The common assumption is that genius across all disciplines comes early in a career. Einstein in physics, Nash in economics, Picasso in art, and T. S. Eliot in literature, serve as models who all made their greatest contributions early in their long lives. After this initial burst of creativity, many believe that the innovatorís originality is stifled as they become imbedded in the customs of their discipline. In Creative Careers: The Life Cycles of Nobel Laureates in Economics, authors Ben Weinbergís and David Galensonís analysis shows marked differences in creativity over the lifecycle of the major contributors. They find that there are two basic types of innovators: conceptual and experimentalist. Although the researchers studied do not always fit this dichotomy neatly, conceptual innovators fit the model of the aforementioned contributors and produce their major work early in their careers while the experimentalistsí best work comes significantly later. They use winners of the Nobel Prize in economics as their case study, but they also demonstrate this phenomenon is prevalent in all fields. They are the first to find differences in the lifecycle of scholarly creativity within a single discipline and to relate these differences to the nature of individual scholarsí work.
Weinberg and Galenson create a formula to separate the conceptualists from the experimentalist. This formula weighs different qualities associated with conceptualists and experimentalists. The basic distinction between conceptualists and experimentalists is that a conceptualist works deductively and an experimentalist works inductively. Conceptual innovations, which pose precise problems, are made more quickly, and can occur at any age. The conceptual innovators are more likely to challenge basic tenets of the discipline that are widely treated as rules by more experienced scholars Theorists tend to be conceptual, and the most abstract theorists tend to be highly conceptual.
The achievement of radical conceptual innovations depends on the ability to identify and explore large deviations from traditional thought. The authors believe that this ability tends to decline with experience, as habits of thought become more firmly established. Variable measures assumptions, assumptions, equations, and proofs are all identified with conceptualists. Because of their unusual thinking, conceptualists often contribute to many fields instead of one. This feature is rare among experimental innovators. In economics, Coase, Friedman, and Stigler are famous examples of conceptualists.
In contrast, experimental economists pose broader questions, which they solve inductively by accumulating evidence that serves as the basis for new generalizations. They arrive gradually and incrementally at their major contributions, often over an extended period of time. Their work relies on analyzing as much evidence as possible so that their generalizations are more powerful. Unlike the conceptualists, they produce many tables that report raw data. The authors believe that the great work of experimentalists comes later in life because they need time to accumulate their data and also because older thinkers will become more familiar and efficient in processing their data. Fogel, Kuznets, and Myrdal are famous experimental laureates in economics.
To compare experimentalists and conceptualists, Weinberg and Galenson use citations of the Nobel laureateís work to measure the age of their peak year and important years. A year is ďimportantĒ when laureateís citations are 2 standard deviations above his mean rate of annual citations. The most important year is naturally the year that produces the most citations for each laureate. The authors find this data on the Web of Science, an on-line database comprising the Social Science Citation Index, the Science Citation Index, and the Arts and Humanities Citation Index. The authors define year 1 of a career as the year a laureate received their doctorate or published their first paper.
The authorís realize that measuring productivity is subjective, but believe that their use of citations is effective because citations measure the influence of an article. Important articles are cited often because they open up new areas of focus. Therefore, the seminal work in a field will be cited often. The also add that some new areas of focus attract different levels of attention. However, this variation can only determine when each laureate did his most important work. It cannot serve as an absolute for making inter-personal comparisons because works may be, for example, ahead of their time and, therefore, currently under-cited.At first, the laureates are simply divided into two groups Ė experimental and conceptual. After comparing this split, the authors further divide the conceptual group into extreme conceptual and moderate conceptual groups. These two subtypes are then compared.
The authors find that the profiles for the experimental and conceptual laureates differ markedly. For conceptual laureates, the probability of a major work in the first year of their career is 3.3%, and they peak on average at age 43. For experimental laureates, the probability of a major work in their first year is zero at the beginning of the career. The mean age for their peak is 61. Forty five percent of the important years for the conceptual laureates occurred within the first 10 years of their careers. By contrast, only 19% of the moderate conceptual laureatesí important years were in those first 10 years. Seventy five percent of the pure conceptual laureates had their single best year within the first ten years of their careers.
