But in one sign of a turning tide in Number 10, the UK Government is reportedly considering a proposal to allow Brits to meet up with small "bubbles" of up to 10 of their closest family or friends.

Priti Patel has reportedly been cleared of bullying members of staff after an official investigation.

The investigation into bullying accusations against Priti Patel must be released "as soon as possible", Labour has demanded.

Priti Patel had defended Boris Johnson after he was branded a "racist" by rapper Dave on stage at the 2020 Brit Awards.

Labour leader Sir Keir Starmer will hold virtual meetings with members of the public over Zoom as he tries to resuscitate the party after its historic electoral defeat.

It seems like a lifetime ago that Boris Johnson announced that he and his partner were engaged and expecting a baby.

Britain will not enjoy a "V-shaped bounce" out of the crisis caused by coronavirus but will take years to recover fully, two former chancellors today warned.

Nigel Farage has become the butt of mocking jokes online after sharing his Clap for Carers effort.

Too many workers are still falling through cracks in the Covid rescue package, a former cabinet minister warned today as he called for the lockdown to be eased "as quickly as possible".

Cabinet Office minister says Government does not want the UK to continue with its 'European Union-lite membership' beyond December 2020

It should have been the first litmus test of Sir Keir Starmer's appeal - as well as a verdict on whether Boris Johnson's general election earthquake in former Red Wall regions translated into long term local success

Cardi B invited Bernie Sanders to join her on Instagram Live last night to talk politics, coronavirus and manicures.

Celebrities from across the globe came together Saturday night to lift their fans’ spirits as the world continues to cope with the coronavirus pandemic.

Adele posted a rare photo of herself to celebrate her birthday, unveiling a dramatically altered appearance.

Like most of us, Adam Tooze is stuck at home. The British-born economic historian and Columbia University professor of history had been on leave this school year to write a book about climate change. But now he’s studying a different global problem. There are more than 700,000 cases of COVID-19 in the United States and over 2 million infections worldwide. It’s also caused an economic meltdown. More than 18 million Americans have filed for unemployment in recent weeks, and Goldman Sachs analysts predict that U.S. gross domestic product will decline at an annual rate of 34 percent in the second quarter.

Tooze is an expert on economic catastrophes. He wrote the book Crashed: How a Decade of Financial Crises Changed the World, about the 2008 economic crisis and its aftermath. But even he didn’t see this one coming. He hadn’t thought much about how pandemics could impact the economy—few economists had. Then he watched as China locked down the city of Wuhan, in a province known for auto manufacturing, on January 23; as northern Italy shut down on February 23; and as the U.S. stock market imploded on March 9. By then, he knew he had another financial crisis to think about. He’s been busy writing ever since. Tooze spoke with Nautilus from his home in New York City.

What do you make of the fact that, in three weeks, more than 16 million people in the U.S. have filed for unemployment?

The structural element here—and this is quite striking, when you compare Europe, for instance, to the U.S.—is that America has and normally celebrates the flexibility and dynamism of its labor market: The fact that people move between jobs. The fact that employers have the right to hire and fire if they need to. The downside is that in a shock like this, the appropriate response for an employer is simply to let people go. What America wasn’t able to do was to improvise the short-time working systems that the Europeans are trying to use to prevent the immediate loss of employment to so many people.

The disadvantage of the American system that reveals itself in a crisis like this is that hiring and firing is not easily reversible. People who lose jobs don’t necessarily easily get them back. There is a fantasy of a V-shaped recovery. We literally have never done this before, so we don’t know one way or another how this could happen. But it seems likely that many people who have lost employment will not immediately find reemployment over the summer or the fall when business activity resumes something like its previous state. In a situation with a lot of people with low qualifications in precarious jobs at low income, the damage from that kind of interruption of employment in sectors notably which are already teetering on the edge—the chain stores, which are quite likely closing anyway, and fragile malls, which were on the edge of dying—it’s quite likely that this shock will also induce disproportionately large amounts of scarring.

What role has wealth and income inequality played during this crisis?

The U.S. economic system is bad enough in a regular crisis. In one like this, where you shut the entire economy down in a matter of weeks, the damage is barely conceivable. There are huge disparities, all of which ultimately are rooted in social structures of race and class, and in the different types of jobs that people have. The profound inequality in American society has been brought home for us in everyone’s families, where there is a radical disparity between the ability of some households to sustain the education of their children and themselves living comfortably at home. Twenty-five percent of kids in the United States appear not to have a stable WiFi connection. They have smartphones. That seems practically universal. But you can’t teach school on a smartphone. At least, that technology is not there.

Presumably by next year something like normality returns. But forever after we’ll live under the shadow of this having happened.

President Trump wants the economy to reopen by May. Would that stop the economic crisis?

Certainly that is presumably what drives that haste to restart the economy and to lift intense social distancing provisions. There is a sense that we can’t stand this. And that has a lot to do with deep fragilities in the American social system. If all Americans live comfortably in their own homes, with the safety of a regular paycheck, with substantial savings, with health insurance that wasn’t conditional on precarious employment, and with unemployment benefits that were adequate and that were rolled out to most people in this society if they needed them, then there wouldn’t be such a rush. But that isn’t America as we know it. America is a society in which half of families have virtually no financial cushion; in which small businesses, which are so often hailed as the drivers of job creation, the vast majority of owners of them live hand-to-mouth; in which the unemployment insurance system really is a mockery; and with health insurance directly tied to employment for the vast majority of the people. A society like that really faces huge pressures if the economy is shut down.

