Finding the Face of Genius

 

Today’s guest blogger is Bala Poduval. Bala is a research scientist in the field of Space Weather, studying solar wind conditions in the near-Sun region and at Earth. She is a research affiliate at Space Science Institute, Boulder.

 

[This post was inspired by an essay written by Avi Loeb entitled, How to Collect Matches that Will Catch Fire – eds.]

 

                        Only mediocrity can be trusted to be always at its best.

                        Genius must always have lapses proportionate to its triumphs.

                        -Max Beerbohm, essayist, parodist, and caricaturist (1872-1956)

 

I am not sure if I agree with this quote or how it could be incorporated into the evaluation methods used to select promising scientists. How do we identify these individuals before they make their groundbreaking discoveries?

 

I think one of the key aspects is the method of evaluation. Even after entering the Space Age and High-tech Era, we still haven't updated our methods of evaluation (though, whatever be the methods, groundbreaking discoveries are future events - has scientific progress ever been predicted?). How do we make a wise selection of scientists in a scenario where the number of scientifically literate individuals with advanced degrees is on the rise while artificial intelligence and mechanization tend to lower the number of job opportunities?

 

A person is usually evaluated for qualifications such as consistency (of performance), scientific productivity, and dependability. These qualities could be categorized under "known circumstances," and the evaluators are, knowingly or unknowingly, looking for "self-replications" (to borrow a phrase from Loeb’s essay). To be more quantitative, consider two researchers X and Y: Researcher X has 10 publications in 5 years, and researcher Y has only 4. The general tendency is to select X for an open research or teaching position where both X and Y have applied. And, then, years later, you realize that there hasn't been any impact on scientific progress and wonder why.

 

Let's take a look at the two candidates in retrospect. Researcher X has 10 publications but is the lead author on only two of them. Take a deeper look - none of X’s papers has been cited in any peer-reviewed publications! Now look at researcher Y. Y is the first author of all four papers and they have more than 10 citations each! That is curious, right? Now go deeper still. X has been working in the same field, applying the same techniques with only minor updates, over and again. As a result, X appeared to be the expert in that particular narrow field and technique. How about Y? Y has some expertise in one field, but Y has also branched out to tackle entirely different problems, acquired new skills in limited time, and produced first author papers that have been well received by the scientific community.

 

Now, if you assign points to all these parameters and then make an assessment (differential assessment?), where would you place the higher probability of seeing some innovation or discoveries? You should have hired Y!

 

While it is not always true that the type-Ys are the visionaries or the innovators, the point is that outstanding or groundbreaking discoveries are not guaranteed by the type-Xs either. When we look into the future, we talk in terms of "probabilities." To have an unbiased (or least marginally biased) sample, it is necessary to include some (with a sense of proportion) higher-probability Y's in the cohort. Inaccurate eliminations of the type described above may be one of the key points to be kept in mind to have a pack of "matches that will catch fire" without lighting them first. To emphasize: the problem may not be who you hired but who you eliminated.

 

The number of publications (of a person) over a given period of time shows more of his/her productive capability and not necessarily talent or level of innovation. One of the reasons for this type of evaluation is the necessity (by the funding agent, the host institute, even the society) for a quantitative description of the "profit" of funding a person. While it is important and necessary to have some standard of scientific productivity there is more to it than merely counting papers. Scientific productivity and the "profit" of scientific funding need to be better defined because the current approach is not sufficient to achieve the scientific progress we envisage.

 

It seems the wealth of scientific knowledge of the 19th and 20th century has been used in all possible ways towards improving human life in the form of medical and technological breakthroughs, making the world, in general, safer and healthier than it has ever been. To take the innovations to a higher level and for further betterment of human life, it is necessary to revive science (physics) from the apparent stall. Our efforts in this regard are reflected in the realization of having a cohort of promising scientists capable of making those discoveries that the world seems to be badly in need of.

 

Genius, as it is expected to be the trait of these promising scientists, does not imply being extraordinary in just one particular attribute but a unique and rare combination of many different qualities -- diligence, purposefulness, relentlessness, methodicalness, curiosity, open-mindedness, imagination, attentiveness -- that happens one in a million or so. Genius may not only require extraordinary intelligence, but exceptional creativity/imagination and perseverance as well.

 

To unearth such genius is a formidable task. In light of the discussion of the selection criteria above, we are forced to consider the implications of the opening quote: how to prevent losing a genius if we encounter him/her during the periods of the lapses. Geniuses were not born with a prophecy; it is an attribute that stays dormant waiting for favorable circumstance.

 

Consider an analogy where a professor gives a test where all the formulae, theories, and tools are provided - something like an open book test. The students then solve problems using all possible resources. So, rather than testing knowledge of memorized theories and formulae, the professor is able to test how the students make use of that knowledge to invent new ideas and solve problems. Creating the open-book equivalent to evaluate job candidates is not an easy task, but in my opinion, it is this kind of evaluation that may be required to identify real genius. In other words, the panel itself must have high level of ingenuity to identify genius.

 

Meanwhile, another possible solution would be to have more centers of excellence, government (public) funded institutes dedicated for fundamental research. These centers would offer long-term (if not permanent) positions where the researchers are free to pursue problems of their choice but falls within the general interests of these institutions. This would certainly avoid the unnecessary administrative duties of independent researchers and the uncertainty of continued research funding that can terminate many significant research projects prematurely.

 

Postscript: Discoveries (often) happen serendipitously. It may take a touch of genius to identify genius.

How to Collect Matches that Will Catch Fire

 

Today’s guest blogger is Abraham Loeb. Avi is the Frank B. Baird Jr. Professor of Science at Harvard University. He serves as chair of the Harvard Astronomy department and director of the Institute for Theory & Computation at the Harvard-Smithsonian Center for Astrophysics. Avi is also the father of two young daughters and is working to make the scientific world a better place by the time they enter university.

 

Science can only blossom if young researchers are rewarded for acquired skills and growth rather than inherited academic ancestry.

 

Will a match catch fire when it scratches against the rugged matchbox wall? Knowing the answer is of paramount importance if we want to collect useful matches in our box. One way to find out is to try them all. The only problem with this approach is that by the time we will know the answer, the burnt matches will be of no value. The challenge is how to select useful matches reliably in advance? Putting this challenge into an academic context, how can we select a cohort of promising scientists before they have made their discoveries? This is the fundamental challenge of academic planning. Prestigious universities are plagued by past hirings which led to ‘duds’ or ‘dead wood’, namely faculty who when hired were labeled as geniuses with great promise but in retrospect, decades later, had little impact on the progress of science. At the same time, some of their contemporaries who were not endorsed by prominent scientists and hence moved to faculty positions at lesser schools, carried the day. Without mentioning names, suffice it to say that this is a familiar occurrence. Why is this phenomenon so prevalent?

 

Senior scientists who serve on promotion, prize, or search committees are often asked to evaluate the promise of their younger colleagues. One would naively expect them to approach this challenge in the same way that they address a scientific problem, namely study all available data and construct a model that extrapolates into the future. In order to avoid biases, it would appear natural to adopt a dynamical model which takes into consideration the special initial conditions of an individual and allows for growth in forecasting the individual’s future. For example, a young researcher who did not benefit from the privilege of being nurtured by top quality mentors or had to transition from a different culture or an inferior socio-economic status, should be given more slack. This is common sense. But is it common practice?