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So even if inflation itself seems unlikely, multiplying by the infinite number of universes it creates makes it quite plausible that we find ourselves in a post-inflationary situation.

ather than explaining why we live precisely in this kind of universe, eternal inflation admits there are many kinds of local universes, and expresses the hope that ones like ours are more likely than other kinds.

aturalness is a subtle criterion. In the case of inflationary cosmology, the drive to find a natural theory seems to have paid off handsomely, but perhaps other seemingly unnatural features of our world must simply be accepted. Ultimately it’s nature, not us, that decides what’s natural.

For this reason, cultures all around the world have treated emotional touching as something apart. The Greeks labeled the drive to touch with the word “eros,” and they meant something vaster and deeper than just sexual pleasure.

“Animals have sex and human beings have eros, and no accurate science is possible without making this distinction,” Allan Bloom observed.

The Abrahamic religions also treat sex as something sacred and beautiful when enveloped in loving and covenantal protections, and as something disordered and potentially peace-destroying when not.

Over the past 100 years or so, advanced thinkers across the West have worked to take the shame out of sex, surely a good thing. But they’ve also disenchanted it

“One of the principal outcomes of the sexual revolution was to establish that sex is just like any other social interaction — nothing taboo or sacred about it.” Sex is seen as a shallow physical and social thing, not a heart and soul altering thing.

One unintended effect of this disenchantment is that it becomes easy to underestimate the risks inherent in any encounter.

The woman who talked in an online article about her date with Aziz Ansari is being criticized because what happened to her was not like what happened to the victims of Harvey Weinstein and Louis C.K

The assumption seems to be that as long as there’s consent between adults, everything else is kosher.

Everything we know about touch suggests that even with full consent, the emotional quality of an encounter can have profound positive or negative effects. If Ansari did treat her coldly or neglectfully, it’s reasonable to think that the shame she felt right after was the surface effect of a deeper wound. Neglectful, dehumanizing sex is not harassment, but it’s some other form of serious harm.

Disenchanting emotional touch also causes people to underestimate the way past experiences shape current behavior.

Agency is learned, not bred. And one of the things that undermines agency most powerfully is past sexual harm.

The abuse of intimacy erodes all the building blocks of agency: self-worth, resiliency and self-efficacy (the belief that you can control a situation).

It is precisely someone who lives within a culture of supposedly zipless encounters who is most likely to be unable to take action when she feels uncomfortable.

I hate the way Babe, which published the story about the Ansari date, violated everybody’s privacy here. But it seems that the beginning of good sense is to take the power of touch seriously, as something that has profound good and bad effects.

It seems that the smarter we get about technology, the dumber we get about relationships. We live in a society in which loneliness, depression and suicide are on the rise.

The guiding moral principle here is not complicated: Try to treat other people as if they possessed precious hearts and infinite souls. Everything else will follow.

Do not skip around. To evaluate each candidate add up the six scores ... Firmly resolve that you will hire the candidate whose final score is the highest, even if there is another one whom you like better — try to resist your wish to invent broken legs to change the ranking.

than if you do what people normally do in such situations, which is to go into the interview unprepared and to make choices by an overall intuitive judgment such as "I looked into his eyes and liked what I saw."

This is the big problem in science that no one is talking about: even an honest person is a master of self-deception. Our brains evolved long ago on the African savannah, where jumping to plausible conclusions about the location of ripe fruit or the presence of a predator was a matter of survival. But a smart strategy for evading lions does not necessarily translate well to a modern laboratory, where tenure may be riding on the analysis of terabytes of multidimensional data. In today's environment, our talent for jumping to conclusions makes it all too easy to find false patterns in randomness, to ignore alternative explanations for a result or to accept 'reasonable' outcomes without question — that is, to ceaselessly lead ourselves astray without realizing it.

Failure to understand our own biases has helped to create a crisis of confidence about the reproducibility of published results

Although it is impossible to document how often researchers fool themselves in data analysis, says Ioannidis, findings of irreproducibility beg for an explanation. The study of 100 psychology papers is a case in point: if one assumes that the vast majority of the original researchers were honest and diligent, then a large proportion of the problems can be explained only by unconscious biases. “This is a great time for research on research,” he says. “The massive growth of science allows for a massive number of results, and a massive number of errors and biases to study. So there's good reason to hope we can find better ways to deal with these problems.”

Although the human brain and its cognitive biases have been the same for as long as we have been doing science, some important things have changed, says psychologist Brian Nosek, executive director of the non-profit Center for Open Science in Charlottesville, Virginia, which works to increase the transparency and reproducibility of scientific research. Today's academic environment is more competitive than ever. There is an emphasis on piling up publications with statistically significant results — that is, with data relationships in which a commonly used measure of statistical certainty, the p-value, is 0.05 or less. “As a researcher, I'm not trying to produce misleading results,” says Nosek. “But I do have a stake in the outcome.” And that gives the mind excellent motivation to find what it is primed to find.

Another reason for concern about cognitive bias is the advent of staggeringly large multivariate data sets, often harbouring only a faint signal in a sea of random noise. Statistical methods have barely caught up with such data, and our brain's methods are even worse, says Keith Baggerly, a statistician at the University of Texas MD Anderson Cancer Center in Houston. As he told a conference on challenges in bioinformatics last September in Research Triangle Park, North Carolina, “Our intuition when we start looking at 50, or hundreds of, variables sucks.”

One trap that awaits during the early stages of research is what might be called hypothesis myopia: investigators fixate on collecting evidence to support just one hypothesis; neglect to look for evidence against it; and fail to consider other explanations.

Economic literacy contributes to a second class of knowledge.