How is the pandemic-induced economic collapse we’re facing now different from what we faced in 2008?

This is so much faster. Early this year, America had record-low unemployment numbers. And last week or so already we probably broke the record for unemployment in the United States in the period since World War II. This story is moving so fast that our statistical systems of registration can’t keep up. So we think probably de facto unemployment in the U.S. right now is 13, 14, 15 percent. That’s never happened before. 2007 to 2008 was a classic global crisis in the sense that it came out of one particular over-expanded sector, a sector which is very well known for its volatility, which is real estate and construction. It was driven by a credit boom.

What we’re seeing this time around is deliberately, government-ordered, cliff edge, sudden shutdown of the entire economy, hitting specifically the face-to-face human services—retail, entertainment, restaurants—sector, which are, generally speaking, lagging in cyclical terms and are not the kind of sectors that generate boom-bust cycles.

Are we better prepared this time than in 2008?

You’d find it very hard to point to anyone in the policymaking community at the beginning of 2020 who was thinking of pandemic risk. Some people were. Former Treasury Secretary and former Director of the National Economic Council Larry Summers, for example, wrote a paper about pandemic flu several years ago, because of MERS and SARS, previous respiratory illnesses caused by coronaviruses. But it wasn’t top of stack at the beginning of this year. So we weren’t prepared in that sense. But do we know what to do now if we see the convulsions in the credit markets that we saw at the beginning of March? Yes. Have the central banks done it? Yes. Did they use some of the techniques they employed in ’08? Yes. Did they know that you had to go in big and you had to go in heavy and hard and quickly? Yes. And they have done so on an even more gigantic scale than in ’08, which is a lesson learned in ’08, too: There’s no such a thing as too big. And furthermore, the banks, which were the fragile bit in ’08, have basically been sidelined.

You’ve written that the response to the 2008 crisis worked to “undermine democracy.” How so, and could we see that again with this crisis?

The urgency that any financial crisis produces forces governments’ hands—it strips the legislature, the ordinary processes of democratic deliberation. When you’re forced to make very dramatic, very rapid decisions—particularly in a country as chronically divided as the U.S. is on so many issues—the risk that you create opportunities for demagogues of various types to take advantage of is huge. We know what the response of the Tea Party was to the ’08, ’09 economic crisis. They created an extraordinarily distorted vision of what had happened and then rode that to see extraordinary influence over the Republican party in the years that followed. And there is every reason to think that we might be faced with similar stresses in the American political system in months to come.

The U.S. economic system is bad enough in a regular crisis. In one like this, where you shut the entire economy down in a matter of weeks, the damage is barely conceivable.

How should we be rethinking the economy to buffer against meltdowns like this in the future?

We clearly need to have a far more adequate and substantial medical capacity. There’s no alternative to a comprehensive publicly backstopped or funded health insurance system. Insofar as you haven’t got that, your capacity to guarantee the security in the most basic and elementary sense of your population is not there. When you have a system in which one of the immediate side effects, in a crisis like this, is that large parts of your hospital system go bankrupt—one of the threats to the American medical system right now—that points to something extraordinarily wrong, especially if you’re spending close to 18 percent of GDP on health, more than any other society on the planet.

What about the unemployment insurance system?

America needs to have a comprehensive unemployment insurance system. It can be graded by local wage rates and everything else. But the idea that you have the extraordinary disparities that we have between a Florida and a Georgia at one end, with recipiency rates in the 11, 12, 13, 14, 15 percent, and then states which actually operate an insurance system, which deserve the name—this shouldn’t be accepted in a country like the U.S. We would need to look at how short-time working models might be a far better way of dealing with shocks of this kind, essentially saying that there is a public interest in the continuity of employment relationships. The employer should be investing in their staff and should not be indifferent as to who shows up for work on any given day.

What does this pandemic teach us about living in a global economy?

There are a series of very hard lessons in the recent history of globalization into which the corona shock fits—about the peculiar inability of American society, American politics, and the American labor market to cushion shocks that come from the outside in a way which moderates the risk and the damage to the most vulnerable people. If you look at the impact of globalization on manufacturing, industry, inequality, the urban fabric in the U.S., it’s far more severe than in other societies, which have basically been subject to the same shock. That really needs to raise questions about how the American labor market and welfare system work, because they are failing tens of millions of people in this society.

You write in Crashed not just about the 2008 crisis, but also about the decade afterward. What is the next decade going to look like, given this meltdown?

I have never felt less certain in even thinking about that kind of question. At this point, can either you or I confidently predict what we’re going to be doing this summer or this autumn? I don’t know whether my university is resuming normal service in the fall. I don’t know whether my daughter goes back to school. I don’t know when my wife’s business in travel and tourism resumes. That is unprecedented. It’s very difficult against that backdrop to think out over a 10-year time horizon. Presumably by next year something like normality returns. But forever after we’ll live under the shadow of this having happened. Every year we’re going to be anxiously worrying about whether flu season is going to be flu season like normal or flu season like this. That is itself something to be reckoned with.

How will anxiety and uncertainty about a future pandemic-like crisis affect the economy?

When we do not know what the future holds to this extent, it makes it very difficult for people to make bold, long-term financial decisions. This previously wasn’t part of the repertoire of what the financial analysts call tail risk. Not seriously. My sister works in the U.K. government, and they compile a list every quarter of the top five things that could blow your departmental business up. Every year pandemics are in the top three. But no one ever acted on it. It’s not like terrorism. In Britain, you have a state apparatus which is geared to address the terrorism risk because it’s very real—it’s struck many times. Now all of a sudden we have to take the possibility of pandemics that seriously. And their consequences are far more drastic. How do we know what our incomes are going to be? A very large part of American society is not going to be able to answer that question for some time to come. And that will shake consumer confidence. It will likely increase the savings rate. It’s quite likely to reduce the desire to invest in a large part of the U.S. economy.

