The Way of The Polymath

This content is cross-posted to the Orthogonal Research and Education Lab Medium.

What constitutes a polymath, and why are they so rare? Another way to ask this question is why are there so few foxes relative to hedgehogs? The occasional hyper-specialist would have you believe the term "polymath" is an epithet. However, there are a number of skills that the polymath possesses that translate into an advantage for advancing both theory and fundamental knowledge. Aside from the mastery of multiple intellectual areas, the most important of these is the ability to synthesize information from a number of sources. The advent of digital scholarship may enable this ability in the foxes among us [1].

One depiction (late 19th, early 20th century) of a polymath.

Back in 2015, Nature Careers released a list of recommendations to combat the hyper-specialist tendencies of PhD programs [2], but many of these are simply window-dressing. One view is that improving the state of interdisciplinary thinking is to improve the infrastructure for collaboration and disseminating big ideas. However, a more fundamental (and harder-to- implement) change that can be made is to reconfigure the epistemic landscape of science [3]. One aspect of this indeed involves the training of scientific generalists, but generalist training does not equate polymathism.


The other factor involves the potential zero-sum nature of generalized knowledge [4]. There is a constant tradeoff between deep expertise in one area versus more shallow expertise in a number of areas simultaneously. Society tends to reward deep expertise, and synthesis is rather expensive knowledge-wise. In any case, there is a game-theoretic interpretation of this scenario, but that is a topic for another post.  

Here is a semi-annotated reading list on singular (but multidisciplinary) academic activity:

Hossenfelder, S.   The loneliness of my notepad. Backreaction blog, July 8 (2015).


Issacson, W.   Myth of the Lone Genius. Aspen Journal of Ideas, July 24 (2015). 

These articles critically examine the myth of the lone genius. The first article points out that tools enabling collaboration (e.g. internet, large-scale consortia) are finally starting to bear fruit. Proportion of single-author papers has gone down over last 20-30 years, but that does not mean lone efforts are in absolute decline. In fact, "isolation" is a myth, given the social networks and information-sharing culture in academia.


Bateman, T.S. and Hess, A.M.   Different personal propensities among scientists relate to deeper vs. broader knowledge contributions. PNAS, 112(12), 3653-3658. (2015).


Kirkegaard, E.   Personality correlates of breadth vs. depth of research scholarship. Project Polymath blog, March 6 (2015).

* relates style of scientific investigation to type of contributions (specialized studies vs. broad interdisciplinary synthesis) made by scientists. Survey methodology does not assume that contributions can be both deep and broad, despite setting this up as a dichotomy. Is "deeper" vs. "broader" a major dichotomy in scientific exploration.

* suggests that the difference between scientific generalists and specialists is epistemic, not economic as traditionally assumed (e.g. a certain strategy is more or less risky).

* views differences in types of scientists (e.g. polymathy) as a matter of personality, not epistemic bias.


Palla, G., Tibely, G., Mones, E., Pollner, P., and Vicsek, T.   Hierarchical networks of scientific journals. arXiv, 1506.05661 (2015).

Presents a hierarchical network analysis of scientific journals and their relevance to measuring influence and the diffusion of ideas in specific scientific fields.


Muldoon, R. and Weisberg, M.  Robustness and idealization in models of cognitive labor. Synthese, 183, 161-174 (2011).

* introduce us to an economic optimization model called the marginal contribution/reward (MCR).

* motivation of individuals or groups of scientists is accomplished either through self-interest or epistemic norms.

* MCR assumes that cognitive labor can be optimally distributed across collaborations to solve hard problems. The problem is stated as a constrained maximization of "success" and "return". Model does not provide good approximations of success, return, or epistemic norms, not does it distinguish amongst different scientific skill-sets (generalists vs. specialists vs. hyper-specialists).


Sarma, G.P.   Should we train scientific generalists? arXiv, 1410.4422 (2014).

