Predictive models dominate our lives — not always for the better

Prof. George E. P. Box warned us: “All models are wrong, but some are useful.” I have worked with people who develop models over the years, mostly relating to economic policy. They were conscientious modelers, dedicated to developing a model that would reasonably estimate the economic impact of various laws and policies. That’s when the models can be useful, but not all are. All models are built on a multitude of assumptions, and many of those assumptions increasingly reflect the ideological and political views of the modelers. If the assumptions are skewed, so will be a model’s predictions. Let’s start with econometric models that attempt to predict the impact of various government tax and spending proposals. By law, Congress requires the Congressional Budget Office (CBO) to “score” most major federal tax and spending bills. That is, there must be an estimate of a bill’s impact on federal revenue and/or spending. Such projections are often way off the mark — sometimes by design. Take the Affordable Care Act (i.e., ObamaCare), which Democrats passed in 2010. President Obama had declared, “I will not sign a plan that adds one dime to our deficits — either now or in the future.” That meant ACA costs had to be offset by other savings or new revenue. The CBO estimates a bill’s impact over 10 years. Since the new ObamaCare taxes wouldn’t offset its spending, Democrats began the taxes four years before the health insurance provisions kicked in. Ten years of taxes to offset six years of spending, and POOF! It’s paid for — at least according to the CBO. No one — NO ONE — really believed ObamaCare wouldn’t add to the federal debt, especially after the tenth year. And subsequent CBO estimates proved it was billions of dollars short. But the model said it was balanced at the time of passage, and that gave Democrats the political cover they needed to vote for it. Or take the constant barrage of climate model predictions. Environmentalists, the left and most of the media accuse skeptics of being “climate deniers.” But what many on the right are skeptical of isn’t actual scientific data, but some climate models’ predictions of temperatures and sea level rise 50 or 100 years in the future. And yet the media regularly conflate the two. If you don’t believe the predictions of a climate model then you are denying the science, when what’s actually being questioned is many of the assumptions built into the model. Here’s an example. Nearly all climate models in the late 1990s and early 2000s greatly overestimated rising temperatures because they didn’t take into account what’s now known as the “warming hiatus” that lasted about 14 years – from about 1998 to 2012 – when global temperatures remained relatively flat. In other words, the actual data did not match the models’ predictions, which left climate modelers and environmentalists scrambling to explain the discrepancies. To be sure, the earth has been on a gradual warming trend since the end of the ice age — and no one asserts the ice age ended because of human activity. The earth appears likely to continue warming for the foreseeable future, and humans may be exacerbating that warming. But no climate model is sophisticated enough to know the temperature or sea level 50 or 100 years from now — especially since countless variables, including innovation, may fundamentally change. And yet leftists and environmentalists want us to dramatically alter the economy and our way of life – e.g., through the Green New Deal – based on predictions that might, but probably won’t, be correct. And speaking of predictions that aren’t correct, can we talk about those coronavirus pandemic models? A new National Bureau of Economic Research (NBER) working paper highlights just how influential – and wrong – some of the pandemic models have been. Both U.S. and UK leaders were advocating a measured response to the coronavirus pandemic until the UK’s Imperial College-London released its model’s results predicting 500,000 deaths in the UK and 2.2 million deaths in the U.S. Health officials in both countries freaked and began promoting much stricter guidelines, shutting down the economy and pushing shelter-in-place orders. Within a couple of weeks, the Imperial College scaled back its predictions, to no more than 20,000 UK deaths. And most pandemic modelers have been revising their worst-case scenarios. To be fair, the College said it changed its predictions based on governments’ actions to lockdown their economies. But the real problem is there is so little accurate data about the coronavirus and its workings. Even so, the media have been obsessed with the worst-case numbers. And anyone who raised doubts about those predictions was pilloried by the media and the left as denying the “science.” Of course, it isn’t just right-of-center people who occasionally doubt the models. The left regularly dismisses any economic model that shows that some tax cuts actually increase federal revenues — predictions that have been proven correct several times. And yet models increasingly control our lives because policymakers use them to justify their actions and their votes. As Dr. Anthony Fauci, the lead U.S. epidemiologist in this pandemic, recently warned, “I know my modeling colleagues are going to not be happy with me, but models are as good as the assumptions you put into them.” He’s right. There is a place for models in the public policy debate, but people, and especially the media, need to recognize their limitations — and especially their potential impact on our lives. All models are wrong, and some are even harmful.   This article appeared on The Hill website at https://thehill.com/opinion/technology/496322-predictive-models-dominate-our-lives-not-always-for-the-better]]>

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