For some economic decisions, such as buying a home or investing in the stock market, it is possible to hire professional or technical help when making a choice, but in most cases it is neither economical nor practical for an individual to hire a skilled professional every time an economic decision needs to be made.

There are three essential ingredients for effective economic education in the schools.

The development of economic literacy must begin in the schools. Even young children are capable of learning basic economic concepts that help them understand their economic world. In the secondary years, that initial foundation can be expanded to include instruction in a broader set of economic ideas and concepts

Some may think that economics is too difficult a subject to be taught to children and youth, and that such instruction should wait until college

Waiting until students are in college to teach economics is simply a matter of "too little and too late." The majority of students end their formal education with secondary school, and even those students who continue their learning at a college or university may not take an economics course.

Economic literacy improves the competence of each individual for making personal and social decisions about the multitude of economic issues that will be encountered over a lifetime.

First, teachers must be knowledgeable about the subject and be able to help students learn how to use basic economic concepts to analyze personal and social issues. Second, good curriculum guides and instructional materials are needed that present economic content at an appropriate level for the student to understand. Third, economics must have a central place in the school curriculum—similar to math, science, history and language arts—so that substantial classroom time is devoted to economics instruction

Over the past 40 years there has been a significant improvement in each area.

A study of high school transcripts shows that only about 44 percent of high school students take a separate course in economics. This course is usually offered in the 12th grade as an elective and lasts for only a semester.

Given this situation—that fewer than half of high school graduates take a course in economics—it should not be surprising that study after study show that there is widespread economic illiteracy among youth and the American public.

Some 87 percent of high school seniors rated their knowledge and understanding of economic and economic issues as only fair or poor.

Knowing what determines prices in a market economy and accepting the outcomes are two different things. If demand or supply conditions change, prices in a competitive market will rise and fall. Having a basic understanding of how markets work does not always mean that people will like price changes, especially if prices rise, but it should increase the probability of accepting the market outcome

The development of basic economic literacy is an important goal for a democratic society that relies heavily on informed citizenry and personal economic decision-making.

To achieve that goal will require that significant gaps in the economic education of youth be closed by giving economics a more central place in the school curriculum. More economics coursework at the precollege level sets a foundation for economic literacy, but it is only the beginning.

The quality of mathematics research in the United States today is the envy of the scientific world. This is a direct result of the openness and inclusivity of the profession.

According to our own experiences at the universities where we teach, an overwhelming majority of American students with strong math backgrounds are either foreign-born or first-generation students who have additional support from their education-conscious families. At all levels, STEM disciplines are more and more dependent on a constant flow of foreign talent.

The Organization for Economic Cooperation and Development compares mathematical proficiency among 15-year-olds by country worldwide. According to its 2018 report, America ranked 37th while China, America’s main competitor for world leadership, came in first.

This is despite the fact that the United States is the fifth-highest spender per pupil among the 37 developed OECD nations

This massive failure of our K-12 education system trickles through the STEM pipeline.

At the undergraduate level, too few American students are prepared for higher-level mathematics courses. These students are then unprepared for rigorous graduate-level work

Can Americans maintain this unmatched excellence in the future? There are worrisome signs that suggest not.

There are many reasons for this failure, but the way that we educate and prepare teachers is particularly influential. The vast majority of K-12 math teachers are graduates of teacher-preparation programs that teach very little substantive mathematics

This has led to a constant stream of ill-advised and dumbed-down reforms. One of the latest fads is anti-racist mathematics. Promoted in several states, the bizarre doctrine threatens to further degrade the teaching of mathematics.

Another major concern is the twisted interpretation of diversity, equity, and inclusion (DEI).

Under the banner of DEI, universities are abandoning the use of standardized tests like the SAT and GRE in admissions, and cities are considering scrapping academic tracking and various gifted programs in schools, which they deem “inequitable.”

such programs are particularly effective, when properly implemented, at discovering and encouraging talented children from disadvantaged backgrounds.

This will only lead to a further widening of racial disparities in educational outcomes while lowering American children’s rankings in education internationally.

These measures will not only hinder the progress of the generations of our future STEM workforce but also contribute to structural inequalities, as they are uniquely detrimental to students whose parents cannot send them to private schools or effective enrichment programs.

These are just a few examples of an unprecedented fervor for revolutionary change in the name of Critical Race Theory (CRT), a doctrine that views the world as a fierce battleground for the narratives of various identity groups.

The new 2021 Mathematics Framework, currently under consideration by California’s Department of Education, does away “with all tracking, acceleration, gifted programs, or any instruction that involves clustering by individual differences, without expressing any awareness of the impact these drastic alterations would have in preparing STEM-ready candidates.”

Ill-conceived DEI policies, often informed by CRT, and the declining standards of K-12 math education feed each other in a vicious circle

Regarding minorities, in particular, public K-12 education all too often produces students unprepared to compete, thus leading to large disparities in admissions at universities, graduate programs, and faculty positions. This disparity is then condemned as a manifestation of structural racism, resulting in administrative measures to lower the evaluation criteria. Lowering standards at all levels leads eventually to even worse outcomes and larger disparities, and so on in a downward spiral.

A case in point is the recent report by the American Mathematical Society that accuses the entire mathematics community, with the thinnest of specific evidence, of systemic racial discrimination. A major justification put forward for such a grave accusation is the lack of sufficient representation of Black mathematicians in the professio

the report, while raising awareness of several ugly facts from the long-ago past, makes little effort to address the real reasons for this, mainly the catastrophic failure of the K-12 mathematical educational system.

The National Science Foundation, a federal institution meant to support fundamental research, is now diverting some of its limited funding to various DEI initiatives of questionable benefit.

