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But the headline-grabbing differences between the findings of these Nobel laureates are less significant than the profound agreement in their scientific approach to economic questions, which is characterized by formulating and testing precise hypotheses

I’m troubled by the sense among skeptics that disagreements about the answers to certain questions suggest that economics is a confused discipline, a fake science whose findings cannot be a useful basis for making policy decisions.

It is true that the answers to many “big picture” macroeconomic questions — like the causes of recessions or the determinants of growth — remain elusive.

As is the case with epidemiologists, the fundamental challenge faced by economists — and a root cause of many disagreements in the field — is our limited ability to run experiments

economists have recently begun to overcome these challenges by developing tools that approximate scientific experiments to obtain compelling answer

Other economic studies have taken advantage of the constraints inherent in a particular policy to obtain scientific evidence

Even when such experiments are unfeasible, there are ways to use “big data” to help answer policy questions

That’s the latest among several recent studies by economists working independently, all arriving at similar findings: A few “superstar firms” have grown to dominate their industries, crowding out competitors and controlling markets to a degree not seen in many decades.

Let’s look beyond such obvious winner-take-all examples as Apple or Alphabet, the parent of Google.

Consider real-estate services. In 1997, according to Profs. Grullon, Larkin and Michaely, that sector had 42 publicly traded companies; the four largest generated 49% of the group’s total revenue. By 2014, only 20 public firms were left, and the top four— CBRE Group, Jones Lang LaSalle, Realogy Holdings and Wyndham Worldwide—commanded 78% of the group’s combined revenue.

Or look at supermarkets. In 1997, there were 36 publicly traded companies in that industry, with the top four accounting for more than half of total sales. By 2014, only 11 were left. The top four—Kroger, Supervalu, Whole Foods Market and Roundy’s (since acquired by Kroger)—held 89% of the pie.

The U.S. had more than 7,000 public companies 20 years ago, the professors say; nowadays, it’s fewer than 4,000.

The winners are also grabbing most of the profits

At the end of 1996, the 25 companies in the S&P 500 with the highest net profit margins—income as a percentage of revenue—earned a median of just under 21 cents on every dollar of sales. Last year, the top 25 such companies earned a median of 39 cents on the dollar.

Two decades ago, the median net margin among all S&P 500 members was 6.7%. By the end of 2016, that had increased to 9.7%.

So while companies as a whole became more profitable over the past 20 years, the winners have become vastly more profitable, nearly doubling the gains they got on each dollar of sales.

Why might it be easier now for winners to take all? Prof. Michaely suggests two theories. Declining enforcement of antitrust rules has led to bigger mergers, less competition and higher profits.

The other is technology. “If you want to compete with Google or Amazon,” he says, “you’ll have to invest not just billions, but tens of billions of dollars.”

Still, history offers a warning. Many times in the past, winners have taken all but seldom for long.

it also explained why committees have so much trouble coming to consistent conclusions and why, with an increasingly polarized electorate, democracy can become increasingly dysfunctional.

until Kenneth drew on the techniques of topology (that is, the study of geometric properties and spatial relations), no one had ever been able to establish precise conditions under which there would be prices that would clear all markets, or under which one could assume that the market outcome was optimal

in the early 1950s, he clarified the very specific conditions under which market outcomes were for the best and, of equal importance, the far more general conditions under which public interventions in markets had the potential to make things better.

The idea is that because people and environments are always changing, partners must provide one another with enough corrective feedback so they can be “on the same page.” 

Gottman and his colleagues found that couples who remained stoic during conflicts actually tended to fare worse than couples that were more “volatile".

These couples exert a healthy amount of influence on one another, both positively and negatively. But as long as their interactions favor the positive, they tend to enjoy relatively stable relationships over the long term.

The 5:1 ratio also seems to ring true in the business world.

The results showed that the most successful teams made an average of 5.6 positive comments per every negative one, while the average ratio among the lowest performing teams was just 0.36 to 1.

Negative feedback can prevent you from driving off a cliff.

This failing in the West’s economies is also a failing of economics

many people have long felt the desire to do something with their lives besides consuming goods and having leisure. They desire to participate in a community in which they can interact and develop.

Our prevailing political economy is blind to the very concept of inclusion; it does not map out any remedy for the deficiency

injustice of another sort. Workers in decent jobs view the economy as unjust if they or their children have virtually no chance of climbing to a higher rung in the socioeconomic ladder

“Money is like blood. You need it to live but it isn’t the point of life.”4

justice is not everything that people need from their economy. They need an economy that is good as well as just. And for some decades, the Western economies have fallen short of any conception of a “good economy”—an economy offering a “good life,” or a life of “richness,” as some humanists call it

The good life as it is popularly conceived typically involves acquiring mastery in one’s work, thus gaining for oneself better terms—or means to rewards, whether material, like wealth, or nonmaterial—an experience we may call “prospering.”

As humanists and philosophers have conceived it, the good life involves using one’s imagination, exercising one’s creativity, taking fascinating journeys into the unknown, and acting on the world—an experience I call “flourishing.”

prospering and flourishing became prevalent in the nineteenth century when, in Europe and America, economies emerged with the dynamism to generate their own innovation.