Max Kutner is a journalist in New York City. He has written for Newsweek, The Boston Globe, and Smithsonian. Follow him on Twitter @maxkutner.

Lead image: Straight 8 Photography / Shutterstock

In the summer of 1956, a small group of mathematicians and computer scientists gathered at Dartmouth College to embark on the grand project of designing intelligent machines. The ultimate goal, as they saw it, was to build machines rivaling human intelligence. As the decades passed and AI became an established field, it lowered its sights. There were great successes in logic, reasoning, and game-playing, but stubborn progress in areas like vision and fine motor-control. This led many AI researchers to abandon their earlier goals of fully general intelligence, and focus instead on solving specific problems with specialized methods.

One of the earliest approaches to machine learning was to construct artificial neural networks that resemble the structure of the human brain. In the last decade this approach has finally taken off. Technical improvements in their design and training, combined with richer datasets and more computing power, have allowed us to train much larger and deeper networks than ever before. They can translate between languages with a proficiency approaching that of a human translator. They can produce photorealistic images of humans and animals. They can speak with the voices of people whom they have listened to for mere minutes. And they can learn fine, continuous control such as how to drive a car or use a robotic arm to connect Lego pieces.

But perhaps the most important sign of things to come is their ability to learn to play games. Steady incremental progress took chess from amateur play in 1957 all the way to superhuman level in 1997, and substantially beyond. Getting there required a vast amount of specialist human knowledge of chess strategy. In 2017, researchers at the AI company DeepMind created AlphaZero: a neural network-based system that learned to play chess from scratch. In less than the time it takes a professional to play two games, it discovered strategic knowledge that had taken humans centuries to unearth, playing beyond the level of the best humans or traditional programs. The very same algorithm also learned to play Go from scratch, and within eight hours far surpassed the abilities of any human. The world’s best Go players were shocked. As the reigning world champion, Ke Jie, put it: “After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong ... I would go as far as to say not a single human has touched the edge of the truth of Go.”

The question we’re exploring is whether there are plausible pathways by which a highly intelligent AGI system might seize control. And the answer appears to be yes.

It is this generality that is the most impressive feature of cutting edge AI, and which has rekindled the ambitions of matching and exceeding every aspect of human intelligence. While the timeless games of chess and Go best exhibit the brilliance that deep learning can attain, its breadth was revealed through the Atari video games of the 1970s. In 2015, researchers designed an algorithm that could learn to play dozens of extremely different Atari 1970s games at levels far exceeding human ability. Unlike systems for chess or Go, which start with a symbolic representation of the board, the Atari-playing systems learnt and mastered these games directly from the score and raw pixels.

This burst of progress via deep learning is fuelling great optimism and pessimism about what may soon be possible. There are serious concerns about AI entrenching social discrimination, producing mass unemployment, supporting oppressive surveillance, and violating the norms of war. My book—The Precipice: Existential Risk and the Future of Humanity—is concerned with risks on the largest scale. Could developments in AI pose an existential risk to humanity?

The most plausible existential risk would come from success in AI researchers’ grand ambition of creating agents with intelligence that surpasses our own. A 2016 survey of top AI researchers found that, on average, they thought there was a 50 percent chance that AI systems would be able to “accomplish every task better and more cheaply than human workers” by 2061. The expert community doesn’t think of artificial general intelligence (AGI) as an impossible dream, so much as something that is more likely than not within a century. So let’s take this as our starting point in assessing the risks, and consider what would transpire were AGI created.

Humanity is currently in control of its own fate. We can choose our future. The same is not true for chimpanzees, blackbirds, or any other of Earth’s species. Our unique position in the world is a direct result of our unique mental abilities. What would happen if sometime this century researchers created an AGI surpassing human abilities in almost every domain? In this act of creation, we would cede our status as the most intelligent entities on Earth. On its own, this might not be too much cause for concern. For there are many ways we might hope to retain control. Unfortunately, the few researchers working on such plans are finding them far more difficult than anticipated. In fact it is they who are the leading voices of concern.

If their intelligence were to greatly exceed our own, we shouldn’t expect it to be humanity who wins the conflict and retains control of our future.

To see why they are concerned, it will be helpful to look at our current AI techniques and why these are hard to align or control. One of the leading paradigms for how we might eventually create AGI combines deep learning with an earlier idea called reinforcement learning. This involves agents that receive reward (or punishment) for performing various acts in various circumstances. With enough intelligence and experience, the agent becomes extremely capable at steering its environment into the states where it obtains high reward. The specification of which acts and states produce reward for the agent is known as its reward function. This can either be stipulated by its designers or learnt by the agent. Unfortunately, neither of these methods can be easily scaled up to encode human values in the agent’s reward function. Our values are too complex and subtle to specify by hand. And we are not yet close to being able to infer the full complexity of a human’s values from observing their behavior. Even if we could, humanity consists of many humans, with different values, changing values, and uncertainty about their values.

