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Predicting the president: Two ways election forecasts are misunderstood
Everyone wants to predict who will win the 2020 presidential election. Here are 2 misconceptions to bust so people don't proclaim the death of data like they did in 2016.
- There are two common misconceptions that muddy people's understanding of election forecasting, says Eric Siegel: Blaming the prognosticator and predicting candidates versus predicting voters.
- In 2016, Nate Silver's forecast put about 70% odds on Clinton winning. Despite people's shock at the election results, that forecast was not wrong.
- As predictions for the 2020 presidential election ramp up, it's important to understand what election forecasting means and to bust the misconceptions that warp our expectations.
When it's a presidential election year, speculation is in the cards. It's the national pastime. Everyone wants to predict who'll win.
But, man, did people mismanage their own expectations leading up to the 2016 presidential election, when Donald Trump defeated Hillary Clinton.
This was due in no small part to the misinterpretation of election forecasts. There are two common misconceptions, and correcting them comes down to the fundamental idea of what a probability is.
In 2016, Nate Silver's forecast put about 70% odds on Clinton winning. Who's Nate? There's no more better known person of prediction in this country, no more famous prognostic quant than former New York Times blogger and political poll aggregator Nate Silver, who had gained notoriety for correctly predicting the outcome of the 2012 presidential election for each individual state.
Presently, his up-to-the-minute forecast of the 2020 Democratic Primary is live, and his forecast of the 2020 general election is forthcoming.
By the way, number crunching serves more than just to forecast presidential elections – it also helps win presidential elections. Click here to read all about it.
Misconception #1: Blaming the prognosticator
Nate Silver speaks at a panel in New York City.
Photo: Krista Kennell/Patrick McMullan via Getty Images
When Clinton lost in 2016, everyone was like, "OMG, epic fail!" The reasoning was, well, the 70% forecast that she would win had proven to be wrong, so the problem must have been either bad polling data or something about Silver's model, or both.
But no – the forecast wasn't bad! "70%" does not mean Clinton will clearly win. And a 30% chance of Trump winning isn't a long shot at all. Something that happens 30% of the time is really pretty common and normal. And that's what a probability is. It means that, in a situation just like this, it will happen 30 out of 100 times, that is, 3 out of 10 times. Those aren't long odds.
And Clinton's 70% probability is actually closer to a 50/50 toss-up than it is to a 100% "sure thing." When you see "70%," the take-away isn't that Clinton is pretty much a shoe-in. No, the take-away is, "I dunno." Lot's of uncertainty.
I believe many people saw that "70%," and the thought process was like, "70% is a passing grade, so Clinton will definitely pass, so Clinton will definitely win."
Prediction is hard. To be more specific, there are many situations where the outcome is uncertain and we just can't be confident about what to expect. Nate Silver's model looked at the data and said this one was one of those situations. Now, a confident prediction may feel more satisfying. We all want definitive answers. But it's better for you to shrug your shoulders than to express confidence without a firm basis to do so, and it's better for the math to do the same thing.
Press the press to give it a rest
So, I feel kind of bad for Nate Silver. He totally got a bad rap. Most of the other prominent models at large actually put Clinton's chances much higher – between 92% and 99%. Those models exhibited overconfidence. Silver's model didn't strongly commit. It expressed, first and foremost, uncertainty.
Even the Harvard Gazette, in an article that ultimately defended Silver, put it this way: "Even leading statistical analysis site FiveThirtyEight.com [that's Silver's site] gave Donald Trump a less than 1 in 3 chance of winning. So when he surged to victory... stunned political pundits blamed pollsters and forecasters, proclaiming 'the death of data.'"
It's like the journalist couldn't wrap her head around the fact that "less 1 in 3" – specifically a 30% chance – isn't remote odds. If there were a 30% chance a car would crash, you obviously wouldn't get in the car.
Nate Silver wasn't betting his life on one candidate or the other. His job as a forecaster wasn't to magically predict like a crystal ball. It was to tell you the odds as precisely as possible.
When asked by the same journalist whether he was saying he diverged from the general sentiment that polling had been a "massive failure," Silver said, "Not only am I not on that bandwagon, I think it's pretty irresponsible when people in the mainstream media perpetuate that narrative... We think our general election model was really good. It said there was a pretty good chance of Trump winning... if everyone says 'Trump has no chance' and you use modeling to say 'Hey, look at this more rigorously; he actually has a pretty good chance. Not 50 percent, but 30 percent is pretty good.' To me, that's a highly successful application of modeling."
