The 2013 Nobel Prize in Economic Sciences was awarded last month to professors Eugene Fama and Lars Peter Hansen from the University of Chicago, and Robert Schiller from Yale University “for their empirical analysis of asset prices.” While there is general agreement that these three economists are most deserving of the prize, quite a few were also surprised that Fama and Schiller shared the award because they hold such different views of their discipline.
Professor Fama is the father of the efficient market theory, which says that prices in financial markets reflect all available information. He has long maintained that he does not understand what people mean by financial bubbles and is thus not sure if they actually exist. He is associated with the Chicago School of Economics, which believes that people generally make rational choices about their financial interests.
Professor Schiller, one of the fathers of behavioral economics, holds very different views. He believes that individual and crowd psychology strongly influence the behavior of markets and that social and emotional factors have a strong influence on the economic decisions made by individuals and institutions. In 2000 Schiller published Irrational Exuberance, a book about the dot-com bubble, which he updated in 2005 to cover the housing bubble.
“What kind of science, people wondered, bestows its most distinguished honor on scholars with opposing ideas?,” is the rhetorical question raised by Harvard Economist Raj Chetty in a recent NY Times opinion piece, Yes, Economics is a Science, before coming to the defense of his field:
“[The] headline-grabbing differences between the findings of these Nobel laureates are less significant than the profound agreement in their scientific approach to economic questions, which is characterized by formulating and testing precise hypotheses. I’m troubled by the sense among skeptics that disagreements about the answers to certain questions suggest that economics is a confused discipline, a fake science whose findings cannot be a useful basis for making policy decisions.”
“That view is unfair and uninformed. It makes demands on economics that are not made of other empirical disciplines, like medicine, and it ignores an emerging body of work, building on the scientific approach of last week’s winners, that is transforming economics into a field firmly grounded in fact.”
Scientific disciplines seek to develop testable explanations and predictions through the use of scientific methods: “To be termed scientific, a method of inquiry must be based on empirical and measurable evidence subject to specific principles of reasoning.”
Throughout history, scientific revolutions are launched when new tools make possible all kinds of new measurements and observations. Early in the 17th century, Galileo made major improvements to the recently invented telescope which enabled him to make discoveries that radically changed our whole view of the universe. Over the centuries we’ve seen that new tools, measurements and discoveries precede major scientific breakthroughs in physics, chemistry, biology and other disciplines.
Big data is such a measurement revolution made possible by the new digital tools all around us, including location data transmitted by our mobile phones; searches, web links and social media interactions; payments and transactions; the myriads of smart sensors keeping track of the physical world; and so on. Our new big data tools have the potential to usher an information-based scientific revolution.
Data science is now emerging as a research and applied methodology that seeks to extract insights from all that big data by applying tried-and-true scientific methods. One of the most exciting part of data science is that it can be applied to just about any domain of knowledge, given our newfound ability to gather valuable data on almost any topic.
Like with all scientific revolutions, we have much to learn. Over the past few centuries, the so-called hard sciences like physics and chemistry have developed principles and models that makes it possible to predict the behavior of natural systems under widely varying conditions. But the situation is quite different with social systems, where people, organizations and their intricate interactions are the key components. Its difficult to make accurate predictions in social systems because the behavior of people and organizations exhibit such a high degree of variance.
In a recent blog, Is Economics a Science?, Jared Bernstein observes: “ . . .the theories, variables, concepts, even the most fundamental building blocks of economics are social, not physical constructs. You drop a stone, known forces determine its trajectory. You change a price, and economic theory can tell what might happen. It can even tell you - with mathematical precision - what should happen if that price change is met by fully rational actors with full information. But it cannot tell you what will happen, nor will it ever be able to do so.”
In his article, Chetty points out that the challenges faced by economists in making accurate predictions are not different from those encountered in medicine and public health. “Health researchers have worked for more than a century to understand the big picture questions of how diet and lifestyle affect health and aging, yet they still do not have a full scientific understanding of these connections. Some studies tell us to consume more coffee, wine and chocolate; others recommend the opposite. But few people would argue that medicine should not be approached as a science or that doctors should not make decisions based on the best available evidence.”
“As is the case with epidemiologists, the fundamental challenge faced by economists - and a root cause of many disagreements in the field - is our limited ability to run experiments. If we could randomize policy decisions and then observe what happens to the economy and people’s lives, we would be able to get a precise understanding of how the economy works and how to improve policy. But the practical and ethical costs of such experiments preclude this sort of approach. (Surely we don’t want to create more financial crises just to understand how they work.)”
Researchers in a number of fields are now conducting scientific experiments by analyzing large amounts of data in many cases where controlled experiments are not feasible. Professor Chetty discusses a few such data-driven economic experiments in his article. “And, as the availability of data increases,” he concludes, “economics will continue to become a more empirical, scientific field.”
So, are financial markets efficient or irrational? When asked this question in a recent interview, Professor Hansen simply replied: “I don’t really know how to answer that.” “[You] cannot tell the difference anymore between the behavioral and the rational explanations,” said Professor Fama in another interview. Finally, in a column published shortly after the awards were announced, Professor Shiller diplomatically wrote: “We disagree on a number of important points, but there is nothing wrong with our sharing the prize. In fact, I am happy to share it with my co-recipients, even if we sometimes seem to come from different planets.”