* how to introduce students to a vocabulary of multiple disciplines, and how this would encourage research breadth.

alexarje   Disciplinarities: intra, cross, multi, inter, trans. Alexander Refsum Jensenius blog, March 12. (2012).

NOTES:
[1] Alicea, B.   "Academic Connectivity and the Future of Scientific Ideas". Synthetic Daisies blog, September 9 (2011).

[2] Nurse, P.   To build a scientist. Nature, 523, 371-373 (2015).

[3] Weisberg, M. and Muldoon, R.   Epistemic Landscapes and the Division of Cognitive Labor. Philosophy of Science, 76(2), 225-252 (2009).

[4] Downey, G.   Interdisciplinarity, sub-disciplinarity, and inter-topicality. Uncovering Information Labor blog, March 31 (2006).

Google Summer of Code Experience: Advice for Future Participants

 

As part of the Google Summer of Code program final project evaluations, I was asked to provide advice to future students and mentors in subsequent years. Here is the advice I gave:

Advice to Students:
I have three pieces of advice: do your research, reach outside of your comfort zone, and keep in communication with your mentor. Doing your research means that you continually reevaluate the big picture of your project. Start with a schematic of the workflow or project vision as outlined in your proposal. If you do not fully understand a set of issues (new algorithm, unfamiliar topical area), look them up or ask around.

Reaching outside of your comfort zone means you need to consider contingency plans for each step of your project in case something does not go as planned. If that requires a deep dive into a new method, then be willing to accommodate this into your project schedule. Do not spend too much time on learning new things, however, as the Summer moves pretty fast.

Finally, regular communication with your mentor on multiple channels helps with scheduling, project reevaluation, and keeping within expectations. I have used a combination of Slack, e-mail, and Google Meet with my mentees. Develop your own rhythm -- weekly Meet and e-mail updates can complement daily Slack communication. Don't be afraid to ask questions or take the initiative, but do be sure to coordinate this with your mentor's needs and expectations.

Advice to Mentors:
This year (2018), I mentored three students in two organizations. How did I do it? Weekly schedules, content management, and flexibility. Let's walk through this past summer to provide examples. The first thing I suggest is to mentor your students through the application process, so that their proposals align with your expectations and programmatic time constraints. During the community period, be sure to plan structured activities such as presentations to the broader community or access to background information on the organization and project.

As the coding period ramps up, be sure to set your weekly plan for communication. In my case, I scheduled a weekly meeting time (0.5 to 1 hour) for each student in Google Meet, set each student up in the appropriate Slack team, and prepared a weekly email newsletter (1-2 paragraphs) covering upcoming milestones. Also, be sure to utilize the link between Github issues and Waffle.io, as the latter serves to make outstanding tasks visually salient.

Our weekly meetings focused on four things:

1) what did you do this week/what will you do next week.
2) any outstanding issues/barriers to discuss in detail (or live demos).
3) upcoming milestones, planning for several weeks out.
4) review Waffle board issues, create new issues.

Finally, be flexible with respect to work style and meeting times. I had students from three different countries on two continents, and we often had to reschedule times. It is also important to let them adopt their own working rhythm (provided it is organized and within the bounds of the organization's needs). If they want to interact more or less on Slack, that is up to them. Just encourage them towards your community standards, and work from there. These things may seem like a lot to ask of a mentor, but I have found that it is worth it.

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.

Slate and the Solitary Ethnographic Diagram

While his style and message does not resonate with me at all, I've always thought that Donald Trump's speeches were highly-structured rhetoric. He seems to be using a form of intersubjective signaling [1] understood by a number of constituencies as communicating their values in an authentic manner. Specifically, the speeches have a sentence structure and cadence that can be differentiated from the literalism of contemporary mainstream society or more traditional forms of doublespeak ubiquitous in American politics.

