Posts Tagged ‘uncertainty’

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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Maarten Vanden Eynde, The Invisible Hand (2015)*

We hear it all the time. On a regular basis. Having to do with pretty much everything.

Why is the price of gasoline so high? Mainstream economists respond, “it’s the market.” Or if you think you deserve a pay raise, the answer again is, “go get another offer and we’ll see if you’re worth it according to ‘the market’.”

Alternatively, if you want to solve a particularly pressing problem—such as climate change, widespread unemployment, or Third World poverty—mainstream economists’ usual answer is “let markets handle it.”**

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Markets have a magical, quasi-mystical status within mainstream economics. They are both the original starting-point and far-reaching conclusion of mainstream economic theory. What I mean, first, is markets are there at the very beginning, without any explanation of where they come from or how they are formed—although there may be an occasional nod to Adam Smith (who famously invoked a natural “propensity to truck, barter, and exchange one thing for another”) or Robinson Crusoe (which presents, on one reading of Daniel Defoe’s novel, the model of two individuals who trade to their mutual benefit under conditions of equality, reciprocity, and freedom).*** Otherwise, markets are just there, with the requisite price and quantity axes and supply and demand schedules, as the starting point for economic analysis. Then, after a great deal of theoretical work (concerning the underlying determinants and the final consequences), markets are declared to be the best solution to the problem of scarcity (in finding a perfect balance between limited means and unlimited desires).

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The “proof” of the superiority of markets often occurs in two steps (although today, in the usual sloppy teaching of mainstream economics, the second step is left out). At the level of individual markets, mainstream economists’ argue that economic welfare—consisting of the sum of consumer and producer surplus—is maximized at equilibrium. “Consumer surplus” is the extra benefit enjoyed by consumers in a market who pay less for goods and services than they were willing and able to pay for it (areas A + B + C, in the diagram above). Meanwhile, “producer surplus” is the difference between what producers are willing and able to supply a good for and the price they actually receive (areas E + D). At the equilibrium, the sum of the two is at its maximum. In contrast, when the market is not at equilibrium (such as when there’s a minimum wage, a wage rate above the market equilibrium wage rate, the green line in the diagram), there’s a “deadweight loss” (consisting of C + D). As far as mainstream economists are concerned, each market in equilibrium (whether for oranges or labor) creates the most total welfare for market participants.

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What about the market system as a whole? Here, the argument is somewhat different. It’s a theory about efficiency, not welfare.**** Mainstream economists claim that, when taken together (in what is referred to as general equilibrium), markets can generate a set of prices that finds a point—for example, A, B, or C, in the diagram above—on the “production possibilities frontier.”***** That’s the maximum amount an economy, given its technology and resources, can produce. Any point inside the frontier (such as D) represents an inefficient allocation of resources (more can be produced of either or both goods without the kind of tradeoff that occurs on the frontier). Importantly, Pareto efficiency means that no one can be made better off without making someone worse off.

That’s the remarkable, counter-intuitive conclusion of the mainstream theory of markets: everyone—every individual and society as a whole—benefits in a world in which all households and firms make decisions based solely on their own self-interest.

Thus, mainstream economists’ celebrations of the market and market solutions for all economic and social problems rely on both the presumption of markets as the given starting-point of analysis and their sweeping conclusions, concerning individual markets and the market system as a whole.

It is, of course, easy to criticize one or another of the assumptions underlying the celebration of free markets, many of them formulated by mainstream economists themselves. For example, markets may have “negative externalities,” that is, social costs that are greater than private costs (pollution is a common example). Under such conditions, more of a good or service will be produced than is socially beneficial. Monopoly power also distorts markets, since with market power firms will produce less, at a higher price, than if they operated according to the model of perfect competition (and, as mainstream economists are now discovering, it’s likely they will pay lower wages).****** Imperfect and asymmetric information, too, will lead to inefficient market outcomes—such as, for example, when conflicts of interest arise between a principal and an agent in a firm or banks are able to sell more financial products (such as derivatives) if they can conceal the true level of risk.

Thus, we can understand the two poles of debate within mainstream economics. Economists within the conservative or libertarian free-market wing celebrate free markets and criticize any and all forms of government intervention, while those in the more liberal wing focus on market imperfections and call for more government regulation of markets. Once again, it’s the invisible hand versus the invisible hand.