What is the mechanism of the slowdown in productivity

prospering

In nineteenth-century Britain and America, and later Germany and France, a culture of exploration, experimentation, and ultimately innovation grew out of the individualism of the Renaissance, the vitalism of the Baroque era, and the expressionism of the Romantic period.

What made innovating so powerful in these economies was that it was not limited to elites. It permeated society from the less advantaged parts of the population on up.

High-enough wages, low-enough unemployment, and wide-enough access to engaging work are necessary for a “good-enough” economy—though far from sufficient. The material possibilities of the economy must be adequate for the nonmaterial possibilities to be widespread—the satisfactions of prospering and of flourishing through adventurous, creative, and even imaginative work.

today’s standard economics. This economics, despite its sophistication in some respects, makes no room for economies in which people are imagining new products and using their creativity to build them. What is most fundamentally “wrong with economics” is that it takes such an economy to be the norm—to be “as good as it gets.”

ince around 1970, or earlier in some cases, most of the continental Western European economies have come to resemble more completely the mechanical model of standard economics. Most companies are highly efficient. Households, apart from the very low-paid or unemployed, have gone on saving

In most of Western Europe, economic dynamism is now at lows not seen, I would judge, since the advent of dynamism in the nineteenth century. Imagining and creating new products has almost disappeared from the continent

The bleak levels of both unemployment and job satisfaction in Europe are testimony to its dreary economies.

a recent survey of household attitudes found that, in “happiness,” the median scores in Spain (54), France (51), Italy (48), and Greece (37) are all below those in the upper half of the nations labeled “emerging”—Mexico (79), Venezuela (74), Brazil (73), Argentina (66), Vietnam (64), Colombia (64), China (59), Indonesia (58), Chile (58), and Malaysia (56)

The US economy is not much better. Two economists, Stanley Fischer and Assar Lindbeck, wrote of a “Great Productivity Slowdown,” which they saw as beginning in the late 1960s.11 The slowdown in the growth of capital and labor combined—what is called “total factor productivity”—is star

though the injustices in the West’s economies are egregious, they ought not to be seen as a major cause of the productivity slowdowns and globalization. (For one thing, a slowdown of productivity started in the US in the mid-1960s and the sharp loss of manufacturing jobs to poorer countries occurred much later—from the late 1970s to the early 1990s.) Deeper causes must be at work.

The plausible explanation of the syndrome in America—the productivity slowdown and the decline of job satisfaction, among other things—is a critical loss of indigenous innovation in the established industries like traditional manufacturing and services that was not nearly offset by the innovation that flowered in a few new industries

hat then caused this narrowing of innovation? No single explanation is persuasive. Yet two classes of explanations have the ring of truth. One points to suppression of innovation by vested interests

some professions, such as those in education and medicine, have instituted regulation and licensing to curb experimentation and change, thus dampening innovation

established corporations—their owners and stakeholders—and entire industries, using their lobbyists, have obtained regulations and patents that make it harder for new firms to gain entry into the market and to compete with incumbents.

The second explanation points to a new repression of potential innovators by families and schools. As the corporatist values of control, solidarity, and protection are invoked to prohibit innovation, traditional values of conservatism and materialism are often invoked to inhibit a young person from undertaking an innovation.

ow might Western nations gain—or regain—widespread prospering and flourishing? Taking concrete actions will not help much without fresh thinking: people must first grasp that standard economics is not a guide to flourishing—it is a tool only for efficiency.

Widespread flourishing in a nation requires an economy energized by its own homegrown innovation from the grassroots on up. For such innovation a nation must possess the dynamism to imagine and create the new—economic freedoms are not sufficient. And dynamism needs to be nourished with strong human values.

a reform of education stands out. The problem here is not a perceived mismatch between skills taught and skills in demand

The problem is that young people are not taught to see the economy as a place where participants may imagine new things, where entrepreneurs may want to build them and investors may venture to back some of them. It is essential to educate young people to this image of the economy.

It will also be essential that high schools and colleges expose students to the human values expressed in the masterpieces of Western literature, so that young people will want to seek economies offering imaginative and creative careers. Education systems must put students in touch with the humanities in order to fuel the human desire to conceive the new and perchance to achieve innovations

This reorientation of general education will have to be supported by a similar reorientation of economic education.

Yet obviously something is deeply wrong with economics. While economists using textbook macro models got things mostly and impressively right, many famous economists refused to use those models — in fact, they made it clear in discussion that they didn’t understand points that had been worked out generations ago.

Moreover, it’s hard to find any economists who changed their minds when their predictions, say of sharply higher inflation, turned out wrong.

let’s grant that economics as practiced doesn’t look like a science. But that’s not because the subject is inherently unsuited to the scientific method. Sure, it’s highly imperfect — it’s a complex area, and our understanding is in its early stages.

