I recently read Yuval Noah Harari’s Sapiens: A Brief History of Humankind. It’s a bit late for a formal review since the book is over five years old. But in case you haven’t read it, the book is fascinating. Harari manages to convey so much historical information in an extraordinarily clear and concise manner. Despite its far-reaching scope, Harari manages to come across as profound and radical. I do not mean radical in the “leftist” or “extreme” sense, rather in the effective way that the author gets to the root dynamics underlying all of history. He is a master of the generalist perspective that I seek in my own writing. One almost imagines him clairvoyant in describing the challenges in the near future resulting from a few inherent historical forces. I won’t give more away but do read it!
I bring up Harari because he reminds me of another work that has influenced my economic thinking, the late Robert Heilbroner’s Worldly Philosophers. The book is a beautiful excursion over which the reader meets most of the major economic thinkers of the past two centuries or so and learns about how their own life experiences shaped their theories. It is not what I want to write about today. But I do want to point out a well-cited passage from this best-selling book. In it, Heilbroner describes how, as economics evolved into an academic discipline around the turn of the 20th century, its investigations increasingly threw out “pinpoint beams” rather than “wide-searching beacons.”
What he meant, of course, is that as it became ever more specialized economics was losing sight of the big picture. As concerns economics, his insight undoubtedly turned out to be prescient. But I would venture that it also offers a strong critique of contemporary society more generally. We have become a society and world of specialists, expert in one or a few things but lacking sufficient general understanding to confront major problems. Harari and Heilbroner, among others not named here, underscore the urgency of a generalist perspective.
Generalism, of course, is antithetical to quantitative measure. To an economist, quantitative precision is what it is all about. Yet anyone – or even any computer – can spit out numbers from data or information that it is fed. Questions regarding where the numbers come from, or what assumptions underly them – these are usually afterthoughts despite their importance. Data talks. As long as nobody is keeping score, or no one’s job is on the line, the accuracy of (precise) valuations and predictions does not really matter. Which is probably a good thing, because they are almost always wrong.
And this is as should be expected, since real world economics is infinitely more complex than any quantitative model, no matter how sophisticated. As I’ve commented earlier, so many physical, political, historical, cultural, and ethical intangibles go into determining economic outcomes. None of this can be credibly “measured” and so no, economics can never be a science.
When I was younger, I naïvely though that almost any type of problem had a solution that could be found. But my studying economics for a quarter century has put the lie to that. Strangely, I feel reassured – liberated, even – by the realization that numbers and computation will not avail us in confronting the major challenges faced by the country and the world. Don’t get me wrong. Science, specialization, and quantitative precision have contributed immensely to improving our living standards over the centuries. But overspecialization has rendered most of us unable to think critically and exercise enlightened judgment in confronting problems much larger than one’s own field of knowledge.
Harari’s ideas that money is a “fiction” or that capitalism is a religion will not be of any help in projecting the Fed funds rate for the next five years. But they are examples of deep insights that betray a superior, even if broader, understanding of the big picture than most neoclassical economists can ever hope to achieve.
Comments 1
Interesting points about the balance between generalists and specialization. I find a similar limitation in the psychology field. I’d like to talk more about your ideas of data. I believe it can answer a lot of questions but that they need to be matched against other (less measurable) factors in order to gain a broad perspective