Meanwhile, other countries, especially China, are doing precisely the opposite, following the model of our past dedication to objective measures of excellence. How long before we will see a reverse exodus, away from the United States?

The present crisis can still be reversed by focusing on a few concrete actions:

Improve schools in urban areas and inner-city neighborhoods by following the most promising education programs. These programs demonstrate that inner-city children benefit if they are challenged by high standards and a nurturing environment.

Follow the lead of other highly successful rigorous programs such as BASIS schools and Math for America, which focus on rigorous STEM curricula, combined with 21st-century teaching methods, and recruit talented teachers to help them build on their STEM knowledge and teaching methods.

Increase, rather than eliminate, tailored instruction, both for accelerated and remedial math courses.

Reject the soft bigotry of low expectations, that Black children cannot do well in competitive mathematics programs and need dumbed-down ethnocentric versions of mathematics.

Uphold the objective selection process based on merit at all levels of education and research.

It can occur when a physician refuses to consider alternative diagnoses once an initial diagnosis has been established, despite contradicting data, such as lab results. This bias leads physicians to see what they want to see

Anchoring bias is closely related to confirmation bias and comes into play when interpreting evidence. It refers to physicians’ practices of prioritizing information and data that support their initial impressions, even when first impressions are wrong

When physicians move from deliberation to action, they are sometimes swayed by emotional reactions rather than rational deliberation about risks and benefits. This is called the affect heuristic, and, while heuristics can often serve as efficient approaches to problem solving, they can sometimes lead to bias

Further down the treatment pathway, outcomes bias can come into play. This bias refers to the practice of believing that good or bad results are always attributable to prior decisions, even when there is no valid reason to do so

The dual-process theory, a cognitive model of reasoning, can be particularly relevant in matters of clinical decision making

This theory is based on the argument that we use 2 different cognitive systems, intuitive and analytical, when reasoning. The former is quick and uses information that is readily available; the latter is slower and more deliberate.

Consideration should be given to the difficulty physicians face in employing analytical thinking exclusively. Beyond constraints of time, information, and resources, many physicians are also likely to be sleep deprived, work in an environment full of distractions, and be required to respond quickly while managing heavy cognitive loads

Simply increasing physicians’ familiarity with the many types of cognitive biases—and how to avoid them—may be one of the best strategies to decrease bias-related errors

The same review suggests that cognitive forcing strategies may also have some success in improving diagnostic outcomes

Afterwards, the resident physicians were debriefed on both case-specific details and on cognitive forcing strategies, interviewed, and asked to complete a written survey. The results suggested that resident physicians further along in their training (ie, postgraduate year three) gained more awareness of cognitive strategies than resident physicians in earlier years of training, suggesting that this tool could be more useful after a certain level of training has been completed

A 2013 study examined the effect of a 3-part, 1-year curriculum on recognition and knowledge of cognitive biases and debiasing strategies in second-year residents

Cognitive biases in clinical practice have a significant impact on care, often in negative ways. They sometimes manifest as physicians seeing what they want to see rather than what is actually there. Or they come into play when physicians make snap decisions and then prioritize evidence that supports their conclusions, as opposed to drawing conclusions from evidence

Fortunately, cognitive psychology provides insight into how to prevent biases. Guided reflection and cognitive forcing strategies deflect bias through close examination of our own thinking processes.

During medical education and consistently thereafter, we must provide physicians with a full appreciation of the cost of biases and the potential benefits of combatting them.

These cognitive errors, which distract leaders from optimal policy making and citizens from taking steps to promote their own and others’ interests, cannot merely be ascribed to repudiations of science.

The first error that thwarts effective policy making during crises stems from what economists have called the “identifiable victim effect.” Humans respond more aggressively to threats to identifiable lives, ie, those that an individual can easily imagine being their own or belonging to people they care about (such as family members) or care for (such as a clinician’s patients) than to the hidden, “statistical” deaths reported in accounts of the population-level tolls of the crisis

Yet such views represent a second reason for the broad endorsement of policies that prioritize saving visible, immediately jeopardized lives: that humans are imbued with a strong and neurally mediated3 tendency to predict outcomes that are systematically more optimistic than observed outcomes

A third driver of misguided policy responses is that humans are present biased, ie, people tend to prefer immediate benefits to even larger benefits in the future.

Even if the tendency to prioritize visibly affected individuals could be resisted, many people would still place greater value on saving a life today than a life tomorrow.

Similar psychology helps explain the reluctance of many nations to limit refrigeration and air conditioning, forgo fuel-inefficient transportation, and take other near-term steps to reduce the future effects of climate change

The fourth contributing factor is that virtually everyone is subject to omission bias, which involves the tendency to prefer that a harm occur by failure to take action rather than as direct consequence of the actions that are taken

Although those who set policies for rationing ventilators and other scarce therapies do not intend the deaths of those who receive insufficient priority for these treatments, such policies nevertheless prevent clinicians from taking all possible steps to save certain lives.

An important goal of governance is to mitigate the effects of these and other biases on public policy and to effectively communicate the reasons for difficult decisions to the public. However, health systems’ routine use of wartime terminology of “standing up” and “standing down” intensive care units illustrate problematic messaging aimed at the need to address immediate danger

Second, had governments, health systems, and clinicians better understood the “identifiable victim effect,” they may have realized that promoting flattening the curve as a way to reduce pressure on hospitals and health care workers would be less effective than promoting early restaurant and retail store closures by saying “The lives you save when you close your doors include your own.”

Third, these leaders’ routine use of terms such as “nonpharmaceutical interventions”9 portrays public health responses negatively by labeling them according to what they are not. Instead, support for heavily funding contact tracing could have been generated by communicating such efforts as “lifesaving.