And sure, the economy itself changes over time, so that what was true 75 years ago may not be true today — although what really impresses you if you study macro, in particular, is the continuity, so that Bagehot and Wicksell and Irving Fisher and, of course, Keynes remain quite relevant today.

No, the problem lies not in the inherent unsuitability of economics for scientific thinking as in the sociology of the economics profession — a profession that somehow, at least in macro, has ceased rewarding research that produces successful predictions and rewards research that fits preconceptions

This is not what the G.O.P. seems inclined to do. Instead it lets lukewarmers poke holes in liberal proposals for climate insurance policies, and then sits back satisfied that no insurance policy, no extra effort, is necessary at all.

the anti-Paris sentiments that moved Trump weren’t entirely reality-based either. And a clear Republican plan for how to “prepare for and adapt to whatever climate change brings” does not actually exist.

In its absence, lukewarmism is a critique without an affirmative agenda, a theory of the case without a party that’s prepared to ever act on it.

I also want to concede two problems with this approach. The first is that no less than alarmism, lukewarmism can be vulnerable to cherry-picking and selection bias

when you’re dealing with long-term trends, there’s a lot of evidence to choose from

This means that every lukewarmer, including especially those in positions of political authority, should be pressed to identify trends that would push them toward greater alarmism and a sharper focus on the issue.

the closer the real trend gets to the worst-case projections, the more my lukewarmism will look Pollyannish and require substantial reassessment.

Tyson praises science as an “exercise in finding what is true” that’s based on peer-reviewed experimentation backed by other experiments and counter-experiments that gives birth to an “emergent truth.” He points out that science is “not something to toy with.” “You can’t say, ‘I chose not to believe in E=mc2,’” he says, referring to physicist Albert Einstein’s corroborated theory of special relativity. “You don’t have that option. It is true, whether or not you believe in it.”

Tyson warns that every minute someone is in denial of a scientific truth delays the “political solution that should have been established years ago.”  “Recognize what science is, and allow to be what it can and should be: In the service of civilization,” he says. “It’s in our hands.”

But Marshall demonstrated that capitalism advances not by immiserating the poor, but by boosting productivity. Factory owners make relentless small improvements that allow them to produce both higher wages and lower prices, thereby spreading the gains of material progress throughout society.

Schumpeter further expanded the idea of productivity increases. The economy doesn't simply get bigger and bigger. It goes through a constant process of discombobulation as entrepreneurs invent new products and processes.

Marx got it upside down: capitalism's recurrent crises actually make it stronger.

The things you do with your life are mostly chosen: your job, spouse and hobbies.

At our foundation, we were a society with strong covenantal attachments — to family, community, creed and faith. Then on top of them we built democracy and capitalism that celebrated liberty and individual rights.

The deep covenantal institutions gave people the capacity to use their freedom well. The liberal institutions gave them that freedom.

This delicate balance — liberal institutions built atop illiberal ones — is now giving way. The big social movements of the past half century were about maximizing freedom of choice. Right-wingers wanted to maximize economic choice and left-wingers lifestyle choice. Anything that smacked of restraint came to seem like a bad thing to be eliminated.

We’ll call this worldview — which is all freedom and no covenant — naked liberalism

The problem with naked liberalism is that it relies on individuals it cannot create.

Naked liberals of right and left assume that if you give people freedom they will use it to care for their neighbors, to have civil conversations, to form opinions after examining the evidence. But if you weaken family, faith, community and any sense of national obligation, where is that social, emotional and moral formation supposed to come from? How will the virtuous habits form?

Naked liberalism has made our society an unsteady tree. The branches of individual rights are sprawling, but the roots of common obligation are withering away.

Freedom without covenant becomes selfishness. And that’s what we see at the top of society, in our politics and the financial crisis

In my experience, most people under 40 get this. They sense the social and moral void at the core and that change has to come at the communal, emotional and moral level.

And that’s what we see at the bottom of society — frayed communities, broken families, opiate addiction.

Freedom without a unifying national narrative becomes distrust, polarization and permanent political war.

covenantal attachments they become fragile. Moreover, if you rob people of their good covenantal attachments, they will grab bad ones.

First, they will identify themselves according to race. They will become the racial essentialists you see on left and right

Then they resort to tribalism. This is what Donald Trump provides. As Mark S. Weiner writes on the Niskanen Center’s blog, Trump is constantly making friend/enemy distinctions, exploiting liberalism’s thin conception of community and creating toxic communities based on in-group/out-group rivalry.

Trump offers people cultural solutions to their alienation problem. As history clearly demonstrates, people will prefer fascism to isolation, authoritarianism to moral anarchy.

If we are going to have a decent society we’re going to have to save liberalism from itself. We’re going to have to restore and re-enchant the covenantal relationships that are the foundation for the whole deal. The crucial battleground is cultural and prepolitical.

Freedom without connection becomes alienation.

Many public intellectuals were trained in the social sciences and take the choosing individual as their mental starting point. They have trouble thinking about our shared social and moral formative institutions and how such institutions could be reconstituted.

Congressional Republicans think a successful tax bill will thwart populism. Mainstream Democrats think the alienation problem will go away if we redistribute the crumbs a bit more widely.