This is why the most recent challenge from Slate Magazine was too good to pass up. The challenge (which has the feel of a Will Shortz challenge): diagram a passage from a Donald Trump speech given on July 21 in Sun City, South Carolina. The passage is as follows:

"Look, having nuclear—my uncle was a great professor and scientist and engineer, Dr. John Trump at MIT; good genes, very good genes, OK, very smart, the Wharton School of Finance, very good, very smart—you know, if you’re a conservative Republican, if I were a liberal, if, like, OK, if I ran as a liberal Democrat, they would say I’m one of the smartest people anywhere in the world—it’s true!—but when you’re a conservative Republican they try—oh, do they do a number—that’s why I always start off: Went to Wharton, was a good student, went there, went there, did this, built a fortune—you know I have to give my like credentials all the time, because we’re a little disadvantaged—but you look at the nuclear deal, the thing that really bothers me—it would have been so easy, and it’s not as important as these lives are (nuclear is powerful; my uncle explained that to me many, many years ago, the power and that was 35 years ago; he would explain the power of what’s going to happen and he was right—who would have thought?), but when you look at what’s going on with the four prisoners—now it used to be three, now it’s four—but when it was three and even now, I would have said it’s all in the messenger; fellas, and it is fellas because, you know, they don’t, they haven’t figured that the women are smarter right now than the men, so, you know, it’s gonna take them about another 150 years—but the Persians are great negotiators, the Iranians are great negotiators, so, and they, they just killed, they just killed us"

Okay, here you go -- an ethnographic-style diagram [2] based on one man, but perhaps instructive of an entire American subculture (click to enlarge). The diagram focuses on the relationship between John and Donald Trump (context-specific braintrust) and a specific worldview of power wielded through nuclear weapons, financial ability, and persuasion.

NOTES:

[1] In this case, intersubjective signaling could be used as a mechanism to reinforce group cohesion, particularly when the group's belief structure is defined by epistemic closure.

[2] Perceived lack of agency shown as red arcs terminated with a dot.

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.

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.

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).

Neo-proprietarians + Conceptual Obfuscation = To What End?

It's been an interesting/bizarre month with respect to the open source/open access ethos [see disclaimer in 1]. The first part of the month saw two events. One was the posthumous birthday of Aaron Schwartz, and the other was a social media kerfuffle that represents part of a more general conservative backlash to net neutrality [2]. More recently, an article in Nature [3] argued that increases in the number of published papers in recent years (partially enabled by open-access publishers), has diluted the quality-control process enforced through peer-review.

What do these three events have in common? They all involve brushes with neo-proprietarianism, or the advocacy of intellectual property (IP) rights through a simple assumption: private ownership/management of IP is always somehow morally superior to open access. This might range from overzealous prosecutors and politicians to earnest, well-meaning scientists. The result is oddly-placed criticism of ideas that are potentially more beneficial to society than to private owners or firms. At times, people with no direct stake in the IP rights defend the claims of rights holders, which seems odd except in the light of neo-propietarianism (or defending the mere idea of ownership rather than its drawbacks and consequences).

Open Access as Political Rhetoric: Alexis Ohanian's Ad Campaign in support of net neutrality.

In the case of both Aaron Schwartz and the aforementioned Nature article, the issue at hand is open access to scientific articles. The Nature article is well-meaning and raises some good points about how scientists might be compensated for work such as peer review. However, the tone and overall argument is reminiscent of another critique of open access publishing published last year in Science. Furthermore, there are some issues with making a link between publication quantity and the overall quality of the scientific literature. This is particularly true when the argument is made in a Nature article, lest it be interpreted as a conflict of interest [3].

Whether this is simply the act of conflating "the best of the best" with a restrictive paid-access model of publishing or an implicit argument for the infallibility of peer-review elitism is unclear. However, blog posts and data analyses by two biologists (one being Michael Eisen) and a bioinformatician [4, 5, 6] provide a very different (and more nuanced) view of the open-access journal phenomenon. This includes a rebuttal of the argument that the explosion of open-access journals is bad for the quality of the scientific literature and a burden on peer reviewers.