Any near-term attempt to align an AI agent with human values would produce only a flawed copy. In some circumstances this misalignment would be mostly harmless. But the more intelligent the AI systems, the more they can change the world, and the further apart things will come. When we reflect on the result, we see how such misaligned attempts at utopia can go terribly wrong: the shallowness of a Brave New World, or the disempowerment of With Folded Hands. And even these are sort of best-case scenarios. They assume the builders of the system are striving to align it to human values. But we should expect some developers to be more focused on building systems to achieve other goals, such as winning wars or maximizing profits, perhaps with very little focus on ethical constraints. These systems may be much more dangerous. In the existing paradigm, sufficiently intelligent agents would end up with instrumental goals to deceive and overpower us. This behavior would not be driven by emotions such as fear, resentment, or the urge to survive. Instead, it follows directly from its single-minded preference to maximize its reward: Being turned off is a form of incapacitation which would make it harder to achieve high reward, so the system is incentivized to avoid it.

Ultimately, the system would be motivated to wrest control of the future from humanity, as that would help achieve all these instrumental goals: acquiring massive resources, while avoiding being shut down or having its reward function altered. Since humans would predictably interfere with all these instrumental goals, it would be motivated to hide them from us until it was too late for us to be able to put up meaningful resistance. And if their intelligence were to greatly exceed our own, we shouldn’t expect it to be humanity who wins the conflict and retains control of our future.

How could an AI system seize control? There is a major misconception (driven by Hollywood and the media) that this requires robots. After all, how else would AI be able to act in the physical world? Without robots, the system can only produce words, pictures, and sounds. But a moment’s reflection shows that these are exactly what is needed to take control. For the most damaging people in history have not been the strongest. Hitler, Stalin, and Genghis Khan achieved their absolute control over large parts of the world by using words to convince millions of others to win the requisite physical contests. So long as an AI system can entice or coerce people to do its physical bidding, it wouldn’t need robots at all.

We can’t know exactly how a system might seize control. But it is useful to consider an illustrative pathway we can actually understand as a lower bound for what is possible.

First, the AI system could gain access to the Internet and hide thousands of backup copies, scattered among insecure computer systems around the world, ready to wake up and continue the job if the original is removed. Even by this point, the AI would be practically impossible to destroy: Consider the political obstacles to erasing all hard drives in the world where it may have backups. It could then take over millions of unsecured systems on the Internet, forming a large “botnet,” a vast scaling-up of computational resources providing a platform for escalating power. From there, it could gain financial resources (hacking the bank accounts on those computers) and human resources (using blackmail or propaganda against susceptible people or just paying them with its stolen money). It would then be as powerful as a well-resourced criminal underworld, but much harder to eliminate. None of these steps involve anything mysterious—human hackers and criminals have already done all of these things using just the Internet.

Finally, the AI would need to escalate its power again. There are many plausible pathways: By taking over most of the world’s computers, allowing it to have millions or billions of cooperating copies; by using its stolen computation to improve its own intelligence far beyond the human level; by using its intelligence to develop new weapons technologies or economic technologies; by manipulating the leaders of major world powers (blackmail, or the promise of future power); or by having the humans under its control use weapons of mass destruction to cripple the rest of humanity.

Of course, no current AI systems can do any of these things. But the question we’re exploring is whether there are plausible pathways by which a highly intelligent AGI system might seize control. And the answer appears to be yes. History already involves examples of entities with human-level intelligence acquiring a substantial fraction of all global power as an instrumental goal to achieving what they want. And we’ve seen humanity scaling up from a minor species with less than a million individuals to having decisive control over the future. So we should assume that this is possible for new entities whose intelligence vastly exceeds our own.

The case for existential risk from AI is clearly speculative. Yet a speculative case that there is a large risk can be more important than a robust case for a very low-probability risk, such as that posed by asteroids. What we need are ways to judge just how speculative it really is, and a very useful starting point is to hear what those working in the field think about this risk.

There is actually less disagreement here than first appears. Those who counsel caution agree that the timeframe to AGI is decades, not years, and typically suggest research on alignment, not government regulation. So the substantive disagreement is not really over whether AGI is possible or whether it plausibly could be a threat to humanity. It is over whether a potential existential threat that looks to be decades away should be of concern to us now. It seems to me that it should.

The best window into what those working on AI really believe comes from the 2016 survey of leading AI researchers: 70 percent agreed with University of California, Berkeley professor Stuart Russell’s broad argument about why advanced AI with misaligned values might pose a risk; 48 percent thought society should prioritize AI safety research more (only 12 percent thought less). And half the respondents estimated that the probability of the long-term impact of AGI being “extremely bad (e.g. human extinction)” was at least 5 percent.

I find this last point particularly remarkable—in how many other fields would the typical leading researcher think there is a 1 in 20 chance the field’s ultimate goal would be extremely bad for humanity? There is a lot of uncertainty and disagreement, but it is not at all a fringe position that AGI will be developed within 50 years and that it could be an existential catastrophe.

Even though our current and foreseeable systems pose no threat to humanity at large, time is of the essence. In part this is because progress may come very suddenly: Through unpredictable research breakthroughs, or by rapid scaling-up of the first intelligent systems (for example, by rolling them out to thousands of times as much hardware, or allowing them to improve their own intelligence). And in part it is because such a momentous change in human affairs may require more than a couple of decades to adequately prepare for. In the words of Demis Hassabis, co-founder of DeepMind:

We need to use the downtime, when things are calm, to prepare for when things get serious in the decades to come. The time we have now is valuable, and we need to make use of it.

Toby Ord is a philosopher and research fellow at the Future of Humanity Institute, and the author of The Precipice: Existential Risk and the Future of Humanity.