I even remember hearing him have to talk down his coworkers on his own podcast just before the election, who were talking about Clinton's election as a done deal. It's like nobody understands what "30%" means.
Forecasting isn't futurism
When you're a contestant on the TV quiz show Jeopardy, you only buzz in when you think you know the answer to the question, cause if you get it wrong, you get penalized. So you gauge your own confidence, your own certainty that the answer you have will turn out to be correct. IBM's Watson computer that competed against human champions on that TV show did exactly that. Its predictive model served not only to select the answer to a question, it also provided a gauge of confidence in that answer, which directly informed whether or not the computer buzzed in to answer the question at all.
Here's my big prediction: Futurism will be entirely out of style within 20 years. Ha-ha – get it? My point is, forecasts aren't like futurism. Futurism is the practice of putting your entire reputation down on one confident bet. In contrast, forecasting judiciously allows for uncertainty – it even calls for it, as needed.
Misconception #2: Predicting candidates versus predicting voters
Hillary Clinton and Donald Trump at the first presidential debate of the 2016 presidential election at Hofstra University
Photo: Getty Images
The other common election forecast misconception is that the "70%" estimated how much of the votes Clinton would get. That's very much not the same thing as the chances of winning. Poll aggregators like Silver forecast which candidate will win; any forecast they also make about the percent of voters is secondary and distinct from the main probabilistic forecast.
After all, presidential races are much closer than 70/30. 2016 came out at 46% Trump against 48% Clinton, nationwide.
Now, if the data had us expecting one candidate would actually get 70% of the votes nationwide, then the chances of them winning would indeed be close to a sure thing – and a landslide victory at that. In that case, maybe they would actually end up getting less, like 60% – but that's still a likely electoral college win. And the chances are particularly slim that the outcome would land even further away from the expected 70%, down to below 50%, so a loss of the election would be a long shot, perhaps only a 1% chance. So, if you've forecasted a candidate will get 70% of the votes, that may translate to more like a 99% probability of winning.
Transforming polls to probabilities
Anyway, the 70% wasn't the expected proportion of votes. The expected proportion of votes is the input to Nate Silver's model not the output. To be more precise, the model inputs polls, which estimate how many will vote for each candidate, and outputs a forecast, the probability that a given candidate will win.
An election poll does not constitute magical prognostic technology – it is plainly the act of voters explicitly telling you what they're going to do. It's a mini-election dry run.
But there's a craft to aggregating polls, as Silver has mastered so adeptly. His model cleverly weighs large numbers of poll results, based on how many days or weeks old the poll is, the track record of the pollster, and other factors.
So Silver's model turns poll results into a forecasted probability. It maps from one to the other. That's what a predictive model does in general. It takes the data you have as input, and formulaically transforms it to a probability of the outcome or behavior you're seeking to foresee.
Often, model probabilities come closer to 50% than 100%. They're uncertain, like when your Magic Eight Ball says, "The outlook is hazy." It can be hard to sit with and accept a lack of certainty. When the stakes are high, we'd prefer to feel confident, to know how it's going to turn out. Don't let that impulse draw you to a false narrative. Practice not knowing. Shrug your shoulders more. It's good for you.
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Eric Siegel, Ph.D., founder of the Predictive Analytics World and Deep Learning World conference series and executive editor of The Machine Learning Times, makes the how and why of predictive analytics (aka machine learning) understandable and captivating. He is the author of the award-winning book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, the host of The Dr. Data Show web series, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. Follow him at @predictanalytic.
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How would the ability to genetically customize children change society? Sci-fi author Eugene Clark explores the future on our horizon in Volume I of the "Genetic Pressure" series.
- A new sci-fi book series called "Genetic Pressure" explores the scientific and moral implications of a world with a burgeoning designer baby industry.
- It's currently illegal to implant genetically edited human embryos in most nations, but designer babies may someday become widespread.
- While gene-editing technology could help humans eliminate genetic diseases, some in the scientific community fear it may also usher in a new era of eugenics.
