When you automate an industry you modernize it; when you automate a life you primitivize it. (Eric Hoffer)
It is well known that rationality and science emerged during the Age of Enlightenment. Many indeed attribute human progress over the past few centuries to reason and discovery. Despite a Romantic reaction against some of the worst excesses of the Industrial Revolution, there is little doubt that human reason, empiricism, and science ultimately became the dominant world view.
The “defeat” of Romanticism signaled our entry into the modern era. But after a century of tumult and change, modernism has itself run into diminishing returns. On the one hand, scientific discovery reveals the world to be more complex and uncertain than we had ever imagined. Yet on the other, we increasingly turn to the security of precise numbers and statistics in futile hope of “taming” the uncertainty that so disconcerts us.
It seems an incongruous, indeed unsustainable response. Matters of such historical importance as dominant worldviews are, of course, fiendishly difficult to predict. Nevertheless, the untenability of the present juncture suggests that it is time to move on. It suggests the urgency for an Enlightenment 2.0.
Modernism and Neoclassical Economics
How is this related to economics? In the 1870s, economic thinkers incorporated 19th century physics equations into their own theoretical framework, making the field far more mathematical and abstract. The result – what came to be known as “neoclassical” economics – elevated the prestige of economics as a scientific enterprise and has dominated the field ever since. Decades later, following the Keynesian revolution of the 1930s, economists started relying on measurement and statistics. The modernist or enlightenment genie was out of the bottle, so to speak.
With its tremendous emphasis today on numbers, indexes, and indicators, economics today would be unrecognizable to classical economists like Adam Smith or David Ricardo. They understood that history, politics, and culture bear or society and economics. Today’s “scientific” economics, in contrast, would have economic activity and behavior somehow occur is isolation from everything else.
Such a notion is, of course, silly. Not even professional economists actually believe it. They know that the simple, sterile, and static world that they present in their economics textbooks has no relationship to immensely complex reality. But speaking the “language” of science – i.e., mathematics – permits entry into an exalted and fairly exclusive professional club. There is, therefore, little temptation to reconcile economic models (what economists mean by “theory”) and reality. The very question is actively suppressed or ignored.
Modernism reinforces the study of both “theoretical” and quantitative economics. Scientific precision is the name of the game. Accuracy (not the same thing!) loses importance. If this were all merely absurd it would be bad enough. But it is more. The absurd disconnect between economic orthodoxy and the real world is ideological – in a subtle, but very powerful way.
Neoclassical Economics and Ideology
From the beginning, neoclassical economics has celebrated free markets. The consistent result that one obtains studying microeconomics is that the “socially optimal” outcome obtains when markets are unfettered. I couldn’t be an economist if I failed to see some logic in this. The idea, in fact, goes back to Adam Smith.
But there is subterfuge here of historical proportions, indeed impossible to overstate. The scientific veneer adopted by economics enhanced its policy credibility and continues to. Yet the sleight of hand is as brilliant as it is simple. Even though our economic system grows increasingly oligarchic, the prevailing capitalist ideology remains steadfast in its narrative that ours is a “free market” system. And theory “shows” that free markets are best.
Such deceitfulness has for a century and a half lent political and philosophical justification to a highly unjust economic system. The widespread idea of “economics as a science” conceals pro-capitalist ideology and helps uphold a neoliberal plutocracy. Here I anticipate the obvious objection. We have all been deceived for a century and a half? How stupid does this guy think people are?
Here is where Enlightenment values come in. Our growing fascination with measurement and quantitative data greatly help obfuscate and preserve the myth. There is perhaps no better example than the endurance of our belief (albeit recently diminished, I grant) in economic growth that “trickles down” to the masses. Macroeconomic statistics are deployed to lend credence to the myth about the merits of neoliberalism. This despite a widespread sense that economic gains have been trickling up.
It is one of the great ironies of our time that our dependence on quantitative data muddles more than it clarifies our thinking.
Evolutionary Economics?
Muddled thinking does not make for good policy. (You don’t say?). Yet as Philip Stephens has noted, despite usually being wrong, economists’ certainty on policy remains resolute. Back in 2008, the Queen of England expressed dismay and disbelief at economists’ inability to anticipate the financial crisis. Yet the experience has scarcely humbled economics. Failure to predict financial turmoil has done little to dampen our confidence in our own predictive models.