Fourth, although errors of human cognition are challenging to surmount, policy making, even in a crisis, occurs over a sufficient period to be meaningfully improved by deliberate efforts to counter untoward biases

deep, evolutionarily ancient mechanism that helps us balance overall cost and benefit.

Second, it may be influenced by uncertainty about the outcome (you never know, it might work out, so why not keep trying?)

have shown that both capuchin monkeys and rhesus macaques are susceptible to the same behavior and that it occurs more often when the monkeys are uncertain about the outcome.

That helps when you're foraging for food, hunting prey, waiting for eggs to hatch, seeking a mate, or building a nest or enclosure.

"Monkeys have really quick reaction times on these games," said Brosnan, "so one second to them is actually a long time."

"Most rounds lasted only 1 second. So if you didn't get a reward after that, it was actually better to quit and start a new round. That would likely get you a treat sooner than if you had kept going."

"They persisted 5 to 7 times longer than was optimal," said Brosnan, "and the longer they had already tried, the more likely they were to complete the entire task."

t suggests that this behavior is likely driven by evolution and deeply embedded across species.

"They're like my second set of kids," she said.

it shows that human capacities like rationalization, or human concerns like not giving up on something we have publicly committed to, are probably not the main drivers of the sunk cost phenomenon.

reminds us that there is sometimes a good reason to give up.

A 2017 survey of passengers commissioned by London’s Gatwick Airport found that 15% of men and 6% of women are more likely to cry while watching a film on a flight than if they were to watch that movie elsewhere.

It has been found that if doctors are trained how to translate conditional probabilities into natural frequencies, the ability of the doctors to communicate the risk to their patients goes up dramatically. Just imagine the huge difference this can make to customer satisfaction in the healthcare business.

Sleep is tied to many positive health outcomes according to a constant stream of medical research, much of which recommends more than seven hours a night. My goal was to sleep eight hours each night, and for the first two weeks of the month I tried and failed. I didn't manage it once. It started stressing me out.Wake up, people: You're fooling yourself about sleep, study says I did get more sleep than I would have otherwise because I prioritized getting to bed earlier. But between work, training for a marathon, kids' bedtimes and evening plans, my life was not conducive to that schedule. But since I'm making up the rules (and you make up yours) I gave up on sleep.

Research ties nature exposure to both longevity and happiness, so I made it a goal to commune with nature every day. This could include a run through a park, stopping to hug a tree (which I did at least once), or watching a convoy of ants cross a sidewalk.

Another super-habit for mental and physical health, I made an effort to incorporate meditation in some form -- whether it was 10 breaths, 30 minutes, guided, formal, mantra-led, what have you -- every day.

In order to simply increase my own, general awareness, every day in January my goal was to notice something new. I kept my eyes open, looked a bit longer, stopped to read the history markers, noticing buildings or took a different route than normal -- small ways to avoid sleepwalking through life.

At CNN, I sit near a constant pile of sweets. And that combined with my weakness for them equals a snacking problem. So in July, my goal was to make fresh fruit my standard snack of choice and eat at least one piece or serving a day. And more water -- at least a pint before coffee in the morning.

In August I attempted to not stay seated for more than 30 minutes during waking hours. Recent research has associated a number of poor health conditions with a sedentary lifestyle. And while no one can definitively say how much sitting is bad for us, moving every 30 minutes has emerged as a good guideline.

My commitment in September was 15 minutes of some form of creative writing each day, almost entirely in a writer's notebook that I've had since college. It's full of random dialogue, lists and story plots, and I used to write in it often but not so much in recent years.

Read a novel. That was it. Given that I mainly read non-fiction, this idea seemed rather novel (sorry).

My intention was to make a daily gesture of gratitude -- an emotional state with its own positive health outcomes -- and I quickly fell behind. I decided instead (because, again, I make up the rules) to make the goal a total of 30 thank yous, one for each day of the month, and I managed to cram them all in.

In another attempt to combat unhealthy snacking, my final goal for 2019 was to convert my diet into one filled with non-processed, whole foods. Carrots and peanut butter instead of donuts; almonds instead of old Halloween candy -- you get the idea. I also declared my intention to track my progress and give myself a daily score.

one of the leading modelers, Gabriela Gomes, suggested the entire area of research was being effectively blackballed out of fear it might encourage a relaxation of pandemic vigilance. “This is the very sad reason for the absence of more optimistic projections on the development of this pandemic in the scientific literature,” she wrote on Twitter. “Our analysis suggests that herd-immunity thresholds are being achieved despite strict social-distancing measures.”

Gomes suggested, herd immunity could happen with as little as one quarter of the population of a community exposed — or perhaps just 20 percent. “We just keep running the models, and it keeps coming back at less than 20 percent,” she told Hamblin. “It’s very striking.” Such findings, if they held up, would be very instructive, as Hamblin writes: “It would mean, for instance, that at 25 percent antibody prevalence, New York City could continue its careful reopening without fear of another major surge in cases.”

But for those hoping that 25 percent represents a true ceiling for pandemic spread in a given community, well, it almost certainly does not, considering that recent serological surveys have shown that perhaps 93 percent of the population of Iquitos, Peru, has contracted the disease; as have more than half of those living in Indian slums; and as many as 68 percent in particular neighborhoods of New York City

overshoot of that scale would seem unlikely if the “true” threshold were as low as 20 or 25 percent.

