Posts Tagged ‘science’

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Roy

Jean-Pierre Roy, “The Sultan and the Strange Loop” (2016)

The U word has once again reared its ugly head.

I’m referring of course to uncertainty, which at least a few of us are pleased has returned to occupy a prominent role in relation to scientific discourse. The idea that we simply do not know is swirling around us, haunting pretty much every pronouncement by economists, virological scientists, epidemiological modelers, and the like.

How many people will contract the novel coronavirus? How many fatalities has the virus caused thus far? And how many people will eventually die because of it? Do face masks work? How many workers have been laid off? How severe will the economic meltdown be in the second quarter and for the rest of the year?

We read and hear lots of answers to those questions but, while individual forecasts and predictions are often presented as uniquely “correct,” they differ from one another and change so often we are forced to admit our knowledge is radically uncertain.

Uncertainty, it seems, erupts every time normalcy is suspended and we are forced to confront the normal workings of scientific practice. It certainly happened during the first Great Depression, when John Maynard Keynes used the idea of radical uncertainty—as against probabilistic risk—to challenge neoclassical economics and its rosy predictions of stable growth and full employment.* And it occurred again during the second Great Depression, when mainstream macroeconomics, especially the so-called dynamic stochastic general equilibrium approach, was criticized for failing to take into account “massive uncertainty,” that is, the impossibility of predicting surprises and situations in which we simply do not know what is going to happen.

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The issue of uncertainty came to the fore again after the election of Donald Trump, which came as a shock to many—even though polls showed a race that was both fairly close and highly uncertain. FiveThirtyEight’s final, pre-election forecast put Hillary Clinton’s chance of winning at 71.4 percent, which elicited quite a few criticisms and attacks, since other models were much more confident about Clinton, variously putting her chances at 92 percent to 99 percent. But, as Nate Silver explained just after the election,

one of the reasons to build a model — perhaps the most important reason — is to measure uncertainty and to account for risk. If polling were perfect, you wouldn’t need to do this. . .There was widespread complacency about Clinton’s chances in a way that wasn’t justified by a careful analysis of the data and the uncertainties surrounding it.

In my view, Silver is one of the best when it comes to admitting the enormous gap between what we claim to know and what we actually know (as I argued back in 2012), which however is often undermined in an attempt to make the results of models seem more accurate and to conform to expectations.

And that’s just as much the case in social sciences (including, and perhaps especially, economics) and the natural sciences as it is in weather forecasting. Many, perhaps most, practitioners and pundits operate as if science is a single set of truths and not a discourse, with all the strengths and failings that implies. What I’m referring to are all the uncertainties, not to mention indeterminisms, linguistic risks and confusions, referrals and deferences to other knowledges and discourses, embedded assumptions (e.g., in both the data-gathering and the modeling) that are attendant upon any practice of discursive production and dissemination.

As Siobhan Roberts recently argued,

Science is full of epistemic uncertainty. Circling the unknowns, inching toward truth through argument and experiment is how progress is made. But science is often expected to be a monolithic collection of all the right answers. As a result, some scientists — and the politicians, policymakers and journalists who depend on them — are reluctant to acknowledge the inherent uncertainties, worried that candor undermines credibility.

What that means, in my view, is science is always subject to discussion and debate within and between contending positions, and therefore decisions need to be made —about facts, concepts, theories, models, and much else—all along the way.

As it turns out, acknowledging that uncertainty, and therefore openly disclosing the range of possible outcomes, does not undermine public trust in scientific facts and predictions. That was the conclusion of a study recently published in the Proceedings of the National Academy of the Sciences.

In the “posttruth” era where facts are increasingly contested, a common assumption is that communicating uncertainty will reduce public trust. . .Results show that whereas people do perceive greater uncertainty when it is communicated, we observed only a small decrease in trust in numbers and trustworthiness of the source, and mostly for verbal uncertainty communication. These results could help reassure all communicators of facts and science that they can be more open and transparent about the limits of human knowledge.

Even if communicating uncertainty does decrease people’s trust in and perceived reliability of scientific facts, including numbers, that in my view is not a bad thing. It serves to challenge the usual (especially these days, among liberals, progressives, and others who embrace a Superman theory of truth) that everyone can and should rely on science to make the key decisions.*** The alternative is to admit and accept that decision-making, under uncertainty, is both internal and external to scientific practice. The implication, as I see it, is that the production and communication of scientific facts as well as their subsequent use by other scientists and the general public is a contested terrain, full of uncertainty. 