But underlying and informing the debate between the two wings of mainstream economics is a shared utopianism of markets as the best, natural and most efficient way of allocating goods and services—including labor, money, and natural resources. They may and often do disagree about the necessity and effectiveness of freeing-up or regulating markets, which comes down to whether or not they “see” exceptions to the basic model of perfect markets. But they share a belief that the logic of decentralized private markets is the appropriate way of thinking about and organizing the “world of goods.” In other words, mainstream economists debate, often intensely and with no small degree of sneering and sarcasm, the best way of getting markets to operate correctly—but that’s only because they utilize the same basic theory according to which a properly functioning market system is the only appropriate foundation and goal for theory and policy. Market fundamentalism thus represents the utopian horizon of mainstream economics.

The critique of market fundamentalism starts where mainstream economics leaves off—with the idea that the world of goods can and should be organized by markets.*******It highlights the hidden ground of the mainstream theory of markets and calls into question the very possibility of market exchange. The result is a different utopian horizon, which both refuses the self-suturing conception of market value and opens up the realm of possibility for other ways of organizing economic and social life.

When mainstream economists blithely draw the diagram or write down the equations for a market, what they’re doing is presuming—while failing to mention, let alone discuss—a whole host of conditions. Callari focuses on mainstream economists’ “image of the economy as a world of goods, and of the world of goods as a homogeneous field.” Such an image serves as the foundation for the positing of calculable “interests,” which thus become the central code of the economy and society. Within the homogeneous field of goods, every action can be connected with every other action in a measured (that is, analytically calculable) way. Once all the appropriate calculations are completed, “the market”—both individual markets and the market system as a whole—finds its equilibrium, the self-suturing reconciliation of all the competing interests. It also closes off the field of goods to any inspiration or influence other than self-interested rationality—be they traditions, social obligations, or ethical commitments.

Taking up on and extending that point, Amariglio argues that many of the features of non-market transactions involving goods and services (such as the gift) also haunt market exchanges.

There is nothing at all “certain” about any act of exchange, and nothing in it less symbolic or less “about” power, responsibility, meaning, and so forth. Likewise, there is something fundamentally “constituted” and “constituting” about identities and subjectivities in every act of exchange. Leaving aside the question of the multiplicity within selves who enter into trades, the fact remains that exchange is a very overloaded activity, and trading partners not only may be of several different minds about the transaction, but are often uncertain as to what exactly such transactions “mean” in terms of their own or others’ wealth and property, the effects on their well-being, who or what subject positions they occupy, what exactly is being traded, and so forth.

Market exchanges are therefore crosscut—just like any other allocative transaction, be it the gift, planning, or plunder—with a whole host of perturbations and undecidables. Both markets and the interests they are said to represent rely on “external” (historical and social) conditions and are, in different times and spaces, characterized by considerable uncertainty and indeterminacy. And once we begin to investigate those conditions, once we begin to analyze the “openness” of markets, we are forced to confront the ability of any act of exchange—and, for that matter, any economic discourse about markets—to successfully suture itself, at least in any kind of “permanent” act of closure.

The impossibility of market exchange, in general, suggests the need to recognize and attend to the historical and social specificity of individual markets—without any overarching, general theory of price or exchange-value. It also opens the door both to other commitments, whether ethical or political, and to other means of transacting goods and services, as they imply different conditions and consequences for society, for the social relations among persons, things, and nature.

Imagining and enacting those possibilities represent the utopian horizon of the critique of markets and mainstream economists’ theory of the market system.

 

*The Invisible Hand is a rubber copy of the right hand of Leopold II, taken at night from the 1926 sculpture by Thomas Vinçotte, located at the Regentlaan in Brussels, Belgium. The mould was taken to a former rubber plantation in Kasai-Occidental in the Democratic Republic of Congo and filled with natural rubber. The rubber hand was presented at Art Brussels 2015. It refers both to Adam Smith’s theory (as elaborated in the Theory of Moral Sentiments and The Wealth of Nations) and to Leopold II’s use of the International African Association (1877-79) and later the Congo Free State (1885-1908) to pillage the available natural resources. The grotesque result is that, by doing so, he “unwittingly” instigated local economic growth but at a high price: more than 10 million people are estimated to have died as a consequence of Leopold’s “Invisible Hand.” The Invisible Hand also points to the custom of chopping off the hands of enslaved people to ensure the rubber quota. To paraphrase Marx, markets come “dripping from head to foot, from every pore, with blood and dirt.”

