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).

Assorted Darwin Day Content

For this year's Darwin Day post, I will highlight a number of items I have recently run across on Twitter. Some of these have been retweeted on the Orthogonal Research and Education Lab Twitter feed, other materials are related to discussions in our research group meetings.

To start things off, I will draw your attention to a new special issue of Royal Society of London B called "Basal cognition: multicellularity, neurons and the cognitive lens" that is worth checking out. The term "basal" refers to evolutionary origins in the context of phylogeny (the tree of life). 

There are three papers of particular note for Darwin Day: "The chemical brain hypothesis for the origin of nervous systems" by Gaspar Jekely, "Elementary Nervous Systems" by Detlev Arendt, and "Uncovering cognitive similarities and differences, conservation and innovation" by Michael Levin and colleagues.

The new paper on elementary nervous systems in Royal Society B (click to enlarge, figure from paper). COURTESY: Detlev Arendt.

A pointer to the Darwin Online repository.

In terms of old drawings and other archival materials, check out the Darwin Online project. This is a nice repository of Darwin-related historical and scientific works. This resource contains books, personal correspondence, and published materials. Speaking of history, let's turn to the deep history of life.....

A billion years of continental drift as an animated gif. Click to enlarge.

This next feature is a new paper on a billion years of plate tectonic dynamics: "Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic" by Mike Tetley and colleagues. Now published in Earth Science Reviews, it is something we recently discussed in the weekly DevoWorm group meeting.

Following up on the DevoWorm discussion, which was about mapping the continental drift animation to the most basal branches of the tree of life, is an attempt to map Mammalian phylogeny [1] to continental drift over the past 225 million years. This was created by Carlos E. Alvarez. The numbers on the maps (top) correspond to the numbered clades (subtrees - bottom). This topic deserves a deeper dive into the latest Phylogeography research [2], which may be the subject of a future blog spot.

An attempt at matching up the tree of life with continental drift (click to enlarge). COURTESY: Carlos E. Alvarez

The next feature is a new paper on evolution of development (evo-devo) in nervous system anatomy called "Evolution of new cell types at the lateral neural border", now published in Current Topics in Developmental Biology. This study even uses converging evidence from genetic regulatory networks and anatomy to demonstrate common mechanisms shared between invertebrates and vertebrates.

A new paper on the evolution of new neuronal cell types (click to enlarge). COURTESY: Jan Stundl (Caltech).

Not only is this Darwin Day, but also the 50th anniversary of a Nature paper by Kimura and Ohta [3] on the Neutral Theory of Molecular Evolution. Neutral Theory postulates that most biological variation is expressed in selectively neutral genes, and so is random in nature [4]. This stands in opposition to the selectionist perspective of evolutionary change [5, 6].

Fully-tweetable neutral theory of evolution. COURTESY: Andrew J. Crawford.

Finally, and returning to neuroevolution, there are several items of interest from the laboratory of Cassandra Extavour. The first is a talk at the Society of Integrative and Comparative Biology meeting on the evo-devo-eco-neuro-biology of Drosophila learning and memory. For more evo-devo work from Dr. Extavour's lab, check out this recent work (with open data) on insect size and shape [7, 8].

Original artwork from SICB Twitter Account, commentary from Ken A. Field.

Hand-drawn notes on the SICB plenary talk. COURTESY: Dr. Ajna Rivera.

NOTES:

[1] Foley N.M., Springer M.S. and Teeling E.C. (2016). Mammal madness: is the mammal tree of life not yet resolved? Philosophical Transactions of the Royal Society B, 37120150140. doi:10.1098/ rstb.2015.0140.

[2] Avise, J.C. (2000). Phylogeography: the history and formation of species. Harvard University Press, Cambridge, MA.

[3] Kimura, M. and Ohta, T. (1971). Protein Polymorphism as a Phase of Molecular Evolution. Nature, 229, 467–469.

[4] Kimura, M. (1983). The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge, UK.

[5] Nei, M. (2005). Selectionism and Neutralism in Molecular Evolution. Molecular Biology and Evolution, 22(12), 2318–2342. doi:10.1093/molbev/msi242.

[6] There are other critiques of selectionism from other perspectives. Here is one in the area of brain function: Fernando, C., Szathmary, E., and Husbands, P. (2012). Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal. Frontiers in Computational Neuroscience, 6, 24. doi:10.3389/ fncom.2012.00024.

[7] Church, S.H., Donoughe, S., de Medeiros, B.A.S., and Extavour, C.G. (2019). Insect egg size and shape evolve with ecology but not developmental rate. Nature, 571, 58–62.