Sometimes quality control is a good thing. This paper was accepted by a so-called "predatory publisher" (maybe you stop bugging me for solicitations to your journal, maybe I won't embarrass you) . In discussions that revolve around publishing "quality" and "prestige", it is common to lump predatory publishers in with all other open-access publishers. Do you need an elite publishing model to prevent things like this from happening? (Answer: a rhetorical "no"). COURTESY: David Mazieres, Eddie Kohler, and Peter Vamplew.

For more reflection on the life of Aaron Schwartz, see this event hosted by the Electronic Freedom Foundation (EFF) called "Hacking for a Better World". And for some less ideonational views on net neutrality, see the two readings on the subject in [7]. Should November be known henceforth as "Open Information Month". In light of this year's events, perhaps.

NOTES:
[1] For purposes of this post, no finer distinctions will be made between true (e.g. the technical definition of) "open access" and Net Neutrality. Particularly as defined by the current debate, Net Neutrality is not equivalent to "open access".

However, for some recent thinking in this area, please see: Godwin, M.   How Wikipedia Zero will serve and promote network neutrality. Mike Godwin's LinkedIn blog, December 1 (2014).

[2] Pendleton, A. and Lannon, B.   One group dominates the second round of net neutrality comments. Sunlight Foundation, December 16 (2014).

[3] Arns, M.   Open accss is tiring out peer reviewers. Nature, 515, 467 (2014).

[4] Taylor, M.   Open-access megajournals reduce the peer-review burden. Sauropod Vertebra Picture of the Week blog, November 27 (2014).

[5] Eisen, M.   Contrary to what you read in Nature, Open Access has not caused the growth in science publishing. It is NOT Junk blog, November 27 (2014).

[6] Saunders, N.   Growth in free and closed scientific publications 2000-2013. Neil Saunders' Rstudio Notebook, November 28 (2014).

[7] Madrigal, A.C. and LaFrance, A.   Net Neutrality: A Guide to (and History of) a Contested Idea. The Atlantic, April 25 (2014) AND Bergstein, B.   Q&A: Lawrence Lessig. MIT Technology Review, October 27 (2014).

Heuristic Haystacks and the Messy Lesson

As an admitted and self-styled parsimony skeptic, I was interested to see a discussion in the blogosphere on the seductive allure of simple explanations [1]. This was in the context of economic policy and decision-making, with Paul Krugman even offering an H.L. Mencken quote: "For every complex problem there is an answer that is clear, simple, and wrong" [2]. Yet while parsimony was never brought up, I suspect that hypotheses and arguments related to the efficient markets hypothesis were always somewhat in mind.

There are, of course, broader parallels between seductive simplicity and parsimony. As I have pointed out before, I find parsimony to be a overly-seductive null model [3]. The simplest explanation often leads us not to the truth, but to what is most conceptually consistent. In some cases (where theory is well-established) this works out well. Intuition in support of serendipity and serendipity in support of discovery is an unassuming (and often underplayed) pillar of science [4]. However, in cases where our intuitions get in the way of objective analysis, this becomes problematic. And this seeming exception is actually quite common. In a related manner, this brings up an interesting problem of the relationship between parsimony as a decision-making criterion and the epistomology of a scientific phenomenon.

An appalling lack of faith in both Occam's and Einstein's worldviews. More horrifying details in my Ignite! talk on the topic.

This relationship, or more accurately inconsistency, is due to argumentatively-influenced judgments on a naturalistic search space. Even in children, it is observed that argumentation is rife with confirmation bias and logically arguing to absurd positions [5]. While argumentation allows us to build hypotheses, it also gets us stuck in a conceptual minimum (my own ad-hoc phrase). In a previous post, I pointed to recent work on how belief systems and associated systems of argumentation can shape our perception of reality. But, of course, this cannot will the natural world to our liking. In fact, it often serves to muddy the conceptual and theoretical waters [6]. Therefore, you often have a conceptual gap unrelated to problem incompleteness which we will flesh out in the rest of this post.

