UPMC and Pitt experts share details of a scientific breakthrough in COVID-19 drug development. Live stream begins at 2 p.m. EDT.
To Beat the Coronavirus, Build a Better Fence
IN MARCH, I WROTE TWO WIDELY READ ARTICLES about the emerging coronavirus pandemic, “Why You Must Act Now,” then “The Hammer and the Dance,” which called for a “hammer” (stringent measures to stop the virus) followed by a “dance” (intelligent but less aggressive actions to prevent the pandemic from coming back).
Since then, many countries have used a hammer: schools closed; businesses shuttered; public events were banned; masks were required; citizens were ordered to shelter at home.
All those actions helped slow the spread of the virus. But as the world failed to dance the right way, it has been facing resurgences of the pandemic. I’ve been examining the failures — and what needs to happen next time.
Measures like masks, testing, contact tracing, isolation, quarantines are still necessary, but one approach has not been emphasized enough: the fence. Countries that quickly closed their borders or carefully monitored anyone coming in have been most successful in slowing infections.
Some countries use fences to block outsiders from crossing their borders. Some countries limit travel within their borders. As the United States considers relaxing some border controls and European countries reimpose travel restrictions, they need to realize that these fences are necessary to control the virus — and if they are enforced, they’ll be effective.
Back in May, the coronavirus was out of control. Brazil, Russia and the United States had noticeably more cases than Japan, Taiwan and South Korea. You might think this would be because the first three have been more lax in their approach. Certainly, the leaders of these nations were half-hearted — and worse — in handling the virus.
But many regional governments within each country actually imposed quite severe restrictions on activity. This shows Oxford University’s Stringency Index, which zeroes in on the strictest measures in a country, whether part of a national, state or local government response to slow the virus, like closing businesses and limiting gatherings. As you can see, Brazil, Russia and the United States rank higher in the stringency of their measures — much stricter, overall, than Japan, Taiwan and South Korea. Yet those countries still had some of the worst outbreaks in the world.
That’s because their national governments didn’t coordinate a central response, leaving state governments to battle the virus largely on their own.
Some states pursued aggressive actions to slow the pandemic while others took a hands-off approach. Those differing responses hamstrung states that adopted stricter measures, since travel between states continued, undermining the efforts of hard-working governments to suppress the virus, and spreading it throughout the countries.
All of us need to start preparing for a deeply worrying scenario on Nov. 3. It is not some outlandish fantasy, but rather the most likely course of events based on what we know today. On election night, President Trump will be ahead significantly in a majority of states, including in the swing states that will decide the outcome. Over the next few days, mail-in ballots will be counted, and the numbers could shift in Joe Biden’s favor. But will Trump accept that outcome? Will the United States?
First, an explanation of why this is the most likely situation. Several surveys have found that, because of the pandemic, in-person and mail-in ballots will show a huge partisan divide. In one poll, 87 percent of Trump voters said they preferred to vote in person, compared with 47 percent of Biden voters. In another, by the Democratic data firm Hawkfish, 69 percent of Biden voters said they planned to vote by mail, while only 19 percent of Trump voters said the same. The firm modeled various scenarios and found that, based on recent polling, if just 15 percent of mail-in ballots are counted on election night, Trump would appear to have 408 electoral votes compared with Biden’s 130. But four days later, assuming 75 percent of the mail-in ballots are counted, the lead could flip to Biden, and after all ballots are counted, Biden would have 334 electoral votes to Trump’s 204.
You don’t have to believe in models to understand that this is a likely scenario. As David Graham writes in an Atlantic essay, on the night of the 2018 midterm elections, the results seemed very disappointing for Democrats. They appeared to have gained far fewer seats in the House and Senate than the polls predicted, a replay of 2016.
Except that as provisional ballots and mail-in ballots were counted, the results changed. “California just defies logic to me,” said Paul D. Ryan (R-Wis.), who was then speaker of the House. “We were only down 26 seats the night of the election, and three weeks later we lost basically every contested California race.” In fact, there are perfectly logical explanations for this “blue shift,” as scholars Edward Foley and Charles Stewart call it. But it’s easy to make it look suspicious.
After the 2018 midterms, Trump declared that a conspiracy was at work. In Florida, when Democrats started narrowing the gaps in two key races, he tweeted that “large numbers of new ballots showed up out of nowhere, and many ballots are missing or forged. An honest vote count is no longer possible — ballots massively infected. Must go with Election Night!” Imagine what Trump is likely to do this November, when his own fate hangs in the balance.
Dan Baer of the Carnegie Endowment for International Peace outlines a frightening and utterly plausible scenario in an excellent article, “How Trump could refuse to go.” Baer imagines close contests in Arizona and Florida, where Republican-controlled governments could argue that the election was marred by irregularities and change the law to allow themselves to appoint the Republican slate of electors.
Trump steals the election is by using state GOP officials and as necessary federal office-holders to refuse to certify some states’ vote counts/slates of electors via false voter fraud claims—throwing the election to the House delegations (GOP has more). Nancy Pelosi (D-Calif.), who is second in line of succession to the presidency, to succeed Trump is quite implausible. But it is not impossible.