[8] Church, S.H., Donoughe, S., de Medeiros, B.A.S., and Extavour, C.G. (2019). A dataset of egg size and shape from more than 6,700 insect species. Scientific Data, 6, 104.

OREL and DevoWorm Review of 2020

As part of our preparations for the New Year, I prepared a set of presentations for my two research groups: Representational Brains and Phenotypes and DevoWorm. I have posted the slides below, and if you see something interesting that you would like to participate in, please contact me. If you are interested in learning more, please join the Orthogonal Research and Education Lab or OpenWorm Slack. You can also attend our weekly meetings: 3pm UTC Saturdays for Saturday Morning NeuroSim (more info), or 3pm UTC Mondays for DevoWorm (more info).

Saturday Morning NeuroSim presentation, with a focus on the Representational Brains and Phenotypes group (click slides to enlarge).

 

DevoWorm weekly meeting presentation, with a focus on the DevoWorm group  (click slides to enlarge).

Synthetic Daisies Summary for 2020

It has been awhile since I've done a readership post. But as 2020 comes to a close, let's do a Top 10 review of posts and pages for the past 12 months, ranked by readership. 

Top 10 posts for 2020 (by readership). Click to enlarge.

The Carnival of Evolution posts (#46 and #58) and "Playing the Long Game of Human Biological Variation" are the top three posts of all time. "Ratchets in Nature" is the only blog post to be formally cited (2 times according to Google Scholar). Readership seems to be down from years past. Only two of the top 10 posts for the year were actually written this year ("Welcome, Summer of Coders" and "Silver Linings of COVID19"). Another post that made the 11 spot (nearly making the list) was another post from 2020, the post on the ASAPBio Preprint Symposium from September. 

Post on the ASAPBio Session on the "Past, Present, and Future of Preprints" (Post #11 for 2020 by readership). Click to enlarge.

I have also posted a view of the top 10 pages viewed in 2020. The pages were created several years ago, and are not typically updated as much as I would like. The top view was on the "Hard to Define Events Workshop", a session from 2012 hosted at that year's Artificial Life conference. We never followed up on this workshop (it's been eight years!), but might be an interesting thing to create a virtual presence around. A meaning of the blog name, a list of favorite blogs, and various presentations round out the top 10. One more page of note is the "Popular Algorithmics" page, which is an accessible presentation of various algorithms. This is something that might be moved to the Synthetic Daisies Github organization as an open-source collection where people can contribute their own entries (a sort of Wikipedia of algorithms).

Top 10 pages for 2020 (by readership). Click to enlarge.

Aside from code and open-source content related to specific blog posts, the Synthetic Daisies Github organization also hosts the Synthetic Daisies meta-blog. This is meant to be a collection of more substantial content from the original blog, organized thematically and presented in a manner similar to an overlay journal. This includes featured posts and thematic collections, such as posts on Evolution or Models, Philosophy of Science, and Representation.

Check out the Synthetic Daisies meta-blog. Click to enlarge.

That's all for this year. Check us out in 2021 with a whole new set of posts!

Observer-dependent Models @ the Philosopher's Web Cafe

 

I gave a talk called "Observer-dependent Models" to the Philosopher's Web Cafe on December 11. I have made the slides available here, and the recording is here. Thanks to Jesse Parent (Orthogonal Lab Manager) and Charlotte Guo (series host) for hosting. It will be almost like being there (almost). 

The talk involved reviewing and redefining the role of observers in empirical and simulated systems. "Observer" refers mostly to computational agents (agent-based simulation and AI), although many of the ideas introduced here may apply to the analysis of empirical observations (experiments). Here is the abstract:

In many areas of science and philosophy, observers are seen as an integral part of understanding the natural world. Aside from a pedagogical role, observers are seen as less important in computational forms of inquiry. In this talk, I will reconsider a role for the observer in computational models as fully integrated with the agent. perhaps more fundamentally, causal outcomes and system dynamics are seen to be contingent on observers, while empirical observations themselves are dependent upon the actions of observers. As this is an article of faith in some interpretations of quantum mechanics, we extend this to algorithmic systems with a combinatorial solution space. The role for observers in computational and empirical investigations is established superficially using a number of concepts, including cybernetics, embodiment, and perceptual information processing. Then we will be introduced to more concrete examples of observer-oriented computational agents, such as observer-emitter systems and viewpoint networks. Finally, we will discuss how this approach goes beyond constructivism to consider multiple observers, multiple perspectives (relativism), and how they affect the interpretation of results.

There is a lot to follow up on from this talk, including a number of themes to explore within the topic of agent-based observers, with more to come in the new year.