**With one notable exception: healthcare.

***The Robinson Crusoe story has been read in a radically different vein by many heterodox economists, including Stephen Hymer and Ulla Grapard.

****Mostly because of Kenneth Arrow’s “Impossibility Theorem,” which challenged the idea that there’s a procedure for deriving a collective or “social” ordering—a Social Welfare Function—based on individual preferences.

*****While mainstream economists can claim to have solved the problem of “existence” (i.e., that there is such a set of prices consistent with overall efficiency), much to their consternation they have not been able to prove either “stability” (that prices, if away from the equilibrium set will move toward the equilibrium) or “uniqueness” (in other words, there may be many such sets of prices).

******That’s why, as I teach my students, there is such a thing as a free lunch: just abolish monopolies and oligopolies, and the economy can increase production (technically, the economy can move from inside to the production possibilities frontier without any additional resources or new technology, just by eliminating imperfect competition).

*******The critique I present here is inspired by two key essays—Antonio Callari’s “The Ghost of the Gift: The Unlikelihood of Economics” and Jack Amariglio’s “Give the Ghost a Chance! A Comrade’s Shadowy Addendum—both published in The Question of the Gift: Essays Across the Disciplines, edited by Mark Osteen. It is also informed by research that appeared in Postmodern Moments in Modern Economics, by Amariglio and myself.

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Special mention

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The business press is having a hard time figuring out this one: the combination of unrelenting drama in and around Donald Trump’s White House and the stability (signaled by the very low volatility) on Wall Street.

As CNN-Money notes,

One of the oldest sayings on Wall Street is that investors hate uncertainty. But that adage, much like other conventional wisdom, is being challenged during the Trump era.

Despite enormous question marks swirling around the fate of President Trump’s economic agenda and his political future, American financial markets have remained unusually calm.

What’s going on?

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What investors actually hate is not uncertainty but, rather, threats to profits. And corporate profits have been growing spectacularly during the recovery from the Second Great Depression. Between the fourth quarter of 2008 and the first quarter of 2017, corporate profits rose more than 150 percent. Meanwhile, U.S. stocks (as measured by the Standard & Poor’s 500) increased by more than 200 percent. The rise in stock prices stems both from the growth in corporate profits and from gains in the stock market itself, which together have fueled further increases in the stock market with steadily declining levels of volatility.

As Ruchir Sharma admits,

Mr. Trump’s mercurial ways may be a source of great concern or indifference, depending on your ideological leanings. But Wall Street doesn’t seem to care one way or other.

What Wall Street cares about is not uncertainty but profits.

That’s the bottom line.

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Mark Tansey, “Coastline Measure” (1987)

The pollsters got it wrong again, just as they did with the Brexit vote and the Colombia peace vote. In each case, they incorrectly predicted one side would win—Hillary Clinton, Remain, and yes—and many of us were taken in by the apparent certainty of the results.

I certainly was. In each case, I told family members, friends, and acquaintances it was quite possible the polls were wrong. But still, as the day approached, I found myself believing the “experts.”

It still seems, when it comes to polling, we have a great deal of difficulty with uncertainty:

Berwood Yost of Franklin & Marshall College said he wants to see polling get more comfortable with uncertainty. “The incentives now favor offering a single number that looks similar to other polls instead of really trying to report on the many possible campaign elements that could affect the outcome,” Yost said. “Certainty is rewarded, it seems.”

But election results are not the only area where uncertainty remains a problematic issue. Dani Rodrik thinks mainstream economists would do a better job defending the status quo if they acknowledged their uncertainty about the effects of globalization.

This reluctance to be honest about trade has cost economists their credibility with the public. Worse still, it has fed their opponents’ narrative. Economists’ failure to provide the full picture on trade, with all of the necessary distinctions and caveats, has made it easier to tar trade, often wrongly, with all sorts of ill effects. . .

