communication (datasets, hypotheses, theory, out-of-scope studies), and recognition for unsung components of the scientific process (such as technical reports or negative results). Negative drivers include a need to satiate cultural traditions, an inability to convey prestige through open means, a conflation of open access with fraud and low-quality work, and an inability to meet the quality needed to do open access successfully.  

Extrinsic motivations

The multitude extrinsic motivations include institutional support, the need for career promotion, community rewards and prestige, the pressure for cost savings, and technological ease of adoption. These can be a mix of positive and negative drivers that make adoption of open access hard to justify. Interactions with open-source software can also drive open access adoption, as the commitment needed to develop shared data and code can be easily extended to other academic artifacts.

What is the path forward? 

Sometimes considering motivations are not enough, and the community is much pettier and more irrational than we like to admit. It is worth thinking about eLife's model in open peer review, which in part lead to a backlash against the editorial staff [4, 5] by less sympathetic members of the scientific community (and barely-disguised corporate interests). Part of this is a disagreement about open strategies, but this is also about the gatekeeping nature the scientific community itself. The eLife model allows for papers to be preprinted, and then peer reviewed. The paper remains live on eLife's website even if the reviews recommend rejection (although the rejection is noted) [6, 7]. This is not novel amongst open peer review platforms but has rankled the more hierarchically oriented members of the scientific community. Perhaps we need to also consider "irrational management strategies", or what intrinsic motivations drive decisions that favor obsolete conventions.


References:

[1] Melvin et.al (2020). Communicating and disseminating research findings to study participants: Formative assessment of participant and researcher expectations and preferences. Journal of Clinical and Translational Science, 4(3), 233–242.

[2] Casci and Adams (2020). Research Culture: Setting the right tone. eLife, 9, e55543.

[3] Nosek et.al (2015). Promoting an open research culture. Science, 348(6242), 1422-1425.

[4] eLife latest in string of major journals put on hold from Web of Science. RetractionWatch. https://retractionwatch.com/2024/10/24/elife-latest-in-string-of-major-journals-put-on-hold-from-web-of-science/

[5] Abbot (2023). Strife at eLife: inside a journal’s quest to upend science publishing. Nature News, March 17. https://www.nature.com/articles/d41586-023-00831-6

[6] F1000 Staff (2022). Open peer review: establishing quality. March 7.  https://www.f1000.com/blog/peer-review-establishing-quality

[7] McCallum et.al (2021). OpenReview NeurIPS 2021 Summary Report. https://docs.openreview.net/reports/conferences/openreview-neurips-2021-summary-report

September 21, 2017

An Infographical Survey of the Bitcoin Landscape


Josh Wardini sent me information on a new Bitcoin infographic that serves as a survey of events over the last 10 years in the world of Bitcoin development and legal regulation. Many interesting factoids in this graphic, some of which were unbeknownst to me. In the next few paragraphs, I will discuss my impressions that are brought to bear by each subset of factoids.




The relationship between blockchain and mining is an interesting one, and underscores the power of blockchain as both a data structure and a secure transaction system. Bitcoin is also its own economic system, complete with social interactions. In particular, the competitive and cooperative aspects of cryptocurrency can serve as a model for understanding the social structure of markets.







This is another interesting feature of bitcoin: the network has computational power to both unlock the value of existing blockchain as well as to create new currency. Bitcoin mining has always been a bit of a black box to me [1], but it seems as though it has potentially two roles in the bitcoin economy. In a Synthetic Daisies post from 2014, I mentioned that the supply of bitcoin is fixed (in the manner of a precious metals supply), but it turns out that it is not that simple. Of course, since then blockchain technology has become the latest hot emerging technology in a number of areas unrelated to Bitcoin and even the digital economy [2].