Last year, even before the coronavirus pandemic, Scientific American [unfortunately, behind a paywall] published a special issue titled “Truth, Lies, and Uncertainty.” The symposium covers a wide range of topics, from medicine and mathematics to statistics and paleobiology. For those of us in economics, perhaps the most relevant is the article on physics (“Virtually Reality, by George Musser).

Musser begins by noting that “physics seems to be one of the only domains of human life where truth is clear-cut.”

The laws of physics describe hard reality. They are grounded in mathematical rigor and experimental proof. They give answers, not endless muddle. There is not one physics for you and one physics for me but a single physics for everyone and everywhere.

Or so it appears.

In fact, Musser explains, practicing physicists operate with considerable doubt and uncertainty, on everything from fundamental theories (such as quantum mechanics and string theory) to bench science (“Is a wire broken? Is the code buggy? Is the measurement a statistical fluke?”).

Consider, for example, quantum theory: if you

take quantum theory to be a representation of the world, you are led to think of it as a theory of co-existing alternative realities. Such multiple worlds or parallel universes also seem to be a consequence of cosmological theories: the same processes that gave rise to our universe should beget others as well. Additional parallel universes could exist in higher dimensions of space beyond our view. Those universes are populated with variations on our own universe. There is not a single definite reality.

Although theories that predict a multiverse are entirely objective—no observers or observer-dependent quantities appear in the basic equations—they do not eliminate the observer’s role but merely relocate it. They say that our view of reality is heavily filtered, and we have to take that into account when applying the theory. If we do not see a photon do two contradictory things at once, it does not mean the photon is not doing both. It might just mean we get to see only one of them. Likewise, in cosmology, our mere existence creates a bias in our observations. We necessarily live in a universe that can support human life, so our measurements of the cosmos might not be fully representative.

Musser’s view is that accepting uncertainty in physics actually leads to a better scientific practice, as long as physicists themselves are the ones who attempt to point out problems with their own ideas.

So, if physicists are willing to live with—and even to celebrate—uncertain knowledge, and even if the general public does lose a bit of trust when a degree of uncertainty is revealed, then it’s time for the rest of us (perhaps especially economists) to relinquish the idea of certain scientific knowledge.

Then, as Maggie Koerth recently explained in relation to the coronavirus pandemic, instead of waiting around around for “absolute, unequivocal facts” to decide our fate, we can get on with the task of making the “big, serious decisions” that currently face us.

 

*Although, as I explained back in 2011, the idea of fundamental uncertainty was first introduced into mainstream economic discourse by Frank Knight.

**And later central bankers (such as the Bank of England’s Andy Haldane) discovered that admitting uncertainty might actually “enhance understanding and therefore authority.”

***The irony is that “the Left” used to be skeptical about and critical of much of modern science—from phrenology, craniometry, and social Darwinism to the atom bomb, sociobiology, and evolutionary psychology.

 

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Alston

Last month, Philip Alston, the United Nations Special Rapporteur on extreme poverty and human rights (whose important work I have written about before), issued a tweet about the new poverty and healthcare numbers in the United States along with a challenge to the administration of Donald Trump (which in June decided to voluntarily remove itself from membership in the United Nations Human Rights Council after Alston issued a report on his 2017 mission to the United States).

The numbers for 2017 are indeed stupefying: more than 45 million Americans (13.9 percent of the population) were poor (according to the Supplemental Poverty Measure*), while 28.5 million (or 8.8 percent) did not have health insurance at any point during the year.

But the situation in the United States is even worse than widespread poverty and lack of access to decent healthcare. It’s high economic inequality, which according to a new report in Scientific American “negatively impacts nearly every aspect of human well-being—as well as the health of the biosphere.”

As Robert Sapolsky (unfortunately behind a paywall) explains, every step down the socioeconomic ladder, starting at the very top, is associated with worse health. Part of the problem, not surprisingly, stems from health risks (such as smoking and alcohol consumption) and protective factors (like health insurance and health-club memberships). But that’s only part of the explanation. But that’s only part of the explanation. The rest has to do with the “stressful psychosocial consequences” of low socioeconomic status.

while poverty is bad for your health, poverty amid plenty—inequality—can be worse by just about any measure: infant mortality, overall life expectancy, obesity, murder rates, and more. Health is particularly corroded by your nose constantly being rubbed in what you do not have.

It’s not only bodies that suffer from inequality. The natural environment, too, is negatively affected by the large and growing gap between the tiny group at the top and everyone else. According to James Boyce (also behind a paywall), more inequality leads to more environmental degradation—because the people who benefit from using or abusing the environment are economically and politically more powerful than those who are harmed. Moreover, those at the bottom—with less economic and political power—end up “bearing a disproportionate share of the environmental injury.”