But, of course, that threshold may not be the same in all places, across all populations, and is surely affected, to some degree, by the social behavior taken to protect against the spread of the disease.

we probably err when we conceive of group immunity in simplistically binary terms. While herd immunity is a technical term referring to a particular threshold at which point the disease can no longer spread, some amount of community protection against that spread begins almost as soon as the first people are exposed, with each case reducing the number of unexposed and vulnerable potential cases in the community by one

you would not expect a disease to spread in a purely exponential way until the point of herd immunity, at which time the spread would suddenly stop. Instead, you would expect that growth to slow as more people in the community were exposed to the disease, with most of them emerging relatively quickly with some immune response. Add to that the effects of even modest, commonplace protections — intuitive social distancing, some amount of mask-wearing — and you could expect to get an infection curve that tapers off well shy of 60 percent exposure.

Looking at the data, we see that transmissions in many severely impacted states began to slow down in July, despite limited interventions. This is especially notable in states like Arizona, Florida, and Texas. While we believe that changes in human behavior and changes in policy (such as mask mandates and closing of bars/nightclubs) certainly contributed to the decrease in transmission, it seems unlikely that these were the primary drivers behind the decrease. We believe that many regions obtained a certain degree of temporary herd immunity after reaching 10-35 percent prevalence under the current conditions. We call this 10-35 percent threshold the effective herd immunity threshold.

Indeed, that is more or less what was recently found by Youyang Gu, to date the best modeler of pandemic spread in the U.S

he cautioned again that he did not mean to imply that the natural herd-immunity level was as low as 10 percent, or even 35 percent. Instead, he suggested it was a plateau determined in part by better collective understanding of the disease and what precautions to take

Gu estimates national prevalence as just below 20 percent (i.e., right in the middle of his range of effective herd immunity), it still counts, I think, as encouraging — even if people in hard-hit communities won’t truly breathe a sigh of relief until vaccines arrive.

If you can get real protection starting at 35 percent, it means that even a mediocre vaccine, administered much more haphazardly to a population with some meaningful share of vaccination skeptics, could still achieve community protection pretty quickly. And that is really significant — making both the total lack of national coordination on rollout and the likely “vaccine wars” much less consequential.

At least 20 percent of the public, and perhaps 50 percent, had some preexisting, cross-protective T-cell response to SARS-CoV-2, according to one much-discussed recent paper. An earlier paper had put the figure at between 40 and 60 percent. And a third had found an even higher prevalence: 81 percent.

The T-cell story is similarly encouraging in its big-picture implications without being necessarily paradigm-changing

These numbers suggest their own heterogeneity — that different populations, with different demographics, would likely exhibit different levels of cross-reactive T-cell immune response

The most optimistic interpretation of the data was given to me by Francois Balloux, a somewhat contrarian disease geneticist and the director of the University College of London’s Genetics Institute

According to him, a cross-reactive T-cell response wouldn’t prevent infection, but would probably mean a faster immune response, a shorter period of infection, and a “massively” reduced risk of severe illness — meaning, he guessed, that somewhere between a third and three-quarters of the population carried into the epidemic significant protection against its scariest outcomes

the distribution of this T-cell response could explain at least some, and perhaps quite a lot, of COVID-19’s age skew when it comes to disease severity and mortality, since the young are the most exposed to other coronaviruses, and the protection tapers as you get older and spend less time in environments, like schools, where these viruses spread so promiscuously.

Balloux told me he believed it was also possible that the heterogeneous distribution of T-cell protection also explains some amount of the apparent decline in disease severity over time within countries on different pandemic timelines — a phenomenon that is more conventionally attributed to infection spreading more among the young, better treatment, and more effective protection of the most vulnerable (especially the old).

Going back to Youyang Gu’s analysis, what he calls the “implied infection fatality rate” — essentially an estimated ratio based on his modeling of untested cases — has fallen for the country as a whole from about one percent in March to about 0.8 percent in mid-April, 0.6 percent in May, and down to about 0.25 percent today.

even as we have seemed to reach a second peak of coronavirus deaths, the rate of death from COVID-19 infection has continued to decline — total deaths have gone up, but much less than the number of cases

In other words, at the population level, the lethality of the disease in America has fallen by about three-quarters since its peak. This is, despite everything that is genuinely horrible about the pandemic and the American response to it, rather fantastic.

there may be some possible “mortality displacement,” whereby the most severe cases show up first, in the most susceptible people, leaving behind a relatively protected population whose experience overall would be more mild, and that T-cell response may play a significant role in determining that susceptibility.

That, again, is Balloux’s interpretation — the most expansive assessment of the T-cell data offered to me

The most conservative assessment came from Sarah Fortune, the chair of Harvard’s Department of Immunology

Fortune cautioned not to assume that cross-protection was playing a significant role in determining severity of illness in a given patient. Those with such a T-cell response, she told me, would likely see a faster onset of robust response, yes, but that may or may not yield a shorter period of infection and viral shedding

Most of the scientists, doctors, epidemiologists, and immunologists I spoke to fell between those two poles, suggesting the T-cell cross-immunity findings were significant without necessarily being determinative — that they may help explain some of the shape of pandemic spread through particular populations, but only some of the dynamics of that spread.

he told me he believed, in the absence of that data, that T-cell cross-immunity from exposure to previous coronaviruses “might explain different disease severity in different people,” and “could certainly be part of the explanation for the age skew, especially for why the very young fare so well.”

the headline finding was quite clear and explicitly stated: that preexisting T-cell response came primarily via the variety of T-cells called CD4 T-cells, and that this dynamic was consistent with the hypothesis that the mechanism was inherited from previous exposure to a few different “common cold” coronaviruses

“This potential preexisting cross-reactive T-cell immunity to SARS-CoV-2 has broad implications,” the authors wrote, “as it could explain aspects of differential COVID-19 clinical outcomes, influence epidemiological models of herd immunity, or affect the performance of COVID-19 candidate vaccines.”

