When a major league baseball player is at bat, all eyes are on that isolated performance by one person. His strikeout or double will be recorded and accessible to everyone on the Internet forever. Like most employees, my performance is not like that. As a social scientist if I get hired to consult to an organization, it is unlikely that anyone outside that organization will know how I did. Moreover, my effort is part of the work of many people so the true consequences of my specific work aren’t obvious.
Economists, though, are social scientists who make visible, recorded predictions. It amazes me that the predictions of some economists have often repeatedly been very wrong, yet they are still sought out by the media and maintain prominent platforms such as a column in the New York Times.
We have a recent example of bad economic predictions that also illustrates how we need to keep a skeptical eye on the advice of experts. In June, Britain voted to leave the European Union. Before the “Brexit” vote, the consensus of expert economists was that it would be an economic disaster for Britain. The renowned economists at the International Monetary Fund predicted that Brexit would be “pretty bad to very bad” for Britain’s economy. The consensus of think tanks and government organizations such as the British Treasury was that Brexit would result in a drop of 2 to 7.9 percent in Britain’s economy in the future.
It has only been three months, but these renowned economists expected most of the damage to occur in the short term. So far, Britain is doing just fine, and to their credit, Morgan Stanley and Goldman Sachs recently revised their pessimistic predictions to optimistic ones.
Do you remember Jonathan Gruber, the primary architect of the Affordable Care Act? He was paid millions of dollars for his model predictions. Among many outputs, his model predicted that the ACA would sharply lower the cost of health care; allow people to keep their existing doctors; increase competition among insurance providers (particularly through the co-ops created); increase employment; increase coverage of young invincibles who would pay for coverage rather than a fine; and decrease income inequality. All of these predictions have been not only incorrect, but spectacularly wrong.
None of us can be an expert on education, health care, climate change, the economy, Zika virus risks, marriage, terrorism, and hundreds of other subjects that are important to our well-being. We need to rely on the advice of experts, but which experts? So-called experts sometimes reach completely different conclusions and the “consensus” of experts is often wrong.
What do all experts have in common? They tend to all have high levels of cognitive processing ability, what we normally call being “smart.” They can remember and process vast amounts of knowledge. They are great at deductive and statistical reasoning. Smart people differ, though, on the information or assumptions to which they apply their brilliance. When I was a computer scientist, we had a saying that “garbage in = garbage out.”
In other words, if you work with flawed data or assumptions, you will produce wrong conclusions or predictions. So, what you want to know from experts, first, is what their assumptions are before you ask about their recommendations. Interestingly, the average person is often just as good at knowing what assumptions are true than any expert.