History is full of examples of nations that built new national narratives, revived family life, restored community bonds and shared moral culture: Britain in the early 19th century, Germany after World War II, America in the Progressive Era. The first step in launching our own revival is understanding that the problem is down in the roots.

The findings suggest that a lot of the time, energy, and money poured into traditional campaigning methods is wasted, and that the campaign operatives hawking tried-and-true tactics don’t have the evidence to back up their claims.

It also casts doubt on the theory of the swing voter who can be persuaded with enough flyers, ad exposure, and conversations with earnest volunteers

In reality, Broockman and Kalla find, direct outreach is most effective at improving voter turnout, suggesting that campaigns should focus on getting their core supporters to the polls than reaching out to a mythical middle.

This new study suggests that intentionally curated, issue-specific persuasion campaigns may shift people’s views more easily than partisan political campaigns.

Emma Green: So, is political campaigning useless?Joshua Kalla: The short answer is ‘no.

There are lots of things that campaigning can accomplish. Two decades of research on voter registration and hundreds of field experiments show really cost-effective ways to increase turnout in the base.But on persuasion, yes, we find that on average, there are very small effects.

Kalla: A lot of campaign operatives think there’s this big pool of moderate, undecided voters that we can spend money on to persuade them to our side. That strategy is probably not the right strategy. And we should be skeptical of big claims of persuasion.

Kalla: All the money is being poured into the same time and the same place. It’s hard to imagine that the hundredth TV ad that a person views is really worth it from a monetary perspective, versus that same money spent in a different race or a lower race. There’s a case to be made that too much money is being spent in the same ways and on the same people.

But the takeaway from this paper should not be that campaigns should stop. Campaigns do a lot of work that is measurable in return on getting voters to vote, and persuading voters. It’s just a question of how the money is spent.

Kalla: The first order of understanding an election and how people vote is partisan identity. Most people vote based on whether there’s a D or an R next to their name. Unpacking that should be more the focus than the horserace.

We don’t see persuasive effects in general elections where a Democrat is talking to a Republican. But in ballot-measure campaigns and primaries and the transgender work, it seems that persuasion is possible.

Most Americans view themselves in a partisan lens. When it comes time to vote, it’s less a function of a person running for office than a person with a party label beside his or her name.

Green: But what about the roughly 39 percent of Americans who identify as independents?Kalla: A lot of independents tend to be what political scientists term as “closeted partisans.” They might not explicitly identify with a party, but if you ask them which party they lean toward, they’ll often give you an answer. Their behavior tends to look a lot like the behavior of people who explicitly identify as partisans.

Green: Our democracy is based on this romantic idea that encounters in the public square—conversations, essays, speeches, etc.— have the power to change how people view the world. If you’re saying that’s basically not true, where does that leave us? Are we all just destined to remain isolated in the prisons of our own convictions?

Kalla: I want to draw a distinction with the transgender canvassing work. That was very much focusing on getting people to be introspective and think about times that they or their loved ones have been discriminated against, and how that made them feel, and how that real, lived experience informs their views on non-discrimination laws and views toward LGBT people. That’s close to an ideal of how we want democracy to function.

That’s not the type of discourse you see in campaigns. I don’t think TV ads or every glossy postcard is really going to lead to enlightened discourse among the American public.

Code is too hard to think about. Before trying to understand the attempts themselves, then, it’s worth understanding why this might be: what it is about code that makes it so foreign to the mind, and so unlike anything that came before it.

Technological progress used to change the way the world looked—you could watch the roads getting paved; you could see the skylines rise. Today you can hardly tell when something is remade, because so often it is remade by code.

Software has enabled us to make the most intricate machines that have ever existed. And yet we have hardly noticed, because all of that complexity is packed into tiny silicon chips as millions and millions of lines of cod

The programmer, the renowned Dutch computer scientist Edsger Dijkstra wrote in 1988, “has to be able to think in terms of conceptual hierarchies that are much deeper than a single mind ever needed to face before.” Dijkstra meant this as a warning.

As programmers eagerly poured software into critical systems, they became, more and more, the linchpins of the built world—and Dijkstra thought they had perhaps overestimated themselves.

What made programming so difficult was that it required you to think like a computer.

“The problem is that software engineers don’t understand the problem they’re trying to solve, and don’t care to,” says Leveson, the MIT software-safety expert. The reason is that they’re too wrapped up in getting their code to work.

