An interview with Samuel Arbesman, author of The Half-Life of Facts
In 1947 a mathematician named Derek J. de Solla Price came to Raffles College in Singapore to teach. He did not, Samuel Arbesman writes in his new book, "intend to spearhead an entirely new way of looking at science." But as you probably guessed, that's exactly what he did, or we would not be retelling his story here.
Price's discovery came when construction on the college library forced him to take a complete set of Philosophical Transactions, published by the Royal Society of London since 1665, back to his dormitory. At home, he stacked the journals up chronologically against a wall, with each pile sorted by year. "One day," Arbesman writes, "while idly looking at this large collection of books that the library had foisted upon him, he realized that the heights of the piles of these bound volumes weren't all the same. But their heights weren't random either. Instead, he realized, the heights of the volumes fit a specific mathematical shape: an exponential curve."
It is this insight that opened up the field of scientometrics, which is the topic of Arbesman's new book, The Half-Life of Facts, published this fall by Current books. I asked Arbesman a few questions about his book -- what it means to quantify scientific learning, and what we can really learn from this sort of study -- and he sent me his answers (via email) to share with you below.
What is scientometrics and what is meant by the title of your book The Half-Life of Facts?
Arbesman: Scientometrics is the quantitative measurement and study of science. Or, more generally, it's the science of science, which ranges from understanding how the number of journals grows, to how scientific collaboration works, to even how scientists receive grants. It is part of the larger body of research devoted to understanding more broadly how knowledge grows, changes, and gets overturned over time.
While many of us know that knowledge changes (such as what is currently considered nutritious, for example) and that any individual fact might seem to shift with a certain degree of randomness, the change in what we know overall obeys mathematical regularities. A powerful analogy is to radioactivity. While we can't predict when a specific atom is going to decay, when we have lots of atoms together -- such as in a chunk of uranium -- suddenly we go from the unpredictable to the systematic and the regular. And we can even encapsulate this decay in a single number: the half-life of the radioactive material.
The same kind of thing occurs with knowledge: Even if we can't predict which new discovery is going to occur or which fact is going to be overturned, facts are far from random in the aggregate. Instead, there is an order and a regularity to how knowledge changes over time. And this awareness and understanding is what the "The Half-Life of Facts" is meant to convey.
It seems that in thinking about knowledge and its evolution, we can create a sort of taxonomy of different kinds of factual changes. Sometimes information we have is plain disproved by science, sometimes it is filled out so that we understand how it works better, and sometimes it is refined in such a way that it gains nuance, more accurately describing the world around us. Are the *kinds* of factual changes that we see changing as science advances? Does this vary across scientific fields?
Arbesman: The path of science is one of getting ever closer to the truth. So in general, as knowledge changes, and even becomes overturned, we are approaching a better understanding of the world around us. And I think this cuts across scientific disciplines. Isaac Asimov made this point eloquently:
When people thought the earth was flat, they were wrong. When people thought the earth was spherical, they were wrong. But if you think that thinking the earth is spherical is just as wrong as thinking the earth is flat, then your view is wronger than both of them put together.
In fact, the correct view of the world is a type of geometric object known as an oblate spheroid. That being said, a perfect sphere is a decent approximation, and a flat Earth is not as good (though fine for very small distances). Even though these theories might give us a totally different mental picture, it turns out we can quantify the amount of error for each successive view of the world, and place them on a continuum. And I think this is a way to unify knowledge change, rather than distinguish the types. Overall we are improving our ability to measure and understand our surroundings.