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.
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).