Though he runs a lab that studies the future of computing, he seems less interested in technology per se than in the minds of the people who use it. Like any good toolmaker, he has a way of looking at the world that is equal parts technical and humane. He graduated top of his class at the California Institute of Technology for electrical engineering,

“The serious problems that have happened with software have to do with requirements, not coding errors.” When you’re writing code that controls a car’s throttle, for instance, what’s important is the rules about when and how and by how much to open it. But these systems have become so complicated that hardly anyone can keep them straight in their head. “There’s 100 million lines of code in cars now,” Leveson says. “You just cannot anticipate all these things.”

a nearly decade-long investigation into claims of so-called unintended acceleration in Toyota cars. Toyota blamed the incidents on poorly designed floor mats, “sticky” pedals, and driver error, but outsiders suspected that faulty software might be responsible

software experts spend 18 months with the Toyota code, picking up where NASA left off. Barr described what they found as “spaghetti code,” programmer lingo for software that has become a tangled mess. Code turns to spaghetti when it accretes over many years, with feature after feature piling on top of, and being woven around

Using the same model as the Camry involved in the accident, Barr’s team demonstrated that there were actually more than 10 million ways for the onboard computer to cause unintended acceleration. They showed that as little as a single bit flip—a one in the computer’s memory becoming a zero or vice versa—could make a car run out of control. The fail-safe code that Toyota had put in place wasn’t enough to stop it

. In all, Toyota recalled more than 9 million cars, and paid nearly $3 billion in settlements and fines related to unintended acceleration.

The problem is that programmers are having a hard time keeping up with their own creations. Since the 1980s, the way programmers work and the tools they use have changed remarkably little.

“Visual Studio is one of the single largest pieces of software in the world,” he said. “It’s over 55 million lines of code. And one of the things that I found out in this study is more than 98 percent of it is completely irrelevant. All this work had been put into this thing, but it missed the fundamental problems that people faced. And the biggest one that I took away from it was that basically people are playing computer inside their head.” Programmers were like chess players trying to play with a blindfold on—so much of their mental energy is spent just trying to picture where the pieces are that there’s hardly any left over to think about the game itself.

The fact that the two of them were thinking about the same problem in the same terms, at the same time, was not a coincidence. They had both just seen the same remarkable talk, given to a group of software-engineering students in a Montreal hotel by a computer researcher named Bret Victor. The talk, which went viral when it was posted online in February 2012, seemed to be making two bold claims. The first was that the way we make software is fundamentally broken. The second was that Victor knew how to fix it.

This is the trouble with making things out of code, as opposed to something physical. “The complexity,” as Leveson puts it, “is invisible to the eye.”

in early 2012, Victor had finally landed upon the principle that seemed to thread through all of his work. (He actually called the talk “Inventing on Principle.”) The principle was this: “Creators need an immediate connection to what they’re creating.” The problem with programming was that it violated the principle. That’s why software systems were so hard to think about, and so rife with bugs: The programmer, staring at a page of text, was abstracted from whatever it was they were actually making.

“Our current conception of what a computer program is,” he said, is “derived straight from Fortran and ALGOL in the late ’50s. Those languages were designed for punch cards.”

WYSIWYG (pronounced “wizzywig”) came along. It stood for “What You See Is What You Get.”

Victor’s point was that programming itself should be like that. For him, the idea that people were doing important work, like designing adaptive cruise-control systems or trying to understand cancer, by staring at a text editor, was appalling.

With the right interface, it was almost as if you weren’t working with code at all; you were manipulating the game’s behavior directly.

When the audience first saw this in action, they literally gasped. They knew they weren’t looking at a kid’s game, but rather the future of their industry. Most software involved behavior that unfolded, in complex ways, over time, and Victor had shown that if you were imaginative enough, you could develop ways to see that behavior and change it, as if playing with it in your hands. One programmer who saw the talk wrote later: “Suddenly all of my tools feel obsolete.”

hen John Resig saw the “Inventing on Principle” talk, he scrapped his plans for the Khan Academy programming curriculum. He wanted the site’s programming exercises to work just like Victor’s demos. On the left-hand side you’d have the code, and on the right, the running program: a picture or game or simulation. If you changed the code, it’d instantly change the picture. “In an environment that is truly responsive,” Resig wrote about the approach, “you can completely change the model of how a student learns ... [They] can now immediately see the result and intuit how underlying systems inherently work without ever following an explicit explanation.” Khan Academy has become perhaps the largest computer-programming class in the world, with a million students, on average, actively using the program each month.

The ideas spread. The notion of liveness, of being able to see data flowing through your program instantly, made its way into flagship programming tools offered by Google and Apple. The default language for making new iPhone and Mac apps, called Swift, was developed by Apple from the ground up to support an environment, called Playgrounds, that was directly inspired by Light Table.

“Typically the main problem with software coding—and I’m a coder myself,” Bantegnie says, “is not the skills of the coders. The people know how to code. The problem is what to code. Because most of the requirements are kind of natural language, ambiguous, and a requirement is never extremely precise, it’s often understood differently by the guy who’s supposed to code.”

In a pair of later talks, “Stop Drawing Dead Fish” and “Drawing Dynamic Visualizations,” Victor went one further. He demoed two programs he’d built—the first for animators, the second for scientists trying to visualize their data—each of which took a process that used to involve writing lots of custom code and reduced it to playing around in a WYSIWYG interface.

Victor suggested that the same trick could be pulled for nearly every problem where code was being written today. “I’m not sure that programming has to exist at all,” he told me. “Or at least software developers.” In his mind, a software developer’s proper role was to create tools that removed the need for software developers. Only then would people with the most urgent computational problems be able to grasp those problems directly, without the intermediate muck of code.