The first point to be made here is that such an inconsistency introduces two biases that shape how we think about the simplest explanation, and more generally about what is optimal. First of all, can we even find the true simplest explanation? Perhaps the simplest possible statement that can be constructed cannot capture the true complexity of a given situation. This is particularly true when there are competing dimensions (or layers or levels) of complexity. Secondly, and particularly in the face of complexity, simplicity can often be a foil to deep understanding. Unfortunately, this is often conceptualized of and practiced upon in a destructive way, favoring simple and homogeneous mental models over more subtle ones.

How to dream of complex sheep....

In the parlance of decision-making theory, parsimony is consistent with the notion of good-enough heuristics. In the work of Gigerenzer [7], such heuristics are claimed to be nearly optimal when compared to formal analysis of a problem. This can also be seen with statistical prediction rules that outperform human judgments in a number of everyday contexts [8]. But is this a statement of problem "wickedness", or a statement of superiority with respect to human cognition? When compared to problems that require needle in a haystack criteria, fast and frugal heuristics (and hence parsimony) is severely lacking.

So complexity introduces a secondary bias at best and serves as a severe limitation to achieving parsimony at worst. One might expect that experimentally verifying a prediction made in conjunction with Occam's Razor requires finding an exact analytical solution. Finding this proverbial "needle in a haystack" requires both a multi-criterion, algorithmically-friendly heuristic solution in addition to a formal strategy that often defies intuition. Seemingly, the simple solution cannot keep up.

I found it! It was quick, but I was also quite lucky,

NOTES:
[1] The Simplicity Paradox. Stumbling and Mumbling blog, September 9 (2014) AND Krugman, P.   Simply Unacceptable. The Conscience of a Liberal blog, September 5 (2014).

[2] This is not to equate parsimony with methodological snake oil -- in fact, I am arguing quite the opposite. But I am merely pointing out that parsimony is an incomplete hypothesis for acquiring knowledge.

[3] For more, please see this Synthetic Daisies post: Alicea, B.   Argument from Non-Optimality: what does it mean to be optimal? Synthetic Daisies blog, July 28 (2013).

[4] Kantorovich, A.   Scientific Discovery: Logic and Tinkering. SUNY Press, Albany (1993).

[5] I say "even" in children even though the latter (logically arguing to absurd conclusions) is often expected from children. But we see these things in adults as well, and such is the point of argumentation theory. For more, please see: Mercier, H.   Reasoning Serves Argumentation in Children. Cognitive Development, 26(3), 177–191 (2011).

[6] Wolchover, N.   Is Nature Unnatural? Quanta Magazine, May 24 (2013).

[7] While there are likely other (and perhaps better) examples, I am using a reference cited in [1]: Gigerenzer, G.   Bounded and Rational. In "Contemporary Debates in Cognitive Science", R.J. Stainton eds. Blackwell, Oxford, UK (2006).

[8] lukeprog   Statistical Prediction Rules Out-Perform Expert Human Judgments. LessWrong blog, January 18 (2011).

Fireside Science: Logical Fallacy vs. Logical Fallacy

This content is cross-posted to Fireside Science. To get the most out of this post, please review the following materials:

Alicea, B.   Informed Intuition > Pure Logic, Reason + No Information = Fallacy? Synthetic Daisies blog, January 4 (2014).

The Skeptics Guide to the Universe.

The peer-review committee for pure rationality. For more, please see [1].

Awhile back, I posted some critiques of and modifications to the conventional approach to logical fallacies [1] here on Synthetic Daisies. It seems as though every debate of the issues on the internet involves an accusation that one side is engaging in some sort of "fallacy". This is especially true of topics of broader societal relevance, where the notion of logical fallacies has become entangled with denialism [2] and epistemic closure [3].