The House cannot pick a president, the Senate cannot choose a vice president. The 20th Amendment is clear on what comes next: Trump’s term expires at noon on Jan. 20, 2021. So does Vice President Pence’s. The next officeholder in line, under federal law, is the Speaker of the House of Representatives. And that is Nancy Pelosi.
In Wisconsin, where state government is divided, Baer imagines the following sequence of events: “The Republican-controlled legislature also moves to change the manner of designating electors, and to approve those pledged to Trump. However, the Democratic Governor, invoking Wisconsin state law, signs and affixes the state seal to the slate of electors for Joe Biden as certified by the state elections commission.” In Baer’s vision, Trump mobilizes his base to go out and protest, tweeting, “thank you Wisconsin! don’t let your governor rob YOUR PRESIDENT!”
Is there a way out of this national nightmare? Two powerful forces could ensure that the United States, already tarnished by its handling of covid-19, does not also end up as the poster child for dysfunctional democracy. The first is the media. We have to abandon the notion of election night and prepare the public for election month. In fact, states have never certified winners on election night. News organizations do that on the basis of statistical projections. It is time to educate the public to wait for the ballots to be counted.
It’s a big election year, and one party’s candidate is the successor to a popular two-term president. A little-known company offers the other party, which is in disarray, technology that uses vast amounts of data to profile voters. The election is incredibly close — and the long-shot candidate wins.
This was 1960, not 2016, and the winning ticket was John F. Kennedy, not Donald Trump.
The little-known — and now nearly entirely forgotten — company was called Simulmatics, the subject of Harvard University historian and New Yorker writer Jill Lepore’s timely new book, If Then: How the Simulmatics Corporation Invented the Future.
Before Cambridge Analytica, before Facebook, before the Internet, there was Simulmatics’ “People Machine,” which was, in Lepore’s telling:
“A computer program designed to predict and manipulate human behavior, all sorts of human behavior, from buying a dishwasher to countering an insurgency to casting a vote.”
Lepore unearths Simulmatics’ story and makes the argument that, amid a broader proliferation of behavioral science research across academia and government in the 1960s, the company paved the way for our 21st-century obsession with data and prediction.
Simulmatics, she argues, is “a missing link in the history of technology,” the antecedent to Facebook, Google and Amazon and to algorithms that attempt to forecast who will commit crimes or get good grades. “It lurks behind the screen of every device,” she writes.
If Then presents Simulmatics as both ahead of its time and, more often than not, overpromising and underdelivering. The company was the brainchild of Ed Greenfield, an advertising executive straight out of Mad Men, who believed computers could help Democrats recapture the White House.
He wanted to create a model of the voting population that could tell you how voters would respond to whatever a candidate did or said. The name Simulmatics was a portmanteau of “simulation” and “automation.” As Greenfield explained it to investors, Lepore writes: “The Company proposes to engage principally in estimating probable human behavior by the use of computer technology.”
The People Machine was originally built to analyze huge amounts of data ahead of the 1960 election in what Lepore describes as, at the time, “the largest political science research project in American history.”
Using surveys, Simulmatics chopped voters into 480 categories, such as “Midwestern, rural, Protestant, lower income, female,” compared these against four years of election returns, and started making predictions. It’s the kind of analysis we take for granted in today’s world of data-driven, microtargeted political campaigns, but at the time it was new and unproven.
The company ended up producing three reports for the Kennedy campaign, although it’s unclear how much impact its work had. Many of its recommendations were “fairly commonplace political wisdom among his close circle of advisers,” Lepore reports, like suggesting Kennedy address anti-Catholic prejudice head-on. “There’s a lot of bluster and nonsense in the archival trail left behind by flimflam men,” she notes.
But that didn’t stop Greenfield and his colleagues from claiming credit for Kennedy’s victory, sparking fears about the computer-powered manipulation of democracy. “A secretly designed robot campaign strategist nicknamed a ‘people-machine’ was said today to have been put to work by President-Elect John F. Kennedy’s top advisers to suggest alternative methods of influencing voters,” one wire service reported. Kennedy’s press secretary flatly denied it, saying, “We did not use the machine. Nor were the machine studies made for us.”
Greenfield, according to Lepore, “didn’t believe in bad publicity.” He took Simulmatics public in 1961 and began pitching the company’s prediction services to media companies, advertising agencies and the government.
Soon, Simulmatics went to Vietnam, thanks to the political connections of another co-founder, Ithiel de Sola Pool, a political scientist at the Massachusetts Institute of Technology whose research included groundbreaking work on social networks. In Saigon, the U.S. Defense Department gave the company a contract to evaluate its counterinsurgency efforts to win the “hearts and minds” of the Vietnamese population. Simulmatics made a lot of money but didn’t appear to produce much of any value, and the Pentagon eventually canceled the contract.
Back in the U.S., Simulmatics tried its hand at predicting race riots in Rochester, N.Y. — with dubious results — and won a contract to contribute to the landmark Kerner Commission investigating the causes of racial unrest (its report used only part of Simulmatics’ work).