Victor implored professional software developers to stop pouring their talent into tools for building apps like Snapchat and Uber. “The inconveniences of daily life are not the significant problems,” he wrote. Instead, they should focus on scientists and engineers—as he put it to me, “these people that are doing work that actually matters, and critically matters, and using really, really bad tools.”

Bantegnie’s company is one of the pioneers in the industrial use of model-based design, in which you no longer write code directly. Instead, you create a kind of flowchart that describes the rules your program should follow (the “model”), and the computer generates code for you based on those rules

In a model-based design tool, you’d represent this rule with a small diagram, as though drawing the logic out on a whiteboard, made of boxes that represent different states—like “door open,” “moving,” and “door closed”—and lines that define how you can get from one state to the other. The diagrams make the system’s rules obvious: Just by looking, you can see that the only way to get the elevator moving is to close the door, or that the only way to get the door open is to stop.

. In traditional programming, your task is to take complex rules and translate them into code; most of your energy is spent doing the translating, rather than thinking about the rules themselves. In the model-based approach, all you have is the rules. So that’s what you spend your time thinking about. It’s a way of focusing less on the machine and more on the problem you’re trying to get it to solve.

“Everyone thought I was interested in programming environments,” he said. Really he was interested in how people see and understand systems—as he puts it, in the “visual representation of dynamic behavior.” Although code had increasingly become the tool of choice for creating dynamic behavior, it remained one of the worst tools for understanding it. The point of “Inventing on Principle” was to show that you could mitigate that problem by making the connection between a system’s behavior and its code immediate.

On this view, software becomes unruly because the media for describing what software should do—conversations, prose descriptions, drawings on a sheet of paper—are too different from the media describing what software does do, namely, code itself.

for this approach to succeed, much of the work has to be done well before the project even begins. Someone first has to build a tool for developing models that are natural for people—that feel just like the notes and drawings they’d make on their own—while still being unambiguous enough for a computer to understand. They have to make a program that turns these models into real code. And finally they have to prove that the generated code will always do what it’s supposed to.

tice brings order and accountability to large codebases. But, Shivappa says, “it’s a very labor-intensive process.” He estimates that before they used model-based design, on a two-year-long project only two to three months was spent writing code—the rest was spent working on the documentation.

uch of the benefit of the model-based approach comes from being able to add requirements on the fly while still ensuring that existing ones are met; with every change, the computer can verify that your program still works. You’re free to tweak your blueprint without fear of introducing new bugs. Your code is, in FAA parlance, “correct by construction.”

“people are not so easily transitioning to model-based software development: They perceive it as another opportunity to lose control, even more than they have already.”

The bias against model-based design, sometimes known as model-driven engineering, or MDE, is in fact so ingrained that according to a recent paper, “Some even argue that there is a stronger need to investigate people’s perception of MDE than to research new MDE technologies.”

“Human intuition is poor at estimating the true probability of supposedly ‘extremely rare’ combinations of events in systems operating at a scale of millions of requests per second,” he wrote in a paper. “That human fallibility means that some of the more subtle, dangerous bugs turn out to be errors in design; the code faithfully implements the intended design, but the design fails to correctly handle a particular ‘rare’ scenario.”

Newcombe was convinced that the algorithms behind truly critical systems—systems storing a significant portion of the web’s data, for instance—ought to be not just good, but perfect. A single subtle bug could be catastrophic. But he knew how hard bugs were to find, especially as an algorithm grew more complex. You could do all the testing you wanted and you’d never find them all.

An algorithm written in TLA+ could in principle be proven correct. In practice, it allowed you to create a realistic model of your problem and test it not just thoroughly, but exhaustively. This was exactly what he’d been looking for: a language for writing perfect algorithms.

TLA+, which stands for “Temporal Logic of Actions,” is similar in spirit to model-based design: It’s a language for writing down the requirements—TLA+ calls them “specifications”—of computer programs. These specifications can then be completely verified by a computer. That is, before you write any code, you write a concise outline of your program’s logic, along with the constraints you need it to satisfy

Programmers are drawn to the nitty-gritty of coding because code is what makes programs go; spending time on anything else can seem like a distraction. And there is a patient joy, a meditative kind of satisfaction, to be had from puzzling out the micro-mechanics of code. But code, Lamport argues, was never meant to be a medium for thought. “It really does constrain your ability to think when you’re thinking in terms of a programming language,”

Code makes you miss the forest for the trees: It draws your attention to the working of individual pieces, rather than to the bigger picture of how your program fits together, or what it’s supposed to do—and whether it actually does what you think. This is why Lamport created TLA+. As with model-based design, TLA+ draws your focus to the high-level structure of a system, its essential logic, rather than to the code that implements it.