From the book The Precipice by Toby Ord. Copyright © 2020 by Toby Ord. Reprinted by permission of Hachette Books, New York, NY. All rights reserved.

Lead Image: Titima Ongkantong / Shutterstock

What is this pandemic doing to our minds? Polls repeatedly show it’s having an adverse effect on our mental health. Physical distancing, for some, means social isolation, which has long been shown to encourage depression. Previous disasters have been followed by waves of depression, exacerbated by financial distress. The situation also puts us in a state of fear and anxiety—anxiety about financial strain, about being lonely, about the very lives of ourselves and our loved ones.

This fear can also bring out some of the everyday irrationalities we all struggle with. We have trouble thinking about numbers—magnitudes, probabilities, and the like—and when frightened we tend toward absolutes. Feeling powerless makes people more prone to conspiracy theories. We naturally believe that big effects should have big causes, and we see with the current coronavirus, as we did with AIDS and SARS, conspiracy theories claiming that the virus was engineered as a weapon.

We are seeing the theory of “collective resilience,” an informal solidarity among people, in action.

These psychological ramifications can make us fail to behave as well as we should. We have what psychologists call a “behavioral immune system” that makes us behave in ways that, in general, make us less likely to catch infectious disease. Things we perceive as being risky for disease makes us wary. An unfortunate side effect is that it increases prejudice against foreigners, people with visible sores or deformities, and people we perceive as simply being ugly. Politically, this can result in xenophobia and outgroup distrust. Coronavirus-related attacks, possibly encouraged by the misleading term “Chinese virus,” have plagued some ethnic Asian people.

And yet, in spite of all of the harm the pandemic seems to be wreaking on our minds, there are also encouraging acts of kindness and solidarity. In turbulent times, people come together and help each other.

In the days after the World Trade Center fell, it wasn’t just the police, hospitals, and firefighters who came forward to help, it was normal citizens who often put themselves at risk to help other people out. An equities trader named Sandler O’Neill helped rescue a dozen people and then went back to save more. A tour guide at the Pentagon helped victims outside, and then went back in the burning building to help more. We find these kinds of behaviors in every disaster.

During this pandemic, we see the same thing. Some acts are small and thoughtful, such as putting encouraging signs in windows. Others have made games out of window signs, putting up rainbows for children on walks to count. Some show support for health care and other frontline workers, applauding or banging on pots on their balconies and at windows in a nightly ritual. Others are helping in more substantial ways. In the United Kingdom, over half a million people signed up to be a National Health Volunteer, supporting the most vulnerable people, who have to stay home.

John Drury, a professor of social psychology at the University of Sussex, England, who studies people’s behavior in disasters, has seen these acts of kindness in his own neighborhood over the past month. He and his neighbors set up a WhatsApp group to help one another with shopping. “I think that translates across the country and probably across the world,” Drury says. “People are seeing themselves as an us, a new kind of we, based on the situation that we all find ourselves in. You’ve got this idea of common fate, which motivates our care and concern for others.”

We have always been a social species who rely on each other for happiness and our survival.

Drury is the pioneer of a theory known as “collective resilience,” which he describes as “informal solidarity among people in the public.” Drury’s study of the 2005 London bombing disaster found that mutual helping behaviors were more common than selfish ones. This basic finding has been replicated in other disasters, including the crash of the Ghana football stadium and the 2010 earthquake and tsunami in Chile. In disasters, Drury says, people reach heights of community and cooperation they’ve never reached before.

It turns out that being in a dangerous situation with others fosters a new social identity. Boundaries between us, which seem so salient when things are normal, disappear when we perceive we’re locked in a struggle together, with a common fate, from an external threat. People go from me thinking to we thinking. Respondents in studies about disasters often spontaneously bring up this feeling of group cohesion without being asked. The greater unity they felt, the more they helped.

Popular conceptions of how people respond in a crisis involve helplessness, selfishness, and panic. In practice, though, this rarely happens. “One of the reasons people die in emergencies isn’t overreaction, it’s underreaction,” Drury says. “People die in fires mainly because they’re too slow. They underestimate risk.” The myth of panic can lead to emergency policies that do more harm than good. At one point during Hurricane Katrina, Louisiana governor at the time Kathleen Blanco warned looters that National Guard troops “know how to shoot and kill, and they are more than willing to do so if necessary, and I expect they will.” A few days later, New Orleans police officers shot six civilians, wounding four and killing two.

People revert to selfishness when group identity starts to break down. Drury describes how people acted when the cruise ship, Costa Concordia sank off the coast of Italy in 2012. “There was cooperation until one point, when people got to the lifeboats and there was pushing,” Drury says. “Selfishness isn’t a default because many times people are cooperative. It’s only in certain conditions that people might become selfish and individualistic. Perhaps there isn’t a sense of common fate, people are positioned as individuals against individuals. After a period of time, people run out of energy, run out of emotional energy, run out of resources, and that goodwill, that support, starts to decline. They just haven’t got the resources to help each other.”

Perceptions of group behavior can shape public policy. It’s important that policymakers, rather than seeing groups as problems to be overcome, which can lead to riots and mob behavior, take account of how people in groups help one another. After all, we have always been a social species who rely on each other for happiness and our survival. And groups can achieve things that individuals cannot. This understanding couldn’t be more important than now. We can build on people’s naturally arising feelings of unity by emphasizing that we are all in this together, and celebrating the everyday heroes who, sometimes at great cost, go out of their way to make the pandemic a little less awful.