It turns out the computational systems (unlike people) is not all that hard to understand. However, digital currency, which is based on human systems, is much harder to understand (or at least fully appreciate). In 2013, I did a brief Synthetic Daisies mention of a flash crash on one of the main Bitcoin exchanges. There is a lot of opportunity to use blockchain and even perhaps cryptocurrency in the world of research. If ways are found to make these technologies more easily scalable, then they might be applied to many research problems involving human social systems [3].


NOTES:
[1] So I sought out a few introductory materials on Bitcoin mining to clarify what I did not know: 

a) startbitcoin (2016). Beginner's Guide to Mining Bitcoins. 99 Bitcoins blog, July 1.

* mining consists of discovery blocks in the blockchain data structure, the discovery of which is rewarded through a "bounty" of x bitcoins. From there, inequality emerges (or not).

b) Mining page. Bitcoin Wiki.

* the total number of blocks is agreed to by the community, as is the total amount of computational power of the network. This makes the monetary supply nominally fixed, but is not required by the technology.

c) Hashcash Algorithm page. Bitcoin Wiki.

Despite the clear metaphoric overtones, Bitcoin mining is essentially like breaking encryption in that it requires a massive amount of computing power thrown at a computationally hard problem, but is also has elements of an artificial life model (e.g. competition for blockchain elements).

Water-cooled rigs probably maximize your investment margin....

[2] Of course, there has been innovation in the use of blockchain for Bitcoin and more general cryptocurrency transactions. For more, please see:

Portegys, T.E. (2017). Coinspermia: a cryptocurrency unchained. Working Paper, ResearchGate, doi:10.13140/RG.2.2.33317.91360.

Brock, A. (2016). Beyond Blockchain: simple scalable cryptocurrencies. Metacurrency project blog, March 31.

[3] A few potential examples:

a) Data Management. 1  2




July 2, 2017

Excellence vs. Relevance

Impetus for this blog post. Twitter h/t to Alex Lancaster (@LancasterSci).

In academia, the term excellence is often used in the context of scarcity and competitive dynamics (e.g. publications, career promotion), and as a result can be used quite arbitrarily [1]. In [1], a distinction is made between excellence and soundness. Excellence is seen as a subjective concept, while soundness (enabled through completeness, thoroughness, and an emphasis on reproducibility) is the adherence to clearly defined and practiced research standards. While it may also be true that the concept of soundness can suffer from the same subjective limitations, it is probably an improvement over the current discussions surrounding excellence.

Another term we rarely refer to, but may be of even greater importance, is the relevance of scientific research. In a previous post, I brought relevance theory to bear on potential biases in scientific selectivity. One way to think of relevance is as the collective attentional focus of a given research group, community, or field. Collective attention (and thus relevance) can change with time: papers, methods, and influences rise and fall as research ideas are executed and new information is encountered [2]. As such, relevance defines the scope of scientific research that defines a particular field or community of researchers. Given a particular focus, what is relevant defines what is excellent. In this case, we return to the biases inherent in excellence, but this time with a framework for understanding what it means in a given context.

There is also an interesting relationship between soundness and relevance. For example, the stated goal of venues like PLoS One and Scientific Reports is to evaluate manuscripts based on methodological soundness rather than merely on field-specific relevance. To some extent this has eliminated issues of arbitrary selectivity, yet reviewers and editors from various fields may still surruptitiously impose their own field-specific conventions to the review process. Interestingly, soundness itself can be a matter of relevance, as the use of specific methodologies and modes of investigation can be highly field-specific.

Sometimes relevance is a matching problem between an individual researcher and the conventions of a specific field. Relevance can be represented as a formalized conceptual problem using skillset geometries [3]. In the example below, I have shown how the relevance of a specific individual overlaps with what is considered relevant in a specific field. In this case, the researcher has expertise in multiple areas of expertise, while the field is deeply rooted in a single domain of knowledge. The area of overlap, or Area of Mutual Relevance, describes the degree of shared relevance between individual and community (sometimes called "fit").