But TLA+ occupies just a small, far corner of the mainstream, if it can be said to take up any space there at all. Even to a seasoned engineer like Newcombe, the language read at first as bizarre and esoteric—a zoo of symbols.

this is a failure of education. Though programming was born in mathematics, it has since largely been divorced from it. Most programmers aren’t very fluent in the kind of math—logic and set theory, mostly—that you need to work with TLA+. “Very few programmers—and including very few teachers of programming—understand the very basic concepts and how they’re applied in practice. And they seem to think that all they need is code,” Lamport says. “The idea that there’s some higher level than the code in which you need to be able to think precisely, and that mathematics actually allows you to think precisely about it, is just completely foreign. Because they never learned it.”

“In the 15th century,” he said, “people used to build cathedrals without knowing calculus, and nowadays I don’t think you’d allow anyone to build a cathedral without knowing calculus. And I would hope that after some suitably long period of time, people won’t be allowed to write programs if they don’t understand these simple things.”

Programmers, as a species, are relentlessly pragmatic. Tools like TLA+ reek of the ivory tower. When programmers encounter “formal methods” (so called because they involve mathematical, “formally” precise descriptions of programs), their deep-seated instinct is to recoil.

Formal methods had an image problem. And the way to fix it wasn’t to implore programmers to change—it was to change yourself. Newcombe realized that to bring tools like TLA+ to the programming mainstream, you had to start speaking their language.

he presented TLA+ as a new kind of “pseudocode,” a stepping-stone to real code that allowed you to exhaustively test your algorithms—and that got you thinking precisely early on in the design process. “Engineers think in terms of debugging rather than ‘verification,’” he wrote, so he titled his internal talk on the subject to fellow Amazon engineers “Debugging Designs.” Rather than bemoan the fact that programmers see the world in code, Newcombe embraced it. He knew he’d lose them otherwise. “I’ve had a bunch of people say, ‘Now I get it,’” Newcombe says.

In the world of the self-driving car, software can’t be an afterthought. It can’t be built like today’s airline-reservation systems or 911 systems or stock-trading systems. Code will be put in charge of hundreds of millions of lives on the road and it has to work. That is no small task.

virtually no speaker of English gets confused when he or she hears the word “mean.” That’s because the different senses of the word occur in such different contexts as to allow listeners to infer its meaning nearly automatically.

To understand why ambiguity makes a language more efficient rather than less so, think about the competing desires of the speaker and the listener. The speaker is interested in conveying as much as possible with the fewest possible words, while the listener is aiming to get a complete and specific understanding of what the speaker is trying to say.

it is “cognitively cheaper” to have the listener infer certain things from the context than to have the speaker spend time on longer and more complicated utterances. The result is a system that skews toward ambiguity, reusing the “easiest” words. Once context is considered, it’s clear that “ambiguity is actually something you would want in the communication system,” Piantadosi says.

“You would expect that since languages are constantly changing, they would evolve to get rid of ambiguity,” Wasow says. “But if you look at natural languages, they are massively ambiguous: Words have multiple meanings, there are multiple ways to parse strings of words. … This paper presents a really rigorous argument as to why that kind of ambiguity is actually functional for communicative purposes, rather than dysfunctional.”

“Ambiguity is only good for us [as humans] because we have these really sophisticated cognitive mechanisms for disambiguating,” he says. “It’s really difficult to work out the details of what those are, or even some sort of approximation that you could get a computer to use.”

The body codes how the brain works, more than the brain controls the body. When we walk—whether taking a pleasant afternoon stroll, or storming off in tears, or trying to sneak into a stranger’s house late at night, with intentions that seem to have exploded into our minds from some distant elsewhere—the brain might be choosing where each foot lands, but the way in which it does so is always constrained by the shape of our legs

The way in which the brain approaches the task of walking is already coded by the physical layout of the body—and as such, wouldn’t it make sense to think of the body as being part of our decision-making apparatus? The mind is not simply the brain, as a generation of biological reductionists, clearing out the old wreckage of what had once been the soul, once insisted. It’s not a kind of software being run on the logical-processing unit of the brain. It’s bigger, and richer, and grosser, in every sense. It has joints and sinews. The rarefied rational mind sweats and shits; this body, this mound of eventually rotting flesh, is really you.

That’s embodied cognition.

Extended cognition is stranger.

The mind, they argue, has no reason to stop at the edges of the body, hemmed in by skin, flapping open and closed with mouths and anuses.

When we jot something down—a shopping list, maybe—on a piece of paper, aren’t we in effect remembering it outside our heads? Most of all, isn’t language itself something that’s always external to the individual mind?

Language sits hazy in the world, a symbolic and intersubjective ether, but at the same time it forms the substance of our thought and the structure of our understanding. Isn’t language thinking for us?

Writing, for Plato, is a pharmakon, a “remedy” for forgetfulness, but if taken in too strong a dose it becomes a poison: A person no longer remembers things for themselves; it’s the text that remembers, with an unholy autonomy. The same criticisms are now commonly made of smartphones. Not much changes.

Between Aug. 1 and the Super Bowl in early February, the N.F.L. administered almost one million tests to players and staff.

Many other organizations have sought safety in mass testing.

When the coronavirus closed down the country last spring, many colleges and universities sought her advice on how to safely reopen.