Lepore weaves her narrative across continents and through time with engaging, conversational prose. Her characters’ personalities, families, affairs, fights and constant gossiping come alive, thanks to extensive troves of family papers and interviews with those closest to them.
At the same time, she braids in the larger context: the fracturing of the Democratic Party over the civil rights movement, the drama of the Cuban missile crisis, the upheaval of the anti-war movement on college campuses, the shattering impact of the assassinations of John F. Kennedy, Martin Luther King Jr. and Bobby Kennedy, the death of midcentury liberal idealism.
But at the heart of the book is a dissonance that Lepore never really resolves. How much did Simulmatics matter? Was it “effective but sinister,” as portrayed in a bestselling thriller by Eugene Burdick, a political scientist who had worked with Greenfield? Or, as the Kennedy campaign contended, was it “ineffective and duplicitous”?
Much of the evidence points toward the latter. Simulmatics went bankrupt in 1970. “Data was scarce. Models were weak. Computers were slow,” Lepore concludes. “The machine faltered, and the men who built it could not repair it.”
The Crisis in Trusting Science (thanks to all the disinfo paid for with Koch Brothers money and “Stink think tanks”
as a growing number say they do not trust the information they are receiving, including on the ongoing coronavirus as we enter the sixth month of the pandemic.
As The Hill’s Reid Wilson reports, two new surveys show most Americans still trust leading scientists and institutions, such as the CDC, but trust levels in scientific and political institutions are eroding. Nearly eight in ten Americans trust the nation’s leading public health agency, according to a survey conducted by the COVID-19 Consortium for Understanding the Public’s Policy Preferences Across States, a group of researchers at Northeastern University, Harvard, Rutgers and Northwestern University. The figure is down from 87 percent who said they trusted the CDC in April.
A poll conducted by the Kaiser Family Foundation found that 67 percent of Americans have a great deal or a fair amount of trust in the CDC to provide reliable information about the coronavirus. That number has dropped 16 percentage points since April.
“I don’t think anyone is thrilled with the status quo. It’s been a disappointment as a general proposition,” said David Lazer, a political scientist at Northeastern and an author of the study.
Police across Canada are increasingly using controversial algorithms to predict where crimes could occur, who might go missing, and to help them determine where they should patrol, despite fundamental human rights concerns, a new report has found.
To Surveil and Predict: A Human Rights Analysis of Algorithmic Policing in Canada is the result of a joint investigation by the University of Toronto’s International Human Rights Program (IHRP) and Citizen Lab. It details how, in the words of the report’s authors, “law enforcement agencies across Canada have started to use, procure, develop, or test a variety of algorithmic policing methods,” with potentially dire consequences for civil liberties, privacy and other Charter rights, the authors warn.
The report breaks down how police are using or considering the use of algorithms for several purposes including predictive policing, which uses historical police data to predict where crime will occur in the future. Right now in Canada, police are using algorithms to analyze data about individuals to predict who might go missing, with the goal of one day using the technology in other areas of the criminal justice system. Some police services are using algorithms to automate the mass collection and analysis of public data, including social media posts, and to apply facial recognition to existing mugshot databases for investigative purposes.
“Algorithmic policing technologies are present or under consideration throughout Canada in the forms of both predictive policing and algorithmic surveillance tools.” the report reads.
Police in Vancouver, for example, use a machine-learning tool called GeoDASH to predict where break-and-enter crimes might occur. Calgary Police Service (CPS) uses Palantir’s Gotham software to identify and visualize links between people who interact with the police—including victims and witnesses—and places, police reports, and the properties and vehicles they own. (A draft Privacy Impact Assessment (PIA) conducted by CPS in 2014 and mentioned in the report noted that Gotham could “present false associations between innocent individuals and criminal organizations and suspects” and recommended measures to mitigate the risk of this happening, but not all the recommendations have been implemented.)
The Toronto Police Service does not currently use algorithms in policing, but police there have been collaborating with a data analytics firm since 2016 in an effort to “develop algorithmic models that identify high crime areas,” the report notes.
The Saskatchewan Police Predictive Analytics Lab (SPPAL), founded in 2015, is using data provided by the Saskatoon Police Service to develop algorithms to predict which young people might go missing in the province. The SPPAL project is an extension of the “Hub model” of policing, in which social services agencies and police share information about people believed to be “at risk” of criminal behavior or victimization. The SPPAL hopes to use algorithms to address “repeat and violent offenders, domestic violence, the opioid crisis, and individuals with mental illness who have come into conflict with the criminal justice system,” the report reads.
“We’ve learned that people in Canada are now facing surveillance in many aspects of their personal lives, in ways that we never would have associated with traditional policing practices,” said Kate Robertson, a criminal defense lawyer and one of the authors of the report, in a phone call with Motherboard.
“Individuals now face the prospect that when they’re walking or driving down the street, posting to social media, or chatting online, police surveillance in the form of systematic data monitoring and collection may be at work,” Robertson added.
The authors note that “historically disadvantaged communities” are at particular risk of being targeted for surveillance and analysis by the technologydue to systemic bias found in historical police data.