How relevant is a single person's skillset in the context of a research community, and how do we leverage this expertise in an inclusive manner? The mutual relevance criterion might provide opportunities in cases where there seems to be a "lack of fit". Understanding the role of collective attention within research communities might allow us to consider how this affects both the flow of new ideas between fields and the successful practice of interdisciplinarity.


NOTES:
[1] Moore, S., Neylon, C., Eve, M.P., O'Donnell, D.P., and Pattinson, D. (2016). Excellence R Us: university research and the fetishisation of excellence. Palgrave Communications, 3, 16105. doi:10.1057/palcomms.2016.105.

[2] Wu, F. and Huberman, B.A. (2007). Novelty and collective attention. PNAS, 104(45), 17599-17601. doi: 10.1073/pnas.0704916104.

[3] First introduced in: Alicea, B. (2017). A peripheral Darwin Day post, but centrality in his collaboration graph. Synthetic Daisies blog, February 16.

January 10, 2017

How to Kick-Start a Crypto-Currency

Here is an infographic (see below) I received from interested reader Steve Rogen, which follows up on a critique of Bitcoin I published back in 2014). He pointed me to a blogpost by Dinar Durham (a Financial Tech startup) explaining the concept of an initial coin offering (ICO). 

An ICO is a way for a new crypto-currency to distribute its coinage across a broader number of users than the more standard Bitcoin approach, and eliminates severe favoritism towards early adopters. The infographic itself demonstrates the process of public offering for a new coin. 

According to Dinar Durham's blogpost, ICOs have a mixed track record of success; while some are successful, others are not. However, they are becoming more popular as the number of altcoin types increases. 



October 22, 2015

Arriving at October 21, 2015...... and beyond

Last year I marked the date, and this year it became a "thing" (at east on the internet). So here are a few links to celebrate the famous date from the "Back to the Future" trilogy.

Billings, L.   Time Travel Simulation Resolves "Grandfather Paradox". Scientific American, September 2 (2015).

"What 'Back to the Future, Part II' Got Wrong (and right)", from the University of Illinois, Urbana-Champaign.


A welcome to the future, from Doc Brown himself:

COURTESY: Universal Studios.

And now..... a bit farther into the future...... The Economics of Star Trek, which is a really active area of internet scholarship:

Transcript of the recent New York ComicCon panel on Trekonomics.

Podcast on the "Economics of Star Trek", courtesy of FW: Thinking.

A few other takes on the Star Trek economy from Noahpinion, Joseph Dickerson, and Slate.

In the future, Spock is on the money. COURTESY: Rick Webb, The Economics of Star Trek: the proto-post scarcity economy.

September 14, 2015

Hodgepodge Blogpost, September 2015

Welcome to the blogging hodgepodge for this month. I wanted to clear up by reading queue, and present some of these ideas and articles in an entertaining way. The topics include: modeling, significant results, and hidden variables (but perhaps not discussed in a conventional manner). As a bonus, we get career advice for scientific researchers and relevant discussion.

Mutant phenptypes from the Fukushima area of Japan. COURTESY: National Geographic.


Flawed Models Cannot Be Made Idealistic

"Essentially, all models are wrong, but some are useful" -- George Box. What makes for a bad model? Poor assumptions, oversimplication/vagueness, or underfitting with respect to available data? These articles address some of these issues, with particular relevance to societal consequences.

Kirchner, L.   When Big Data Becomes Bad. ProPublica, September 2 (2015).

O'Neil, C.   Big Data, Disparate Impact, and the Neoliberal Mindset. Mathbabe blog, September 7 (2015).

Schuster, P.   Models: From Exploration to Prediction -- Bad Reputation of Modeling in Some Disciplines Results from Nebulous Goals. Complexity, doi:10.1002/cplx.21729 (2015).

Rickert, J.   How do you know if your model is going to work? Part 2: Intraining set measures. R-bloggers, September 8 (2015).


Once upon a time, this was a viable model of how nature worked. COURTESY: Geocentric Model, Redorbit.