In short, had economists gone public with the caveats, uncertainties, and skepticism of the seminar room, they might have become better defenders of the world economy.

To be fair, both groups—pollsters and mainstream economists—acknowledge the existence of uncertainty. Pollsters (and especially poll-based modelers, like one of the best, Nate Silver, as I’ve discussed here and here) always say they’re recognizing and capturing uncertainty, for example, in the “error term.”

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Even Silver, whose model included a much higher probability of a Donald Trump victory than most others, expressed both defensiveness about and confidence in his forecast:

Despite what you might think, we haven’t been trying to scare anyone with these updates. The goal of a probabilistic model is not to provide deterministic predictions (“Clinton will win Wisconsin”) but instead to provide an assessment of probabilities and risks. In 2012, the risks to to Obama were lower than was commonly acknowledged, because of the low number of undecided voters and his unusually robust polling in swing states. In 2016, just the opposite is true: There are lots of undecideds, and Clinton’s polling leads are somewhat thin in swing states. Nonetheless, Clinton is probably going to win, and she could win by a big margin.

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As for the mainstream economists, while they may acknowledge exceptions to the rule that “everyone benefits” from free markets and international trade in some of their models and seminar discussions, they acknowledge no uncertainty whatsoever when it comes to celebrating the current economic system in their textbooks and public pronouncements.

So, what’s the alternative? They (and we) need to find better ways of discussing and possibly “modeling” uncertainty. Since the margins of error, different probabilities, and exceptions to the rule are ways of hedging their bets anyway, why not just discuss the range of possible outcomes and all of what is included and excluded, said and unsaid, measurable and unmeasurable, and so forth?

The election pollsters and statisticians may claim the public demands a single projection, prediction, or forecast. By the same token, the mainstream economists are no doubt afraid of letting the barbarian critics through the gates. In both cases, the effect is to narrow the range of relevant factors and the likelihood of outcomes.

One alternative is to open up the models and develop a more robust language to talk about fundamental uncertainty. “We simply don’t know what’s going to happen.” In both cases, that would mean presenting the full range of possible outcomes (including the possibility that there can be still other possibilities, which haven’t been considered) and discussing the biases built into the models themselves (based on the assumptions that have been used to construct them). Instead of the pseudo-rigor associated with deterministic predictions, we’d have a real rigor predicated on uncertainty, including the uncertainty of the modelers themselves.

Admitting that they (and therefore we) simply don’t know would be a start.

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Leicester City was not going to win the Premiership—not by a long shot. Nor was the Republican nomination supposed to be handed to Donald Trump. And Bernie Sanders, well, there was no chance he was going to give Hillary Clinton a serious run for her money (and machine) in the Democratic primaries.

And yet here we are.

Leicester City Football Club, as anyone who has even a fleeting interest in sports (or reads one or another major newspaper or news outlet) knows, were just crowned champions of the Premiership, the highest tier of British football, after starting the season at 5000-1 odds. There really is no parallel in the world of sports—any sport, in any country. (By way of comparison, Donerail, with odds of 91-1 in 1913, is the longest odds winner in Kentucky Derby history.) And the bookies are now being forced to pay up.

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Similarly, Donald Trump was not supposed to win the Republican nomination. Instead, it was going to go to Jeb Bush and, if he failed, to Marco Rubio. (And certainly Ted Cruz, the candidate most reviled by other members of the GOP, was not supposed to be there at the end.)

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Finally, Bernie Sanders’s campaign for the Democratic nomination was written off almost as soon as it was launched. And yet here is—winning the Indiana contest by 5 points (when it was predicted he would lose by the same number of points) and accumulating enough pledged delegates to be him within a couple of hundred of the presumptive nominee.

What’s going on?

In all three cases, the presumption was that the “system” would prevent such an unlikely occurrence, and that the pundits and prognosticators “knew” from early on the likely outcome.

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So, for example, the winner of the Premiership was supposed to come from one of the perennial top four (Manchester United, Chelsea, Arsenal, and Manchester City)—not a club that were only promoted from the second division of British football in 2014 and last April were battling relegation (they finished the season 14th).