Alternatives to neoclassical economics exist and have for some time. Examples include Austrian, ecological, feminist, Marxian, post-Keynesian, and social economics. None has gained any policy traction since they emphasize among other areas, material limits, political inequality, or uncertainty. All are anathema to neoclassical orthodoxy.
Over a century ago, the writings of Thorstein Veblen gave rise to another heterodox approach known as evolutionary economics.[1] Instead of applying deterministic physics methods to economics, here the idea is to view the economy as a complex and unpredictable system. If such as approach sounds more realistic, it is because it is.
The essence of evolutionary economics is its stress on the interdependencies between distinct groups and institutions, and the dialectical role between technology and culture. It considers, in other words, how economics is inseparable from politics, society, and science. It is, unfortunately, that has kept evolutionary economics from joining the mainstream of thought, which insists on analytical precision.
But the analytical precision believed to be required by many in the profession saddles economics with immense limitations when it comes to addressing real problems. It is therefore difficult to imagine anything but a move to a more holistic approach that considers complex interdependencies.
The main question is whether we get there gradually or in a sudden leap.
Thomas Kuhn and Paradigm Shifts
Contrary to the account of most science textbooks, the scientific enterprise has evolved in fits and starts. Or so at least argued Thomas Kuhn in his landmark book, The Structure of Scientific Revolutions. The common – and reassuring – belief is that science progresses linearly, with knowledge accumulating over the years.
But as Kuhn notes, the history of science is replete with theoretical dead ends and the occasional overthrow of earlier orthodoxy. Contrary to Darwin, who appeared to believe that evolution progressed incrementally, scientific evolution itself is punctuated – or characterized by discrete “jumps.” (Perhaps not surprisingly, Steven Jay Gould, whose work was greatly influenced by Kuhn, fashioned a career out of arguing against Darwinian “incrementalism” and offering “punctuated equilibrium” as an alternative).
We can think of the scientific method as objective. Yet in almost every profession, academic or otherwise, there is “turf” that the forces of reaction want to protect. In academe, the institution of tenure exists precisely to protect the “rogue” researcher who, by advancing knowledge, challenges the status quo.
According to Kuhn, a paradigm shift occurs when the evidence against an established model becomes incontrovertible. When this happens, there is revolutionary change. Einstein was a revolutionary, as was Newton before him. But so was Lavoisier. And so, I would argue, was Georgescu-Roegen in economics, although the jury is still out on him.[2]
I submit that a similar reactionary tendency operates at the level of government policy. For reasons stated earlier, there is every reason (for some, anyway) to resist a revolutionary shakeup of economic orthodoxy. The major difference, however, is that unlike the case with science, incontrovertible evidence against any policy regime (like the neoliberal status quo) will always be lacking. Society is no laboratory.
Scientism, the Old and the New
There is no denying that it its pure sense the scientific method is objective. No thinking person would confuse politics with the practice of subjecting hypotheses to observable data. Which is why it is especially disconcerting that “science” has today been so utterly politicized.
The philosophy of scientism would have it that science can and does explain any and all values of importance to humanity. Some interpret this as the belief that everything – not only physical, but moral, spiritual, etc. – can be reduced to science. While not without controversy, exploring this position further would take us too far afield.
But I would venture that a more pernicious strand of scientism dominates popular discourse today. We now tend to conflate numbers and statistics with science,” uncritically favoring quantitative information over anecdote, judgment, or common sense. Statistics, to be sure, have their place. But in the hands of the innumerate, they can mislead or worse.
David Spiegelhalter, chair of Cambridge’s Winton Centre for Risk and Evidence Communication, knows all about it. As far back as May of last year, he dismissed as “numbers theatre” the daily presentation of Covid-19 statistics. In his own words, the numbers were and continue to be “desperately unreliable.” Yet they provide a spurious sense of precision. Again, and much to our peril, precision trumps accuracy.