The Real World is Complex, Idealized Methods Notwithstanding

The debate over replicability in Psychology (and by extension sciences that are not particle physics) rages on. This month, a shot was fired from the "Psychology is not very replicable" camp. The Open Science Collaboration published a paper in Science showing that many replications of experiments fail to reproduce the same levels of statistical significance and power as the original studies.

Critics have blamed this lack of replicability on a number of culprits, including shortcomings of the NHST approach itself. Two potential culprits I have pointed to previously include complexity and cultural context, the latter which we will return to in a bit.



What explains these replicated results? . COURTESY: Figure 1, Science, 349, doi:10.1126/science.aac4716 (2015) AND Loria, TechInsider.

Open Science Collaboration.   Estimating the reproducibility of psychological science. Science, doi:10.1126/science.aac4716 (2015).

Loria, K.   Everything that's wrong with psychology studies in 2 simple charts. TechInsider, August 28 (2015).

Barrett, L.F.   Psychology is not in Crisis. NYTimes Opinion, September 1 (2015).


The Unreasonable Effectiveness of Cultural Context*

* a play on: Wigner, E.   The Unreasonable Effectiveness of Mathematics in the Natural Sciences.


Vanderbilt, T.   Why Futurism Has a Cultural Blindspot. We predicted cell phones, but not women in the workplace. Nautil.us blog, September 10 (2015).

* the latest critique of futurism, this time from a sociological perspective.



Yau, N.   Bourdieu’s Food Space chart, from fast food to French Laundry. Flowing Data blog, June
21 (2012).

* our contemporary Economic World, according to Pierre Bourdieu (as told by Leigh Wells).


Career Advice (Not Avarice):

Hossenfelder, S.   How to publish your first scientific paper. Backreaction blog, September 11 (2015).

* this blog post not only provides advice on how to get started as a published researcher, but also gives advice on how to formulate research ideas and structure manuscripts that will garner the interest of editors and reviewers.

Curry, S.   Peer review, preprints and the speed of science. Guardian, September 7 (2015).

* yet another article in favor of the open-science movement, in this case advocating for mechanisms (e.g. preprint servers, open peer review) that have the potential to speed up and otherwise improve the research enterprise.

McDonnell, J.J.   Creating a Research Brand. Science, 349, 758 (2015).

This author uses a marketing metaphor to help imporive the efficiency of a researcher's efforts. The advice bolis down to the following:

* promote results, publications, and lectures all around a central theme.

* find the right breadth of research. This should be greater than a hyper-specialized topic, but narrow enough to constitute a unique niche.

December 18, 2014

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).

December 4, 2014

Tales of an Academic Start-Up (Orthogonal Research)

This content is cross-posted to my personal website reboot. Previous updates and allusions to Orthogonal Research can be found (in chronological order) here, here, here, here, and here.

Myself, Represented Orthogonally
From January 2014 to November 2014, my primary affiliation was as Primary Investigator at Orthogonal Research [1]. Upon running out of funding at my previous position (Cellular Reprogramming Lab at Michigan State University), I decided to formally found an academic start-up. The idea was to have an affiliation and continue doing research while looking for a more formal position. However, with an open mind, an interest in research innovation, and some background in research commercialization, I decided to turn this into a more formal opportunity. I managed to turn out several papers, become a formal grant-making institute, and begin a long-term, large-scale distributed collaboration (DevoWorm). In fact, this collaboration led to my current position at UIUC.


While I now have a formal position, I am retaining the organizational identity as a secondary affiliation (much as some academics are "Chief Science Officers" at a start-up). I generated a number of reports to document the activities of an "academic start-up". This was stylized in the form of Quarterly Reports, which were based on three month intervals of the calendar year and summarized my academic activities during the period in question. These reports can be downloaded here: Q1, Q2, Q3, Q4 (coming soon).