Now, a new analysis suggests that schools, businesses and other organizations that want to keep themselves safe should think beyond strictly themselves.

By dedicating a substantial proportion of their tests to people in the surrounding community, institutions could reduce the number of Covid-19 cases among their members by as much as 25 percent, researchers report in a new paper, which has not yet been published in a scientific journal.

“It’s natural in an outbreak for people to become self-serving, self-focused,”

“If you’ve been in enough outbreaks you just understand that testing in a box doesn’t makes sense. These things are communicable, and they’re coming in from the community.”

“really profound implications, especially if others can replicate it,” said David O’Connor

“We want to start using more sophisticated modeling and probably economic theory to inform what an optimal testing program would look like.”

Dr. Sabeti is an epidemic veteran, part of teams that responded to an Ebola outbreak in West Africa in 2014 and a mumps outbreak in the Boston area a few years later.

The University of Illinois is testing its students, faculty and staff twice a week and has conducted more than 1.6 million tests since July.

At a time when testing resources were in short supply, many of these institutions were proposing intensive, expensive testing regimens focused entirely on their own members

‘You’re in a drought in a place with a lot of forest fires, and you have a shortage of fire alarms,’” she recalled. “‘And if you run out and buy every fire alarm and install it in your own house, you’ll be able to pick up a fire the moment it hits your house, but at that point it’s burning to the ground.’”

‘You’re in a drought in a place with a lot of forest fires, and you have a shortage of fire alarms,’” she recalled. “‘And if you run out and buy every fire alarm and install it in your own house, you’ll be able to pick up a fire the moment it hits your house, but at that point it’s burning to the ground.’”

Using real-world data from C.M.U., the researchers created a baseline scenario in which 1 percent of people at the school, and 6 percent of those in the surrounding county, were infected by the coronavirus, and the university was testing 12 percent of its members every day.

Under these conditions, the researchers found, if the university used all of its tests on its own members, it would have roughly 200 Covid-19 cases after 40 days

The researchers then tweaked the model’s parameters in various ways: What if the virus were more prevalent? What if students and staff did not report all their contacts? What if they were better about mask-wearing and social distancing? What if the university deployed more tests, or fewer?

Unsurprisingly, the more testing the university did, and the more information it had about its members’ close contacts, the fewer Covid-19 cases there were

But in virtually every scenario, sharing at least some tests with the broader community led to fewer cases than hoarding them.

Some universities are beginning to adopt this outlook.

“A virus does not respect geographic boundaries,” Dr. Pollock said. “It is ludicrous to think that you can get control of an acute infectious respiratory disease like Covid-19, in a city like Davis that hosts a very large university, without coordinated public health measures that connect both the university and the community.”

There are barriers to the more altruistic approach, including internal political pressure to use testing resources in house and concerns about legal liability.

the researchers hope that their model convinces at least some institutions to rethink their strategy, not only during this epidemic but also in future ones.

“An outbreak is an opportunity to buy a lot of community good will, or to burn a lot of community good will,” Dr. Sabeti said. “We could have spent an entire year building up that relationship between organizations and institutions and their communities. And we would have done all that hard work together, as opposed to everybody turning inward.”

The paradox of reform movements is that, if you want to defy authority, you probably shouldn’t think entirely for yourself. You should attach yourself to a counter-tradition and school of thought that has been developed over the centuries and that seems true.

The old leftists had dialectical materialism and the Marxist view of history. Libertarians have Hayek and von Mises. Various spiritual movements have drawn from Transcendentalism, Stoicism, Gnosticism, Thomism, Augustine, Tolstoy, or the Catholic social teaching that inspired Dorothy Day.

These belief systems helped people envision alternate realities. They helped people explain why the things society values are not the things that should be valued. They gave movements a set of organizing principles. Joining a tradition doesn’t mean suppressing your individuality. Applying an ancient tradition to a new situation is a creative, stimulating and empowering act. Without a tradition, everything is impermanence and flux.

If I could offer advice to a young rebel, it would be to rummage the past for a body of thought that helps you understand and address the shortcomings you see. Give yourself a label.

Effective rebellion isn’t just expressing your personal feelings. It means replacing one set of authorities and institutions with a better set of authorities and institutions.

Your morning routine helps to ground you, and using it thoughtfully will help to set the tone for the rest of your day.

Most successful people carve out time in their morning to commit to things that make them feel relaxed, energized and motivated. That can mean working out, reading, meditating or just spending time with your loved ones.

most people require between seven and nine hours of sleep a night. If you’re constantly trying to get by on less than seven hours of sleep, it will catch up with you, likely sooner rather than later.

Nearly everyone I’ve talked to said they don’t consider one, two or even three missed days of their morning routine a failure, so long as they get back to it as soon as they can. They recognize that sometimes they’ll miss their routine, and that’s O.K.

Jerry Seinfeld has a theory of habit creation that requires a person not to “break the chain.” The idea is that if there’s a certain task you want to do every day, you can keep yourself accountable by putting a red “X” over every day you complete this task on a large wall calendar. Soon you’ll have created a chain. Your only job then is not to break it.