Pretty much the same is true in the political arena: neither Trump nor Sanders was taken particularly seriously at the start, and along the way the prevailing common sense was that their campaigns would simply implode or wither away. The idea was that the Republican and Democratic parties and nominating contests were structured so that their preferred nominees would inexorably come out on top.

There are, I think, two lessons to take away from these bolts from the blue. First, the “system,” however defined, is much less complete and determined than people usually think. There are many fissures and spaces in such systems that make what are seemingly unlikely outcomes real possibilities. Second, our presumably certain “knowledges” are exactly that, knowledges, which are constructed—in the face of radical uncertainty—out of theories, presumptions, blind spots, and much else. The fact is, we simply don’t know, and no amount of probabilistic certainty can overcome that epistemological gap.

So—surprise, surprise—Leicester City and Trump won, while Sanders has put up a much more formidable challenge than anyone expected from a socialist presidential candidate in the United States.

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Uncertainty, as we wrote years ago, is a real problem. Not a problem in and of itself. But it’s certainly a problem for modernist thinking.

That’s why, time and gain, neoclassical economists have attempted to reduce uncertainty to probabilistic certainty. It also seems to be why a team of scientists (neurobiologists and others) [ht: ja] have devised an experiment to show that we’re hardwired to experience stress under uncertainty.

So what’s the big deal? Everyone knows that uncertainty is stressful. But what’s not so obvious is that uncertainty is more stressful than predictable negative consequences. Is it really more stressful wondering whether you’ll make it to your meeting on time than knowing you’ll be late? Is it more stressful wondering if you’re about to get sacked than being relatively sure of it? De Berker’s results provide a resounding “yes”.

There are two problems with this approach. First, it ignores the possibility that uncertainty is a discursive phenomenon—that the stories we tell about uncertainty affect how we experience it. Second, uncertainty in and of itself need not be stressful. There are plenty of instances in which the outcome is simply unknown—from sitting down to write a paper to starting a new investment project, from starting a new relationship to participating in a political movement—when our uncertainty about what might happen is precisely what propels us forward.

Sure, turning over rocks that might have snakes hidden under them would probably induce stress. But that’s not because of the uncertainty; it’s because they’re snakes! (And, even then, I have herpetologist friends who would be delighted to find those snakes.)

Let’s just say I’m not convinced of the project to domesticate and control uncertainty, either by reducing it to a probabilistic calculus or to locate it in the brain (as part of some evolutionary process).

There’s lots of uncertainty out there but what it is and how we respond to it depend on the stories we tell (as I have written about many times on this blog). Uncertainty, in other words, is always and everywhere a discursive phenomenon.

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One of the most studied issues in contemporary economics is the effect of an increase in the minimum wage. But here we have a panel of so-called experts composed of mainstream economists who are uncertain—about whether employment will decrease or output will increase.

Ordinarily, I would applaud a health dose of uncertainty among economists, especially mainstream economists.

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But, of course, mainstream economists show themselves to be quite certain about things other than the minimum wage, such as the idea that the median American household, notwithstanding the small increase in household income, is actually much better off.

Just sayin’. . .

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I’ve been listening to and reading lots of financial pundits over the course of the past week—all of whom use the same lingo (the U.S. economy as the “cleanest shirt in the hamper,” the “deterioration in risk appetite” around the globe, and so on) and try to explain the volatility of the stock markets in terms of economic “fundamentals” (like the slowing of the Chinese economy, the prospect of deflation in Europe, and so on).

Me, I’m much more inclined to think of terms of uncertainty, unknowability, and “shit happens.”

Let’s face it: stock markets are speculative markets, in the sense that individual and institutional investors are always speculating (with the aid of computer programs) about how others view the market in order to make their bets—with fundamental uncertainty, unknowability, and the idea that shit happens. That is, they have hunches, and they have no idea if their hunches are correct until others respond—with the same amount of uncertainty, unknowability, and the idea that shit happens. And then all of them make up stories (using the lingo of the day and often referring to changes in the “fundamentals”) after the fact, to justify whatever actions they took and their advice to others.

That’s pretty much the view outlined by Robert Shiller. It’s all about stories characterized by uncertainty, unknowability, and shit happens.