Social Media argumentation, one person's take.

To recap (full version of the post here), I proposed that we replace six fallacies on the chart above and replace them with seven fallacies that are more inclusive of moral (e.g. emotional) and cultural biases. To me, the "Skeptic's Guide to the Universe" model feels like a 12-step program of rationality. It may help you think in a desirable way (e.g. pure rationality). However, pure rationality does not provide you with a means to place conditions on an objective argument. The triumph of logical rigor ultimately becomes a straight-jacket of the mind, reducing one's ability to think situationally.

Are the arbiters of deduction wrong on six counts?

Now it appears that I'm not alone in my concerns. Big Think now has a theme "The Fallacy Fallacy" on the fallacies of logical fallacies [4], with contributions from Alex Berezow, Julia Galef, Daniel Honan, and James Lawrence Powell. 

In this collection of essays and interviews, the overuse of logical fallacies itself is cited as a fallacy of composition, and provides better ways to construct arguments. These include several general observations related to the validity of reason itself. These transcend the popular "identify the fallacy" model.

One theme involves making the case for consensus through joint argumentation. Correct answers are not to be found via the most rigorous argument, but by exploring many complementary arguments, each with their own flaws.  

Another theme involves being mindful of cognitive biases such as confirmation bias or subconscious cultural preferences. Even when an argument is highly rigorous by the standards of logical consistency, they may still suffer from a lack of perspective. 

The third major theme involves the recognition that ignorance is a valid starting point [5] for many arguments. It is impossible to know everything about a topic, so any principled argument is bound to be incomplete. And the traditional fallacy model [6] is likely to make things worse.

NOTES:

[1] This is a list of 24 common logical fallacies, courtesy of Yourlogicalfallacyis.com (Jesse Richardson, Andy Smith, and Som Meadon). Also, most of these are individually found on Wikipedia with a more detailed explanation.

[2] Reinert, C.   Denialism vs. Skepticism. Institute for Ethics and Emerging Technologies blog, February 23 (2014).

[3] Cohen, P.   "Epistemic Closure"? Those are fighting words. NY Times Books, April 27 (2010).

[4] This is not a tautology! But it's not the same thing as the formal version of the fallacy fallacy (a.k.a. argumentum ad logicam).

[5] Contrast with: Argument from Ignorance. RationalWiki.

[6] A nice resource for better understanding all possible logical fallacies: The Fallacy-a-Day-Podcast. A fallacy a day, in readable and podcast form.

Informed Intuition > Pure Logic, Reason + No Information = Fallacy?

This content has been cross-posted to Tumbld Thoughts.

The peer-review committee for pure rationality. COURTESY: [1]

My notes on logical fallacies: perhaps they are not as bad as you think. People can make what are clearly errors in logic, and sometimes such fallacies are used as decision-making heuristics or cultural blends. This helps us make difficult decisions in the absence of information, or make sense of situations with little precedent.

This is like the 12-step program for skeptics and humanists (or those who aspire to these values). Much like 12-step programs, they leave a lot to be desired. These rules are largely naive of propagandist techniques, and the innate cognitive and cultural biases of their readers. The rhetoric argument does not fare with respect to this list. Fallacies on this list that I have an issue with:

1) Special pleading (and appeal to emotion): in cases where people fail to understand the context of a decision, special pleading might help to offset the damage done by a purely logical decision. Legal decisions that do not take in special cases (e.g. Grandfather clauses) are particularly of note.

2) Black-or-white: if decision-making were entirely deliberative (e.g. purely logical), we would never arrive at a decision. In this sense, decision-making must include a impulsive (or emotional) component. 

3) Ad hominem: while attacking the person rather than the argument is a convenient way to win an argument, this idea also assumes that people always argue in good faith and from a position of pure objectivity. This leaves no room for a theory of motivation, particularly when an argument has a thinly-veiled ulterior motive.