“This is at present highly speculative,” they cautioned.

An outbreak isn’t a double pendulum; it’s much more convoluted. Myriad chains of events, operating in overlapping networks, conspire to chart its course.

It’s the double pendulum, and as a physical object, it’s very simple: A pendulum (a string and a weight) is attached to the bottom of another. Its movement is explained by the laws of motion written by Isaac Newton hundreds of years ago.

But slight changes in the initial condition of the pendulum — say it starts its swing from a little higher up, or if the weight of the pendulum balls is a little heavier, or one of the pendulum arms is a bit longer than the other — lead to wildly different outcomes that are very hard to predict.

The double pendulum is chaotic because the motion of the first pendulum influences the motion of the second, which then influences the entire apparatus. There isn’t a simple scale or ratio to describe how the inputs relate to the outputs. A one-gram change to the weight of a pendulum ball can result in a very different swing pattern than a two-gram change.

It teaches us to understand the mechanics of a system — the science of how it works — without being able to precisely predict its future. It helps us visualize how something that seems like it should be linear and predictable just isn’t.

That’s why, when pressed, epidemiologists have to say they don’t know what’s going to happen.

Climate scientists clearly tell us adding CO2 to the air will increase global temperatures. Yet they argue about when the worst effects of climate change will be felt and how bad it will be

Still, they know the mechanics of outbreaks. The chaos “doesn’t necessarily mean we know nothing,” Kissler says. They understand the conditions that make an outbreak worse and the conditions that make it better.

There is a tough tension of the current moment that we all need to work through: The future is clouded in chaos, but we know the mechanics of this system

Here are the mechanics. Scientists know that if we let up on social distancing, without an alternative plan in place, the virus can infect more people. They know this virus is likely to persist for at least a few years without a vaccine. They know it’s very contagious. That it’s very deadly. They also know that its pandemic potential is hardly spent, and that most of the population of the United States and the world is still vulnerable to it.

Will residents keep up with mask-wearing and social distancing, even when their leaders relax regulations? Plus, there are scientific questions about the virus still not understood: Will it diminish transmission in a seasonal pattern? Do children contribute greatly to its spread? How long does immunity last after an infection? Why do some people breathe out more of the virus than others? The answers to these questions will influence the future, and we do not know the answers.

Scientists are still unraveling what makes the difference between a sprawling outbreak in one city and a more manageable one in another. Some of it is the result of policy, some is the result of demographics, some is about structural inequality and racism, and some comes down to individual behavior. Some of it is just luck. That’s chaos for you.

“I don’t see uncertainty as a lack of knowledge,” says Philipp Lorenz-Spreen, a physicist who studies the chaos of a different sort of viral dynamics. “I think it’s a fundamental part of how our world works. It’s not our fault we do not know where this all will go.”

Newton clearly told us what happens when an object drops from the sky. But follow his laws, and find that the path of a double pendulum is very, very difficult to predict.

There’s a simple mechanism that is helping me understand the many possible futures we face with the Covid-19 pandemic.

Epidemiologists are clearly telling us what happens when you bring masses of people together during a pandemic. But they can’t tell us the exact shape this outbreak will take.

The AI boosters emphasized the incredible potential of their innovation, and Netanyahu raised practical objections to their enthusiasm. They cited futurists such as Ray Kurzweil to paint a bright picture of a post-AI world; Netanyahu cited the Bible and the medieval Jewish philosopher Maimonides to caution against upending human institutions and subordinating our existence to machines.

Musk matter-of-factly explained that the “very positive scenario of AI” is “actually in a lot of ways a description of heaven,” where “you can have whatever you want, you don’t need to work, you have no obligations, any illness you have can be cured,” and death is “a choice.” Netanyahu incredulously retorted, “You want this world?”

By the time the panel began to wind down, the Israeli leader had seemingly made up his mind. “This is like having nuclear technology in the Stone Age,” he said. “The pace of development [is] outpacing what solutions we need to put in place to maximize the benefits and limit the risks.”

“You have these trillion-dollar [AI] companies that are produced overnight, and they concentrate enormous wealth and power with a smaller and smaller number of people,” the Israeli leader said, noting that even a free-market evangelist like himself was unsettled by such monopolization. “That will create a bigger and bigger distance between the haves and the have-nots, and that’s another thing that causes tremendous instability in our world. And I don’t know if you have an idea of how you overcome that?”

This was less because he is a prophet and more because he is a pessimist. When it comes to grandiose predictions about a better tomorrow—whether through peace with the Palestinians, a nuclear deal with Iran, or the advent of artificial intelligence—Netanyahu always bets against. Informed by a dark reading of Jewish history, he is a cynic about human nature and a skeptic of human progress.

fter all, no matter how far civilization has advanced, it has always found ways to persecute the powerless, most notably, in his mind, the Jews. For Netanyahu, the arc of history is long, and it bends toward whoever is bending it.

This is why the Israeli leader puts little stock in utopian promises, whether they are made by progressive internationalists or Silicon Valley futurists, and places his trust in hard power instead

“The weak crumble, are slaughtered and are erased from history while the strong, for good or for ill, survive. The strong are respected, and alliances are made with the strong, and in the end peace is made with the strong.”

To his many critics, myself included, Netanyahu’s refusal to envision a different future makes him a “creature of the bunker,” perpetually governed by fear. Although his pessimism may sometimes be vindicated, it also holds his country hostag

In other words, the same cynicism that drives Netanyahu’s reactionary politics is the thing that makes him an astute interrogator of AI and its promoters. Just as he doesn’t trust others not to use their power to endanger Jews, he doesn’t trust AI companies or AI itself to police its rapidly growing capabilities.