Why call it a "Start-up"?
Why do I call it an academic start-up? Three things distinguish this from a more traditional start-up: fundraising, non-tangible vs. tangible outcomes, and business plan. While regular start-ups have multiple rounds of fundraising, the academic start-up is a bit more flexible. We can use cross-subsidization, grant money, and crowdsourcing [2] as a starting point. It is harder to fundraise when you have many non-tangible outcomes. The quarterly reports stress the tangible outcomes (papers, presentations, peer reviewing). However, academic work (particularly of the scientific variety) involves significant learning, internalization, experimentation, and reflection. 

Because of this flexibility and departure from conventional business thinking, the academic start-up does not operate by means of a formal business plan. In the case of Orthogonal, a path forward was made through exploration, tying up the loose ends of previous research, and working with collaborators to generate new productivity. In a sense, an academic start-up is a lot like a creative web endeavor such as programming a YouTube channel or administering blog content (which is part of the Orthogonal Research portfolio).

The workflow and productivity of an academic start-up, enabled by information technology.

Things I learned from this experience
1. There are significant economic "barriers to entry" in the field of research. As I was aware when starting Orthogonal, the University provides much of this infrastructure at significantly reduced cost. A University affiliation is to benefit from numbers (of fellow researchers) and (institutional) prestige. Orthogonal Research occupies a space in which the barriers to entry have fallen enough to do research at a very low cost (e.g. secondary analyses, modeling and simulation, collaborations, theory-building).

2. There is a need to combine academic start-ups with alternative funding methods [3], particularly for researchers who and projects that do not fit in well at more traditional academic institutions. Alternately, an academic start-up can be used as an auxiliary affiliation, to conduct work and collaborations outside the scope of one's Uni affiliation. In any case, this might serve the research workforce and local Universities well, much as research parks can have a synergistic effect with the University. While not everyone will agree that such partnerships are a good development, the small-scale nature of my initiative provides an avenue for high-risk or hybrid projects. Particularly when cross-subsidized, academic start-up might actually catalyze research innovation by not forcing research to conform to any single model.

3. What happens when a large number of independent researchers all have their own organizations, whether or not it is registered as a LLC? They can do things such as share equipment and resources, broker agreements with the local Uni, and provide collaborative support to each other. Here are two examples of how this has become more common:

a. one of my long-term collaborators (Dr. Steven Suhr) is in the process of starting up a biotech/ gene construct business called Biomilab LLC. Part of the purpose for this is to match an impressive skill set with resources that are outside the scope of traditional Uni research opportunities and funding regimes. While he is independent, he also collaborates with with University-affiliated people.

b. in the DevoWorm project, none of the collaborators (excluding myself) have a formal academic affiliation beyond graduate student. One is affiliated with a start-up called MetaCell, LLC, another is the proprietor of New Light Industries, Ltd., and a third is retired from academia but affiliated with Gulf Specimen Marine Lab (non-profit educational institute) in Florida.

NOTES:
[1] "Orthogonal" means "at right angles", "perpendicular", or alternately, "statistical independence". I chose this name to reflect the diverse research I have been fortunate enough to engage in over the years (a fair amount of it in the areas of mathematical and computational modeling). While my research work typically represents an interdisciplinary synthesis, there is always the feeling that the different components are "perpendicular" or "statistically independent" to one another. There is also referential meaning in the oddly- defined relationship between "Independent" and "Affiliated" research.

[2] My feelings about crowdsourcing are mixed. On the one hand, it is a good way to raise money quickly, and it is catching on amongst researchers. On the other hand, there is a tangibility (or hipster) bias, and (at least to me) resembles an inbred capital market (see below).

Diagram of an inbred capital market, or, “why do seemingly ridiculous Kickstarter projects raise large sums of money". Distinct from the phenomena of large corporate entities using the idle time of social groups for fun and profit.

[3] My forays into alternative funding methods have been largely theoretical. However, check out my stock presentation on the topic (from January 2013, so it might be time for a revision). It is called "A New Route to Science Innovation", and lays out the visionary case for the why and how with respect to the alternative funding of science research.

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