Social and institutional trust, too, decline with growing inequality. And, as Bo Rothstein explains, societies like that of the United States can get trapped in a “feedback loop of corruption, distrust and inequality.”

Voters may realize they would benefit from policies that reduce inequality, but their distrust of one another and of their institutions prevents the political system from acting in the way they would prefer.

But what are the economics behind the kind of degrading and destructive inequality we’ve been witnessing in the United States in recent decades? For that, Scientific American turned to Nobel laureate Joseph Stiglitz for an explanation. Readers of this blog will be on familiar ground. As I’ve explained before (e.g., here), Stiglitz criticizes the “fictional narrative” of neoclassical economics, according to which everyone gets what they deserve through markets (which “may at one time have assuaged the guilt of those at the top and persuaded everyone else to accept this sorry state of affairs”), and offers an alternative explanation based on the shift from manufacturing to services (which in his view is a “winner-takes-all system”) and a political rewriting of the rules of economic game (in favor of large corporations, financial institutions, and pharmaceutical companies and against labor). So, for Stiglitz, the science of inequality is based on a set of power-related “market imperfections” that permit those at the top to engage in extracting rents (that is, in withdrawing “income from the national pie that is incommensurate with societal contribution”).

The major problem with Stiglitz’s “science” of economic inequality is that he fails to account for how the United States underwent a transition from less inequality (in the initial postwar period) to growing inequality (since the early 1980s). In order to accomplish that feat, he would need to look elsewhere, to the alternative science of exploitation.

While Stiglitz does mention exploitation at the beginning of his own account (with respect to American slavery), he then drops it from his approach in favor of rent extraction and market imperfections. If he’d followed his initial thrust, he might have been able to explain how—while New Deal reforms and World War II managed to engineer the shift from agriculture to manufacturing, reined in large corporations and Wall Street, and bolstered labor unions—what was kept intact was the ability of capital to appropriate and distribute the surplus produced by workers. Thus, American employers, however regulated, retained both the interest and the means to avoid and attempt to undo those regulations. And eventually they succeeded.

What is missing, then, from Stiglitz’s account is a third possibility, an approach that combines a focus on markets with power, that is, a class analysis of the distribution of income. According to this science of exploitation or class, markets are absolutely central to capitalism—on both the input side (e.g., when workers sell their labor power to capitalists) and the output side (when capitalists sell the finished goods to realize their value and capture profits). But so is power: workers are forced to have the freedom to sell their labor to capitalists because it has no use-value for them; and capitalists, who have access to the money to purchase the labor power, do so because they can productively consume it in order to appropriate the surplus-value the workers create.

That’s the first stage of the analysis, when markets and power combine to generate the surplus-value capitalists are able to realize in the form of profits. And that’s under the assumption that markets are competitive, that is, there are not market imperfections such as monopoly power. It is literally a different reading of commodity values and profits, and therefore a critique of the idea that capitalist factors of production “get what they deserve.” They don’t, because of the existence of class exploitation.

But what if markets aren’t competitive? What if, for example, there is some kind of monopoly power? Well, it depends on what industry or sector we’re referring to. Let’s take one of the industries mentioned by Stiglitz: Big Pharma. In the case where giant pharmaceutical companies are able to sell the commodities they produce at a price greater than their value, they are able to appropriate surplus from their own workers and to receive a distribution of surplus from other companies, when they pay for the drugs covered in their health-care plans. As a result, the rate of profit for the pharmaceutical companies rises (as their monopoly power increases) and the rate of profit for other employers falls (unless, of course, they can change their healthcare plans or cut some other distribution of their surplus-value).**

The analysis could go on. My only point is to point out there’s a third possibility in the debate over growing inequality in the United States—a theory that is missing from Stiglitz’s article and from Scientific American’s entire report on inequality, a science that combines markets and power and is focused on the role of class in making sense of the obscene levels of inequality that are destroying nearly every aspect of human well-being including the natural environment in the United States today.

And, of course, that third approach has policy implications very different from the others—not to force workers to increase their productivity in order to receive higher wages through the labor market or to hope that decreasing market concentration will make the distribution of income more equal, but instead to attack the problem at its source. That would mean changing both markets and power with the goal of eliminating class exploitation.

 

*The official rate was 12.3 percent, which means that 39.7 million Americans fell below the poverty line.

**This is one of the reasons capitalist employers might support “affordable” healthcare, to raise their rates of profit.

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