Time and again, we learned there was never a grand plan or big ideas—just weapons-grade ego, incompetence, thin skin, and prejudice against those who don’t revere him.

Despite all the incredible, damning reporting coming out of Twitter and all of Musk’s very public mistakes, many people still refuse to believe—even if they detest him—that he is simply incompetent.

What is amazing about the current moment is that, despite how ridiculous it all feels, a fundamental tenet of reality and logic appears to be holding true: If you don’t know what you’re doing or don’t really care, you’ll run the thing you’re in charge of into the ground, and people will notice.

And so the moment feels too dumb and too on the nose to be real and yet also very real—kind of like all of reality in 2022.

I don’t really know where any of this will lead, but one interesting possibility is that Musk gets increasingly reactionary and trollish in his politics and stewardship of Twitter.

Leaving the politics aside, from a basic customer-service standpoint this is generally an ill-advised way for the owner of a company to treat an elected official when that elected official wishes to know why your service has failed them. The reason it is ill-advised is because then the elected official could tweet something like what Senator Markey tweeted on Sunday: “One of your companies is under an FTC consent decree. Auto safety watchdog NHTSA is investigating another for killing people. And you’re spending your time picking fights online. Fix your companies. Or Congress will.”

It seems clear that Musk, like any dedicated social-media poster, thrives on validation, so it makes sense that, as he continues to dismantle his own mystique as an innovator, he might look for adoration elsewhere

Recent history has shown that, for a specific audience, owning the libs frees a person from having to care about competency or outcome of their actions. Just anger the right people and you’re good, even if you’re terrible at your job. This won’t help Twitter’s financial situation, which seems bleak, but it’s … something!

Bankman-Fried, the archetype, appealed to people for all kinds of reasons. His narrative as a philanthropist, and a smart rationalist, and a stone-cold weirdo was something people wanted to buy into because, generally, people love weirdos who don’t conform to systems and then find clever ways to work around them and become wildly successful as a result.

Bankman-Fried was a way that a lot of people could access and maybe obliquely understand what was going on in crypto. They may not have understood what FTX did, but they could grasp a nerd trying to leverage a system in order to do good in the world and advance progressive politics. In that sense, Bankman-Fried is easy to root for and exciting to cover. His origin story and narrative become more important than the particulars of what he may or may not be doing.

the past few weeks have been yet another reminder that the smug-nerd-genius narrative may sell magazines, and it certainly raises venture funding, but the visionary founder is, first and foremost, a marketing product, not a reality. It’s a myth that perpetuates itself. Once branded a visionary, the founder can use the narrative to raise money and generate a formidable net worth, and then the financial success becomes its own résumé. But none of it is real.

Adversarial journalism ideally questions and probes power. If it is trained on technology companies and their founders, it is because they either wield that power or have the potential to do so. It is, perhaps unintuitively, a form of respect for their influence and potential to disrupt. But that’s not what these founders want.

even if all tech coverage had been totally flawless, Silicon Valley would have rejected adversarial tech journalism because most of its players do not actually want the responsibility that comes with their potential power. They want only to embody the myth and reap the benefits. They want the narrative, which is focused on origins, ambitions, ethos, and marketing, and less on the externalities and outcomes.

Looking at Musk and Bankman-Fried, it would appear that the tech visionaries mostly get their way. For all the complaints of awful, negative coverage and biased reporting, people still want to cheer for and give money to the “‘smug nerds building castles in the sky.’” Though they vary wildly right now in magnitude, their wounds are self-inflicted—and, perhaps, the result of believing their own hype.

That’s because, almost always, the smug-nerd-genius narrative is a trap. It’s one that people fall into because they need to believe that somebody out there is so brilliant, they can see the future, or that they have some greater, more holistic understanding of the world (or that such an understanding is possible)

It’s not unlike a conspiracy theory in that way. The smug-nerd-genius narrative helps take the complexity of the world and make it more manageable.

Putting your faith in a space billionaire or a crypto wunderkind isn’t just sad fanboydom; it is also a way for people to outsource their brain to somebody else who, they believe, can see what they can’t

the smug nerd genius is exceedingly rare, and, even when they’re not outed as a fraud or a dilettante, they can be assholes or flawed like anyone else. There aren’t shortcuts for making sense of the world, and anyone who is selling themselves that way or buying into that narrative about them should read to us as a giant red flag.

The philosopher Immanuel Kant (1724–1804) argued in his Critique of Pure Reason that the human mind knows objects in innate, a priori ways. Kant claimed that humans, from birth, must experience all objects as being successive (time) and juxtaposed (space). His list of inborn categories describes predicates that the mind can attribute to any object in general. Arthur Schopenhauer (1788–1860) agreed with Kant, but reduced the number of innate categories to one—causality—which presupposes the others.

Modern nativism is most associated with the work of Jerry Fodor (1935–2017), Noam Chomsky (b. 1928), and Steven Pinker (b. 1954), who argue that humans from birth have certain cognitive modules (specialised genetically inherited psychological abilities) that allow them to learn and acquire certain skills, such as language.

For example, children demonstrate a facility for acquiring spoken language but require intensive training to learn to read and write. This poverty of the stimulus observation became a principal component of Chomsky's argument for a "language organ"—a genetically inherited neurological module that confers a somewhat universal understanding of syntax that all neurologically healthy humans are born with, which is fine-tuned by an individual's experience with their native language

In The Blank Slate (2002), Pinker similarly cites the linguistic capabilities of children, relative to the amount of direct instruction they receive, as evidence that humans have an inborn facility for speech acquisition (but not for literacy acquisition).