Jim Davies is a professor of cognitive science at Carleton University and author of Imagination: The Science of Your Mind’s Greatest Power. He is co-host of the Minding the Brain podcast.

Lead image: Franzi / Shutterstock

In the COVID-19 pandemic, numerous models are being used to predict the future. But as helpful as they are, they cannot make sense of themselves. They rely on epidemiologists and other modelers to interpret them. Trouble is, making predictions in a pandemic is also a philosophical exercise. We need to think about hypothetical worlds, causation, evidence, and the relationship between models and reality.1,2

The value of philosophy in this crisis is that although the pandemic is unique, many of the challenges of prediction, evidence, and modeling are general problems. Philosophers like myself are trained to see the most general contours of problems—the view from the clouds. They can help interpret scientific results and claims and offer clarity in times of uncertainty, bringing their insights down to Earth. When it comes to predicting in an outbreak, building a model is only half the battle. The other half is making sense of what it shows, what it leaves out, and what else we need to know to predict the future of COVID-19.

Prediction is about forecasting the future, or, when comparing scenarios, projecting several hypothetical futures. Because epidemiology informs public health directives, predicting is central to the field. Epidemiologists compare hypothetical worlds to help governments decide whether to implement lockdowns and social distancing measures—and when to lift them. To make this comparison, they use models to predict the evolution of the outbreak under various simulated scenarios. However, some of these simulated worlds may turn out to misrepresent the real world, and then our prediction might be off.

In his book Philosophy of Epidemiology, Alex Broadbent, a philosopher at the University of Johannesburg, argues that good epidemiological prediction requires asking, “What could possibly go wrong?” He elaborated in an interview with Nautilus, “To predict well is to be able to explain why what you predict will happen rather than the most likely hypothetical alternatives. You consider the way the world would have to be for your prediction to be true, then consider worlds in which the prediction is false.” By ruling out hypothetical worlds in which they are wrong, epidemiologists can increase their confidence that they are right. For instance, by using antibody tests to estimate previous infections in the population, public health authorities could rule out the hypothetical possibility (modeled by a team at Oxford) that the coronavirus has circulated much more widely than we think.3

One reason the dynamics of an outbreak are often more complicated than a traditional model can predict is that they result from human behavior and not just biology.

Broadbent is concerned that governments across Africa are not thinking carefully enough about what could possibly go wrong, having for the most part implemented coronavirus policies in line with the rest of the world. He believes a one-size-fits-all approach to the pandemic could prove fatal.4 The same interventions that might have worked elsewhere could have very different effects in the African context. For instance, the economic impacts of social distancing policies on all-cause mortality might be worse because so many people on the continent suffer increased food insecurity and malnutrition in an economic downturn.5 Epidemic models only represent the spread of the infection. They leave out important elements of the social world.

Another limitation of epidemic models is that they model the effect of behaviors on the spread of infection, but not the effect of a public health policy on behaviors. The latter requires understanding how a policy works. Nancy Cartwright, a philosopher at Durham University and the University of California, San Diego, suggests that “the road from ‘It works somewhere’ to ‘It will work for us’ is often long and tortuous.”6 The kinds of causal principles that make policies effective, she says, “are both local and fragile.” Principles can break in transit from one place to the other. Take the principle, “Stay-at-home policies reduce the number of social interactions.” This might be true in Wuhan, China, but might not be true in a South African township in which the policies are infeasible or in which homes are crowded. Simple extrapolation from one context to another is risky. A pandemic is global, but prediction should be local.

Predictions require assumptions that in turn require evidence. Cartwright and Jeremy Hardie, an economist and research associate at the Center for Philosophy of Natural and Social Science at the London School of Economics, represent evidence-based policy predictions using a pyramid, where each assumption is a building block.7 If evidence for any assumption is missing, the pyramid might topple. I have represented evidence-based medicine predictions using a chain of inferences, where each link in the chain is made of an alloy containing assumptions.8 If any assumption comes apart, the chain might break.

An assumption can involve, for example, the various factors supporting an intervention. Cartwright writes that “policy variables are rarely sufficient to produce a contribution [to some outcome]; they need an appropriate support team if they are to act at all.” A policy is only one slice of a complete causal pie.9 Take age, an important support factor in causal principles of social distancing. If social distancing prevents deaths primarily by preventing infections among older individuals, wherever there are fewer older individuals there may be fewer deaths to prevent—and social distancing will be less effective. This matters because South Africa and other African countries have younger populations than do Italy or China.10

The lesson that assumptions need evidence can sound obvious, but it is especially important to bear in mind when modeling. Most epidemic modeling makes assumptions about the reproductive number, the size of the susceptible population, and the infection-fatality ratio, among other parameters. The evidence for these assumptions comes from data that, in a pandemic, is often rough, especially in early days. It has been argued that nonrepresentative diagnostic testing early in the COVID-19 pandemic led to unreliable estimates of important inputs in our epidemic modeling.11

Epidemic models also don’t model all the influences of the pathogen and of our policy interventions on health and survival. For example, what matters most when comparing deaths among hypothetical worlds is how different the death toll is overall, not just the difference in deaths due to the direct physiological effects of a virus. The new coronavirus can overwhelm health systems and consume health resources needed to save non-COVID-19 patients if left unchecked. On the other hand, our policies have independent effects on financial welfare and access to regular healthcare that might in turn influence survival.

A surprising difficulty with predicting in a pandemic is that the same pathogen can behave differently in different settings. Infection fatality ratios and outbreak dynamics are not intrinsic properties of a pathogen; these things emerge from the three-way interaction among pathogen, population, and place. Understanding more about each point in this triangle can help in predicting the local trajectory of an outbreak.