What I call “the new scientism” unwisely diminishes phenomena that cannot be measured. Yet according to heterodox economist, E.F. Schumacher, measurement and calculation is a “lower function” compared to the ability to critically evaluate:
… [Q]quality is much more difficult to ‘handle’ than quantity, just as the exercise of judgment is a higher function than the ability to count and calculate. Quantitative differences can be more easily grasped and … easily defined than qualitative differences: their concreteness is beguiling and gives them the appearance of scientific precision, even when this precision has been purchased by the suppression of vital differences of quality. [Schumacher, Small is Beautiful, 1973, p. 51]
Science, Measurement, and Politics
Although it often involves or even requires quantitative measurement, it is important not to confuse pure science with measurement. What does it even mean to be “pro science” these days? The politicizing of science is somewhat reminiscent of the abortion debate; it is all in the framing.
Devotees to pro-choice would never characterize their position as “anti-life.” Yet pro-life believers (who the former describe as “anti-choice”) malign their opponents as “pro-abortion.” Similarly, opponents of a “pro-science” position might regard themselves “anti-elite.” The “anti-elite” (or elite skeptic) view is that even if experts, scholars, and scientists, are not deliberately misleading the public, policymakers often do so by distorting their findings.
Take climate change. As I have remarked elsewhere, I believe climate change denial to be untenable. But what is one to make of warnings that we only have, say, eight years to do something before we face an irreversible climate catastrophe? Really? How would anyone really know? How can we calculate that?
It cannot be. Given the vast uncertainty about so many variables, the precision (exactly eight years) strains credulity. The rhetorical use of precise figures unfortunately is fodder for the critics and skeptics – here about climate change, elsewhere about, say, vaccines or GMOs.
These are not “anti-science” positions. If anything, because skepticism is itself the hallmark of science, one might characterize the skeptics as more pro-science than those naïvely succumbing to the thrall of numbers. I realize that this might seem light a stretch, and emphatically do not deny that climate change is real. I just think it is good to be honest about the limits to how much we know and how much we can know.
Writing for the Financial Times, Jemima Kelly warns that in today’s polarized environment, we increasingly blur the difference between fact and opinion. We often deploy “fact checkers” simply to present evidence to undermine an opponent’s contention. In other words, instead of encouraging healthy disagreement, from which we all learn, fact checkers increasingly serve to silence dissenting views.
Complexity and uncertainty
But the truth is that the world is increasingly complex, and uncertainty greater than ever. With existential dangers that we never before faced, Schumacher’s words are more relevant than fifty years ago, when he uttered them.
I have commented on how economists deal with uncertainty and quantify it by using presumed probabilities and calling it risk. But it really is just playing games, even if it results in articles being published in respected journals. And perhaps the greatest danger is our incautious faith in artificial intelligence (AI) to help us confront existential challenges.
In an uncertain world, the capability to recognize that one is ignorant is vital. As much as we rush to numbers as a safeguard against the discomfort of uncertainty, at least at some level most of us notice when we are ignorant about something. Not so computers. They lack what scientists call metacognition.
Paradoxically, the more science discovers, the more we grow aware of how complex the physical world is, and how little we understand. But humans can do that – that is, appreciate how little they know. AI, in contrast, does not think about its own thinking. Its scope is severely limited to what it knows. This hardly presents a problem for a game like chess, but for the infinitely more complex worlds of ecology, economy, society, and so on, it is of little avail.
My fear is that humankind will continue to be seduced by the quick, neat, and precise answers that such technologies provide. And when you think about it, it is but a logical extension of traditional Enlightenment values. But lacking humility and metacognition in confronting seemingly intractable problems like climate change, AI, financialization, or global pandemics is a recipe for disaster.
An alternative vision
Complexity requires a rehabilitation of the much-maligned practice of “common sense.” There is much that we cannot know, much less measure. Greater emphasis on wisdom can compensate for such limits. And we need to better understand the limitations of numbers and statistical analysis.
To be serious about policy going forward, we must discard orthodox economics. A new paradigm will ideally consider economics within its sociohistorical, political, physical, and possibly even spiritual contexts.
I would never advocate a return to a worldview dominated by prejudice and superstition. But it is imperative to move beyond the narrow vision of modernism to a worldview that better grasps and appreciates nuance and embraces instead of recoiling from uncertainty.
Perhaps we could call this new phase Enlightenment 2.0.
[1] Very similar to what others refer to as institutional economics.
[2] With his brilliant book, The Entropy Law and the Economic Process (1970), the author revolutionized economic theory – alas, half a century ahead of his time.