4) Slippery Slope: in cases of ambiguous moral or logical clarity, the slippery slope might actually help us clarify boundaries between one state and another. Without this boundary, human cognition is left without a reference point, which does not allow for clear (and culturally-relevant) decisions to be made.

5) Ambiguity: ambiguity is a necessary condition of a living argument. In cases where ambiguity is resolved, argument or belief/rule system becomes constricted. Allegorical arguments depend on ambiguity to remain relevant -- perhaps this is simply support for the ambiguity fallacy, but allegories are important devices in abducing (e.g. logical abduction) new logical relationships.

6) Strawman: "misrepresentation" of an argument is often in the eye of the beholder. People tend to extract heuristics in dealing with complex arguments, so it is hard to not construct a strawman (unless it is exceedingly flimsy, as with most intelligent design endeavors). 

Unless an argument is painstakingly recapitulated, any "elevator talk" length summary is bound to fail. And sometimes arguments are inherent to one's belief system -- in fact any criticism in this case could be viewed as a misrepresentation. In any case, strawman-type approaches can be used to set up improvements to an argument.

Not so fast, deduction fans......

Now here are some new fallacies that I have come up with. These are based on personal experience, both in human interactions and artificial intelligence. They are a bit more nuanced and specific than the fallacies presented in the "12-step program" model, but then again it speaks to some of my critiques above.

1) The "economic argument"/argument from efficiency: resource allocations that benefit me or my social group are superior, and can be extended to efficiency criteria. 

2) The correlative argument: things that co-occur are always significant. The problem is not discussed in the framework of complex, multivariate causality.

3) Argument from exemplar, normalization fallacy: similar to the correlative argument, but runs in the other direction. In this case, the argument is made from a single example.

In some cases, while the argument is made from extended observation, those observations do not map to the natural phenomenon well. Alternately, comparing phenomena that do not have the same underlying statistical distribution is an example of the normalization fallacy.

4) False consensus: consensus (meeting of the minds, political coalitions, peer-review) always puts you in a better place than where you started. A variant of the normalization fallacy, but involves the assumption that intellectual triangulation will solve any problem.

5) Argument from extreme relativism: when every culture is correct, no matter how morally repulsive the practice. This comes from a misunderstanding of cultural relativism: relativism is not about values, but about the intersubjectivity of cultural variants. In other words, these variants cannot be understood in isolation, only in the context of other practices.

6) Argument from moral superiority: an argument that is rooted in moral superiority (using partially or questionably factual information to intimidate). The goal of such an argument is to reform, prosletyze, or otherwise morally manipulate the intended target.

7) Highly-contingent statistic fallacy: statistics that are the most extreme in recorded history, or the first time a double play was turned in the 5th inning by a left-handed second baseman at night. The superlative is misleading, because the situation is either highly-artificial or not conducive to replication.

Different Ways of Explaining

To conclude, I will demonstrate how there are different ways of explaining. Is one specific type always superior, or is it context-dependent? Or do the abductive and deductive approaches have their own unique advantages?

Ghosts in the machine....

Who's better at explaining things, a novice with an interest and a creative mind, or an expert at really complex concepts [2]? Here, we have an example of the former (Bjork explaining how a TV works in three minutes) and the latter (Hiroshi Ishiguro and other robotics experts explaining the uncanny valley in one minute). And, of course, Spock can address both sides of the equation in one quick caption.

Robots and Humans. Theory of Mind but no context-dependence. Weird and Unnerving.

NOTES:

[1] This is a list of 24 common logical fallacies, courtesy of The Skeptics Guide to the Universe and Yourlogicalfallacyis.com (Jesse Richardson, Andy Smith, and Som Meadon). Also, most of these are individually found on Wikipedia with a more detailed explanation.

[2] For an interesting example of how readers of a magazine for statistics professionals explained the Monty Hall problem to a general audience: Reader's Challenge: the Monty Hall problem. Significance magazine, October, 32-33 (2013).