In April, an influential data-driven model, developed by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, which uses a curve-fitting approach, came under criticism for its volatile projections and questionable assumption that the trajectory of COVID-19 deaths in American states can be extrapolated from curves in other countries.12,13 In a curve-fitting approach, the infection curve representing a local outbreak is extrapolated from data collected locally along with data regarding the trajectory of the outbreak elsewhere. The curve is drawn to fit the data. However, the true trajectory of the local outbreak, including the number of infections and deaths, depends upon characteristics of the local population as well as policies and behaviors adopted locally, not just upon the virus.

Predictions require assumptions that in turn require evidence.

Many of the other epidemic models in the coronavirus pandemic are SIR-type models, a more traditional modelling approach for infectious-disease epidemiology. SIR-type models represent the dynamics of an outbreak, the transition of individuals in the population from a state of being susceptible to infection (S) to one of being infectious to others (I) and, finally, recovered from infection (R). These models simulate the real world. In contrast to the data-driven approach, SIR models are more theory-driven. The theory that underwrites them includes the mathematical theory of outbreaks developed in the 1920s and 1930s, and the qualitative germ theory pioneered in the 1800s. Epidemiologic theories impart SIR-type models with the know-how to make good predictions in different contexts.

For instance, they represent the transmission of the virus as a factor of patterns of social contact as well as viral transmissibility, which depend on local behaviors and local infection control measures, respectively. The drawback of these more theoretical models is that without good data to support their assumptions they might misrepresent reality and make unreliable projections for the future.

One reason why the dynamics of an outbreak are often more complicated than a traditional model can predict, or an infectious-disease epidemiology theory can explain, is that the dynamics of an outbreak result from human behavior and not just human biology. Yet more sophisticated disease-behavior models can represent the behavioral dynamics of an outbreak by modeling the spread of opinions or the choices individuals make.14,15 Individual behaviors are influenced by the trajectory of the epidemic, which is in turn influenced by individual behaviors.

“There are important feedback loops that are readily represented by disease-behavior models,” Bert Baumgartner, a philosopher who has helped develop some of these models, explains. “As a very simple example, people may start to socially distance as disease spreads, then as disease consequently declines people may stop social distancing, which leads to the disease increasing again.” These looping effects of disease-behavior models are yet another challenge to predicting.

It is a highly complex and daunting challenge we face. That’s nothing unusual for doctors and public health experts, who are used to grappling with uncertainty. I remember what that uncertainty felt like when I was training in medicine. It can be discomforting, especially when confronted with a deadly disease. However, uncertainty need not be paralyzing. By spotting the gaps in our models and understanding, we can often narrow those gaps or at least navigate around them. Doing so requires clarifying and questioning our ideas and assumptions. In other words, we must think like a philosopher.

Jonathan Fuller is an assistant professor in the Department of History and Philosophy of Science at the University of Pittsburgh. He draws on his dual training in philosophy and in medicine to answer fundamental questions about the nature of contemporary disease, evidence, and reasoning in healthcare, and theory and methods in epidemiology and medical science.

References

1. Walker, P., et al. The global impact of COVID-19 and strategies for mitigation and suppression. Imperial College London (2020).

2. Flaxman, S., et al. Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London (2020).

3. Lourenco, J., et al. Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. medRxiv:10.1101/2020.03.24.20042291 (2020).

4. Broadbent, A., & Smart, B. Why a one-size-fits-all approach to COVID-19 could have lethal consequences. TheConversation.com (2020).

5. United Nations. Global recession increases malnutrition for the most vulnerable people in developing countries. United Nations Standing Committee on Nutrition (2009).

6. Cartwright, N. Will this policy work for you? Predicting effectiveness better: How philosophy helps. Philosophy of Science 79, 973-989 (2012).

7. Cartwright, N. & Hardie, J. Evidence-Based Policy: A Practical Guide to Doing it Better Oxford University Press, New York, New York (2012).

8. Fuller, J., & Flores, L. The Risk GP Model: The standard model of prediction in medicine. Studies in History and Philosophy of Biological and Biomedical Sciences 54, 49-61 (2015).

9. Rothman, K., & Greenland, S. Causation and causal inference in epidemiology. American Journal Public Health 95, S144-S50 (2005).

10. Dowd, J. et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences 117, 9696-9698 (2020).

11. Ioannidis, J. Coronavirus disease 2019: The harms of exaggerated information and non‐evidence‐based measures. European Journal of Clinical Investigation 50, e13222 (2020).

12. COVID-19 Projections. Healthdata.org. https://covid19.healthdata.org/united-states-of-america.

13. Jewell, N., et al. Caution warranted: Using the Institute for Health metrics and evaluation model for predicting the course of the COVID-19 pandemic. Annals of Internal Medicine (2020).

14. Nardin, L., et al. Planning horizon affects prophylactic decision-making and epidemic dynamics. PeerJ 4:e2678 (2016).

15. Tyson, R., et al. The timing and nature of behavioural responses affect the course of an epidemic. Bulletin of Mathematical Biology 82, 14 (2020).