A number of other theorists[1][2][3] have disagreed with these claims. Instead, they have outlined alternative theories of how modularization might emerge over the course of development, as a result of a system gradually refining and fine-tuning its responses to environmental stimuli.[4]

Many empiricists are now also trying to apply modern learning models and techniques to the question of language acquisition, with marked success.[20] Similarity-based generalization marks another avenue of recent research, which suggests that children may be able to rapidly learn how to use new words by generalizing about the usage of similar words that they already know (see also the distributional hypothesis).[14][21][22][23]

The term universal grammar (or UG) is used for the purported innate biological properties of the human brain, whatever exactly they turn out to be, that are responsible for children's successful acquisition of a native language during the first few years of life. The person most strongly associated with the hypothesising of UG is Noam Chomsky, although the idea of Universal Grammar has clear historical antecedents at least as far back as the 1300s, in the form of the Speculative Grammar of Thomas of Erfurt.

This evidence is all the more impressive when one considers that most children do not receive reliable corrections for grammatical errors.[9] Indeed, even children who for medical reasons cannot produce speech, and therefore have no possibility of producing an error in the first place, have been found to master both the lexicon and the grammar of their community's language perfectly.[10] The fact that children succeed at language acquisition even when their linguistic input is severely impoverished, as it is when no corrective feedback is available, is related to the argument from the poverty of the stimulus, and is another claim for a central role of UG in child language acquisition.

Researchers at Blue Brain discovered a network of about fifty neurons which they believed were building blocks of more complex knowledge but contained basic innate knowledge that could be combined in different more complex ways to give way to acquired knowledge, like memory.[11

experience, the tests would bring about very different characteristics for each rat. However, the rats all displayed similar characteristics which suggest that their neuronal circuits must have been established previously to their experiences. The Blue Brain Project research suggests that some of the "building blocks" of knowledge are genetic and present at birth.[11]

modern nativist theory makes little in the way of specific falsifiable and testable predictions, and has been compared by some empiricists to a pseudoscience or nefarious brand of "psychological creationism". As influential psychologist Henry L. Roediger III remarked that "Chomsky was and is a rationalist; he had no uses for experimental analyses or data of any sort that pertained to language, and even experimental psycholinguistics was and is of little interest to him".[13]

, Chomsky's poverty of the stimulus argument is controversial within linguistics.[14][15][16][17][18][19]

Neither the five-year-old nor the adults in the community can easily articulate the principles of the grammar they are following. Experimental evidence shows that infants come equipped with presuppositions that allow them to acquire the rules of their language.[6]

Paul Griffiths, in "What is Innateness?", argues that innateness is too confusing a concept to be fruitfully employed as it confuses "empirically dissociated" concepts. In a previous paper, Griffiths argued that innateness specifically confuses these three distinct biological concepts: developmental fixity, species nature, and intended outcome. Developmental fixity refers to how insensitive a trait is to environmental input, species nature reflects what it is to be an organism of a certain kind, and the intended outcome is how an organism is meant to develop.[24]

Let's consider a case in which Ulfelder argues there is insufficient data to render a prediction -- North Korea. There is no official data on North Korean GDP, so what can we do? It turns out that the same data science approaches that were used to aggregate polls have other uses as well. One is the imputation of missing data. Yes, even when it is all missing. The basic idea is to use the general correlations among data that you do have to provide an aggregate way of estimating information that we don't have.

As it turned out, in this month's election public opinion polls were considerably more precise than the fundamentals. The fundamentals were not always providing bad predictions, but better is better.

In 2012 there were two types of models: one type based on fundamentals such as economic growth and unemployment and another based on public opinion surveys

There is a tradition in world politics to go either back until the Congress of Vienna (when there were fewer than two dozen independent countries) or to the early 1950s after the end of the Second World War. But in reality, there is no need to do this for most studies.

Ulfelder tells us that "when it comes to predicting major political crises like wars, coups, and popular uprisings, there are many plausible predictors for which we don't have any data at all, and much of what we do have is too sparse or too noisy to incorporate into carefully designed forecasting models." But this is true only for the old style of models based on annual data for countries. If we are willing to face data that are collected in rhythm with the phenomena we are studying, this is not the case

The new history of capitalism is less a movement than what proponents call a “cohort”: a loosely linked group of scholars who came of age after the end of the cold war cleared some ideological ground, inspired by work that came before but unbeholden to the questions — like, why didn’t socialism take root in America? — that animated previous generations of labor historians.

the crisis hit, and people started asking, ‘Oh my God, what has Wall Street been doing for the last 100 years?’ ”

While most scholars in the field reject the purely oppositional stance of earlier Marxist history, they also take a distinctly critical view of neoclassical economics, with its tidy mathematical models and crisp axioms about rational actors.

The history of capitalism has also benefited from a surge of new, economically minded scholarship on slavery, with scholars increasingly arguing that Northern factories and Southern plantations were not opposing economic systems, as the old narrative has it, but deeply entwined.

In a paper called “Toxic Debt, Liar Loans and Securitized Human Beings: The Panic of 1837 and the Fate of Slavery,” Edward Baptist, a historian at Cornell, looked at the way small investors across America and Europe snapped up exotic financial instruments based on slave holdings, much as people over the past decade went wild for mortgage-backed securities and collateralized debt obligations — with a similarly disastrous outcome.

a human life is not just a means to produce outcomes, it is an end in itself. When we evaluate our friends, we don’t just measure the consequences of their lives. We measure who they intrinsically are. We don’t merely want to know if they have done good. We want to know if they are good.

We live in a relentlessly commercial culture, so it’s natural that many people would organize their lives in utilitarian and consequentialist terms. But it’s possible to get carried away with this kind of thinking — to have logic but no wisdom