In general, bubbles appear to be associated with half-baked popular stories that inspire investor optimism, stories that can neither be proved nor disproved. . .

the proliferation of such stories is a natural part of economic equilibrium. Successful people who value their careers rely on an instinctive sense for what pitch will sell. Who knows what the truth is, anyway?

As time goes on, the stories justifying investor optimism become increasingly shopworn and criticized, and people find themselves doubting them more and more. Even though people are asking themselves if prices are too high, they are slow to take action to sell. When prices make a sudden drop, as they did in recent days, people tend to become fearful, even if there is a subsequent rebound. With the drop they suddenly realize that their views might be shared by other people, and start looking for information that might confirm their belief. Some are driven to sell immediately. Others are slower, but they are all similarly motivated. The result is an irregular but large stock market decline over a year or more. . .

It is entirely plausible that the shaking of investor complacency in recent days will, despite intermittent rebounds, take the market down significantly and within a year or two restore CAPE ratios to historical averages. This would put the S. & P. closer to 1,300 from around 1,900 on Wednesday, and the Dow at 11,000 from around 16,000. They could also fall further; the historical average is not a floor.

Or maybe this could be another 1998. We have no statistical proof. We are in a rare and anxious “just don’t know” situation, where the stock market is inherently risky because of unstable investor psychology.

I would only add one correction: we always “just don’t know”—not just in anxious situations of volatility (such as during the past week), but also in more stable periods. In fact, we don’t even know if we’re in a volatile or stable period (until a new story becomes the common sense that what we’ve been through was volatile or stable) and we certainly don’t know how a stable situation becomes volatile (and vice versa).

Really, all we can say, when it comes to bubbles, is: shit happens.

12wage

Until recently, we were certain what would happen with an increase in the minimum wage—and that would be the reason to oppose any and all such attempts. Now, it’s a guessing game—and that uncertainty about its possible effects has become reason enough to oppose increasing the minimum wage.

What the hell is going on?

minimum_wage

First, the certainty: neoclassical economists confidently asserted that the minimum wage caused unemployment (because it meant, at a wage above the equilibrium wage, the quantity supplied of labor would be created than the quantity demanded). Therefore, any increase in the minimum wage would cause more unemployment and, despite the best intentions of people who wanted to raise the minimum wage, it would actually hurt the poor, since many would lose their jobs.

But, of course, theoretically, the neoclassical labor-market model was missing all kinds of other effects, from wage efficiencies (e.g., higher wages might reduce labor turnover and increase productivity) to market spillovers (e.g., higher wages might lead to more spending, which would in turn increase the demand for labor). If you take those into account, the effects of increasing the minimum wage became more uncertain: it might or might not lead to some workers losing their jobs but those same workers might get jobs elsewhere as economic activity picked up precisely because workers who kept their jobs might be more productive and spend more of their higher earnings.

And that’s precisely what the new empirical studies have concluded: some have find a little less employment, others a bit more employment. In the end, the employment effects are pretty much a wash—and workers are receiving higher wages.

But that’s mostly for small increases in the minimum wage. What if the increase were larger—say, from $7.25 to $10, $12, or $15 an hour?

Well, we just don’t know. All we can do is guess what the effects might be at the local, state, or national level. But conservatives (like David Brooks, big surprise!) are seizing on that uncertainty to oppose increasing the minimum wage.

And that’s what I find interesting: uncertainty, which was at one time (e.g., for conservatives like economist Frank Knight) the spur to action, is now taken to be the reason for inaction. And those who oppose increasing the minimum wage are now choosing the certainty of further misery for minimum-wage workers over the uncertainty of attempting to improve their lot.

Addendum

They want less of a guessing game?

Then, let’s make the effects of raising the minimum wage more certain. Why not increase government expenditures in areas where raising the minimum wage represents a dramatic increase for workers? Or mandate that employers can’t fire any of the low-wage workers once the minimum wage is increased? Or, if an employer chooses to close an enterprise rather than pay workers more, hand the enterprise over to the workers themselves? Any or all of those measures would increase the certainty of seeing positive effects for the working poor of raising the minimum wage.

But then we’re talking about a different game—of capital versus labor, of profits versus wages. And we know, with a high degree of certainty, the choices neoclassical economists and conservative pundits make in that game.