Learning to be thick skinned!

The following anecdote came in a posting to one of the mailing lists I subscribe to, on decision theory. The message of course is quite domain independent, and in many ways transcends time too!

On Christmas Eve 1874, Tchaikovsky brought the score of his Piano Concerto no. 1 to the renowned pianist and conductor and the founder of the Moscow Conservatory, Nikolai Rubinstein, for advice on how to make the solo part more effective. This is how Tchaikovsky remembers it.

“I played the first movement. Not a single word, not a single comment! … I summoned all my patience and played through the end. Still silence. I stood up and asked, ‘well?’’’

“Then a torrent poured forth from Nikolai Gregorievich’s mouth… My concerto, it turned out, was worthless and unplayable – passages so fragmented, so clumsy, so badly written as to be beyond rescue – the music itself was bad, vulgar – here and there I had stolen from other composers – only two or three pages were worth preserving – the rest must be thrown out or completely rewritten…”

‘I shall not alter a single note’ I replied, I shall publish the work exactly as it stands!’ And this I did.”

The moral of the story: If you believe in the merits your work, don’t let a bad referee report get you down. Listen to Tchaikovsky’s Piano Concerto no. 1 to lift your spirit and move on.


On blue sky work…

Useful perspective to keep in mind for the next time one receives unfairly critical comments about speculative work:

Successful research enables problems which once seemed hopelessly complicated to be expressed so simply that we soon forget that they ever were problems. Thus the more successful a research, the more difficult does it become for those who use the result to appreciate the labour which has been put into it. This perhaps is why the very people who live on the results of past researches are so often the most critical of the labour and effort which, in their time, is being expended to simplify the problems of the future.

– Sir Bennett Melvill Jones, British aerodynamicist.

What constrains your research?

Matt Welsh, a former Harvard professor who moved to Google, and who writes the Volatile and Decentralized blog, has some interesting observations on this topic: http://matt-welsh.blogspot.co.uk/2013/04/the-other-side-of-academic-freedom.html.

As he points out, there are at least four constraints that must be simultaneously satisfied:

  • What you can get funding to do;
  • What you can publish (good) papers about;
  • What you can get students to help you with;
  • What you can do better than anyone else in the field.

Wise words of advice that should be heeded, especially by early career researchers! I found the discussion in the comments as interesting as the main post.

Hard Problems in Social Science

I came across these short talks on hard problems in the social sciences, modeled after the famous Hilbert problems. I am not sure if the analogy holds under strict scrutiny (Is there a Hilbert today who can command the same influence in such a diverse area? Is there even a cohesive community at whom these challenges can be aimed?), but the talks were certainly very interesting and thought provoking.

Clearly, some talks/questions are closer to home than others but even the ones that seemed initially unrelated are thought provoking. The ones that came closest include a talk by Susan Carey on the problem of concept learning – something that is directly on the critical path of intelligent autonomous robotics. I had already read some of her papers before but this talk gave a concise summary and highlighted a connection to human learning in the classroom, something I rarely think about. Taleb’s question was the well known one about how to design robust strategies in the face of ‘black swans’. Despite his notoriety and (as I am told) penchant for personal rants, he managed to be quite constructive in the way he delineated the problem.  This is a problem that does show up quite centrally in our work – how do you learn policies for an a priori unknown open world. Some others were interesting too – Ann Swidler asks what makes a good theory of institutions (e.g., how and why exactly does al-Sistani come to have such enourmous influence in Afghanistan, even more so than the people with the guns or the money)? Her hypotheses were quite interesting and may turn out to be relevant as we wonder how to make resilient organizations of artificial agents!

Are citation counts in life sciences inflated?

If things like this are considered publishable in peer-reviewed archival journals (and gather >50 citations, a real mark of accomplishment in other areas of science), then perhaps they are!

M.M. Tai, A mathematical model for the determination of total area under glucose tolerance and other metabolic curves, Diabetes Care, vol. 17 no. 2 152-154, 1994.


OBJECTIVE–To develop a mathematical model for the determination of total areas under curves from various metabolic studies. RESEARCH DESIGN AND METHODS–In Tai’s Model, the total area under a curve is computed by dividing the area under the curve between two designated values on the X-axis (abscissas) into small segments (rectangles and triangles) whose areas can be accurately calculated from their respective geometrical formulas. The total sum of these individual areas thus represents the total area under the curve. Validity of the model is established by comparing total areas obtained from this model to these same areas obtained from graphic method (less than +/- 0.4%). Other formulas widely applied by researchers under- or overestimated total area under a metabolic curve by a great margin. RESULTS–Tai’s model proves to be able to 1) determine total area under a curve with precision; 2) calculate area with varied shapes that may or may not intercept on one or both X/Y axes; 3) estimate total area under a curve plotted against varied time intervals (abscissas), whereas other formulas only allow the same time interval; and 4) compare total areas of metabolic curves produced by different studies. CONCLUSIONS–The Tai model allows flexibility in experimental conditions, which means, in the case of the glucose-response curve, samples can be taken with differing time intervals and total area under the curve can still be determined with precision.