Weinberg and Galenson also acknowledge that many economists are not pure conceptual innovators or experimentalists. They add moderate conceptualists to the spectrum in between the pure conceptualists and pure experimentalists. (The data listed above was for comparing the pure conceptual and experimental thinkers.) The authors give Coase, Friedman, and Hayek as famous examples in economics. The work of these moderate conceptual laureates often combines theoretical and empirical analyses. Like the experimental laureates, the moderate conceptual laureatesí work frequently contains empirical analysis.
Unlike the experimental laureates, their work relies less heavily on unprocessed data and more heavily on statistical analysis. The conclusion from studying these people in between the pure conceptualists and pure experimentalists is that the more abstract and conceptual the innovation, the earlier in a career it is likely to occur. They find that the moderate conceptualists peak at an average age of 48. Seventeen percent of the moderate conceptualists had their best year in the first ten years of their career. The extreme conceptual laureates are 2.4 times more likely than the moderate conceptual laureates to have an important year in the first 10 years of their career, they are 4.4 times more likely to have their single best year in the first 10 years of their career.
The authors conclude that their empirical analysis strongly supports the idea that innovators can be differentiated by being conceptualists, experimentalists, or a combination of the two. Previous literature demonstrated that there are different peak ages for different fields. From their data, the authors hypothesize that this difference across disciplines is due to the relative levels of empiricism and conceptualism inherent in the field. Physics is a conceptual field, and consequently, many contributions are made from scientists early in their careers. Sociology draws conclusions from large pools of data, and it is not surprising that many key contributors to the discipline are older.
It seems that the ability to formulate and solve problems deductively declines earlier in the career than the ability to create inductively. As a scholar ages, he or she accumulates knowledge connected to their discipline and becomes progressively more accustomed to the particular habits of his or her discipline. These two effects likely augment the creativity of the inductive, experimentalist scholar because the power of their dataís generalizations increases as their evidence grows. As the experimental scholar ages, his or her productivity should amplify due to improved efficiency in amassing and scrutinizing data. In contrast, this accumulation of knowledge and the establishment of fixed habits of thought which begins early in a career may reduce the researcherís ability to innovate the new abstract ideas that are so critical to conceptual innovators.
About a year ago, I had a disagreement with a friend from home about when original thinkers peaked. We reached very different conclusions. His argument was that the human mind peaked in the personís mid-twenties. His implication was that the innovators in physics were superior and smarter than their equivalents in biology because the key contributions in physics generally came from younger researchers. He argued that biology didnít attract thinkers of equal quality as physics because many biologists werenít even making their biggest contributions when their minds were sharpest.
I disagreed with his first assumption and his implication from this assumption. This article supports my idea, and I will be sending him a copy of the article. I said that all innovation was not the same. It was unlikely that Einstein could have contributed as greatly and as early to biology as he had in physics because biology was inherently more experimental in nature. I thought Einsteinís use of thought experiments would not be nearly as effective in biology. I said that different fields attract different kinds of thinkers. Einstein was a lousy experimentalist and likely would not have fared very well in biology. I said that the beauty of biology was that most of its research could be tested rather easily compared to other fields. Many of Einsteinís hypotheses are untestable and will be untestable for years to come. Einstein would not choose biology, and biology would not choose Einsten either. I believed that experimentalists should reach their apex later in the careers because it would take years of testing to deviate from older procedures and create truly original methods. This article supplies data which supports my hypothesis.
I enjoyed this article for reasons other than proving a friend wrong. It was clearly written and not overly difficult. Galenson is my favorite researcher that I have read for my thesis. The only downside is that he doesnít have many more articles that I could use for my thesis project.