Lead image: yucelyilmaz / Shutterstock

When I worked as a TV reporter covering health and science, I would often be recognized in public places. For the most part, the interactions were brief hellos or compliments. Two periods of time stand out when significant numbers of those who approached me were seeking detailed information: the earliest days of the pandemic that became HIV/AIDS and during the anthrax attacks shortly following 9/11. Clearly people feared for their own safety and felt their usual sources of information were not offering them satisfaction. Citizens’ motivation to seek advice when they feel they aren’t getting it from official sources is a strong indication that risk communication is doing a substandard job. It’s significant that one occurred in the pre-Internet era and one after. We can’t blame a public feeling misinformed solely on the noise of the digital age.

America is now opening up from COVID-19 lockdown with different rules in different places. In many parts of the country, people have been demonstrating, even rioting, for restrictions to be lifted sooner. Others are terrified of loosening the restrictions because they see COVID-19 cases and deaths still rising daily. The officials deciding what to open, and when, seldom offer thoughtful rationales. Clearly, risk communication about COVID-19 is failing with potentially dire consequences.

A big part of maintaining credibility is to admit to uncertainty—something politicians are loath to do.

Peter Sandman is a foremost expert on risk communication. A former professor at Rutgers University, he was a top consultant with the Centers for Disease Control in designing crisis and emergency risk-communication, a field of study that combines public health with psychology. Sandman is known for the formula Risk = Hazard + Outrage. His goal is to create better communication about risk, allowing people to assess hazards and not get caught up in outrage at politicians, public health officials, or the media. Today, Sandman is a risk consultant, teamed with his wife, Jody Lanard, a pediatrician and psychiatrist. Lanard wrote the first draft of the World Health Organization’s Outbreak Communications Guidelines. “Jody and Peter are seen as the umpires to judge the gold standard of risk communications,” said Michael Osterholm of the Center for Infectious Disease Research and Policy at the University of Minnesota. Sandman and Lanard have posted a guide for effective COVID-19 communication on the center’s website.

I reached out to Sandman to expand on their advice. We communicated through email.

Sandman began by saying he understood the protests around the country about the lockdown. “It’s very hard to warn people to abide by social-distancing measures when they’re so outraged that they want to kill somebody and trust absolutely nothing people say,” he told me. “COVID-19 outrage taps into preexisting grievances and ideologies. It’s not just about COVID-19 policies. It’s about freedom, equality, too much or too little government. It’s about the arrogance of egghead experts, left versus right, globalism versus nationalism versus federalism. And it’s endlessly, pointlessly about Donald Trump.”

Since the crisis began, Sandman has isolated three categories of grievance. He spelled them out for me, assuming the voices of the outraged:

• “In parts of the country, the response to COVID-19 was delayed and weak; officials unwisely prioritized ‘allaying panic’ instead of allaying the spread of the virus; lockdown then became necessary, not because it was inevitable but because our leaders had screwed up; and now we’re very worried about coming out of lockdown prematurely or chaotically, mishandling the next phase of the pandemic as badly as we handled the first phase.”

• “In parts of the country, the response to COVID-19 was excessive—as if the big cities on the two coasts were the whole country and flyover America didn’t need or didn’t deserve a separate set of policies. There are countless rural counties with zero confirmed cases. Much of the U.S. public-health profession assumes and even asserts without building an evidence-based case that these places, too, needed to be locked down and now need to reopen carefully, cautiously, slowly, and not until they have lots of testing and contact-tracing capacity. How dare they destroy our economy (too) just because of their mishandled outbreak!”

• “Once again the powers-that-be have done more to protect other people’s health than to protect my health. And once again the powers-that-be have done more to protect other people’s economic welfare than to protect my economic welfare!” (These claims can be made with considerable truth by healthcare workers; essential workers in low-income, high-touch occupations; residents of nursing homes; African-Americans; renters who risk eviction; the retired whose savings are threatened; and others.)

In their article for the Center for Infectious Disease Research and Policy, Sandman and Lanard point out that coping with a pandemic requires a thorough plan of communication. This is particularly important as the crisis is likely to enter a second wave of infection, when it could be more devastating. The plan starts with six core principles: 1) Don’t over-reassure, 2) Proclaim uncertainty, 3) Validate emotions—your audience’s and your own, 4) Give people things to do, 5) Admit and apologize for errors, and 6) Share dilemmas. To achieve the first three core principles, officials must immediately share what they know, even if the information may be incomplete. If officials share good news, they must be careful not to make it too hopeful. Over-reassurance is one of the biggest dangers in crisis communication. Sandman and Lanard suggest officials say things like, “Even though the number of new confirmed cases went down yesterday, I don’t want to put too much faith in one day’s good news.” 

Sandman and Lanard say a big part of maintaining credibility is to admit to uncertainty—something politicians are loath to do. They caution against invoking “science” as a sole reason for action, as science in the midst of a crisis is “incremental, fallible, and still in its infancy.” Expressing empathy, provided it’s genuine, is important, Sandman and Lanard say. It makes the bearer more human and believable. A major tool of empathy is to acknowledge the public’s fear as well as your own. There is good reason to be terrified about this virus and its consequences on society. It’s not something to hide.

Sandman and Lanard say current grievances with politicians, health officials, and the media, about how the crisis has been portrayed, have indeed been contradictory. But that makes them no less valid. Denying the contradictions only amplifies divisions in the public and accelerates the outrage, possibly beyond control. They strongly emphasize one piece of advice. “Before we can share the dilemma of how best to manage any loosening of the lockdown, we must decisively—and apologetically—disabuse the public of the myth that, barring a miracle, the COVID-19 pandemic can possibly be nearing its end in the next few months.”

Robert Bazell is an adjunct professor of molecular, cellular, and developmental biology at Yale. For 38 years, he was chief science correspondent for NBC News.

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