Tribalism and discrimination<p>One question the "Genetic Pressure" series explores: What would tribalism and discrimination look like in a world with designer babies? As designer babies grow up, they could be noticeably different from other people, potentially being smarter, more attractive and healthier. This could breed resentment between the groups—as it does in the series.</p><p>"[Designer babies] slowly find that 'everyone else,' and even their own parents, becomes less and less tolerable," author Eugene Clark told Big Think. "Meanwhile, everyone else slowly feels threatened by the designer babies."</p><p>For example, one character in the series who was born a designer baby faces discrimination and harassment from "normal people"—they call her "soulless" and say she was "made in a factory," a "consumer product." </p><p>Would such divisions emerge in the real world? The answer may depend on who's able to afford designer baby services. If it's only the ultra-wealthy, then it's easy to imagine how being a designer baby could be seen by society as a kind of hyper-privilege, which designer babies would have to reckon with. </p><p>Even if people from all socioeconomic backgrounds can someday afford designer babies, people born designer babies may struggle with tough existential questions: Can they ever take full credit for things they achieve, or were they born with an unfair advantage? To what extent should they spend their lives helping the less fortunate? </p>
Sexuality dilemmas<p>Sexuality presents another set of thorny questions. If a designer baby industry someday allows people to optimize humans for attractiveness, designer babies could grow up to find themselves surrounded by ultra-attractive people. That may not sound like a big problem.</p><p>But consider that, if designer babies someday become the standard way to have children, there'd necessarily be a years-long gap in which only some people are having designer babies. Meanwhile, the rest of society would be having children the old-fashioned way. So, in terms of attractiveness, society could see increasingly apparent disparities in physical appearances between the two groups. "Normal people" could begin to seem increasingly ugly.</p><p>But ultra-attractive people who were born designer babies could face problems, too. One could be the loss of body image. </p><p>When designer babies grow up in the "Genetic Pressure" series, men look like all the other men, and women look like all the other women. This homogeneity of physical appearance occurs because parents of designer babies start following trends, all choosing similar traits for their children: tall, athletic build, olive skin, etc. </p><p>Sure, facial traits remain relatively unique, but everyone's more or less equally attractive. And this causes strange changes to sexual preferences.</p><p>"In a society of sexual equals, they start looking for other differentiators," he said, noting that violet-colored eyes become a rare trait that genetically engineered humans find especially attractive in the series.</p><p>But what about sexual relationships between genetically engineered humans and "normal" people? In the "Genetic Pressure" series, many "normal" people want to have kids with (or at least have sex with) genetically engineered humans. But a minority of engineered humans oppose breeding with "normal" people, and this leads to an ideology that considers engineered humans to be racially supreme. </p>
Regulating designer babies<p>On a policy level, there are many open questions about how governments might legislate a world with designer babies. But it's not totally new territory, considering the West's dark history of eugenics experiments.</p><p>In the 20th century, the U.S. conducted multiple eugenics programs, including immigration restrictions based on genetic inferiority and forced sterilizations. In 1927, for example, the Supreme Court ruled that forcibly sterilizing the mentally handicapped didn't violate the Constitution. Supreme Court Justice Oliver Wendall Holmes wrote, "… three generations of imbeciles are enough." </p><p>After the Holocaust, eugenics programs became increasingly taboo and regulated in the U.S. (though some states continued forced sterilizations <a href="https://www.uvm.edu/~lkaelber/eugenics/" target="_blank">into the 1970s</a>). In recent years, some policymakers and scientists have expressed concerns about how gene-editing technologies could reanimate the eugenics nightmares of the 20th century. </p><p>Currently, the U.S. doesn't explicitly ban human germline genetic editing on the federal level, but a combination of laws effectively render it <a href="https://academic.oup.com/jlb/advance-article/doi/10.1093/jlb/lsaa006/5841599#204481018" target="_blank" rel="noopener noreferrer">illegal to implant a genetically modified embryo</a>. Part of the reason is that scientists still aren't sure of the unintended consequences of new gene-editing technologies. </p><p>But there are also concerns that these technologies could usher in a new era of eugenics. After all, the function of a designer baby industry, like the one in the "Genetic Pressure" series, wouldn't necessarily be limited to eliminating genetic diseases; it could also work to increase the occurrence of "desirable" traits. </p><p>If the industry did that, it'd effectively signal that the <em>opposites of those traits are undesirable. </em>As the International Bioethics Committee <a href="https://academic.oup.com/jlb/advance-article/doi/10.1093/jlb/lsaa006/5841599#204481018" target="_blank" rel="noopener noreferrer">wrote</a>, this would "jeopardize the inherent and therefore equal dignity of all human beings and renew eugenics, disguised as the fulfillment of the wish for a better, improved life."</p><p><em>"Genetic Pressure Volume I: Baby Steps"</em><em> by Eugene Clark is <a href="http://bigth.ink/38VhJn3" target="_blank">available now.</a></em></p>
It's hard to stop looking back and forth between these faces and the busts they came from.
- A quarantine project gone wild produces the possibly realistic faces of ancient Roman rulers.
- A designer worked with a machine learning app to produce the images.
- It's impossible to know if they're accurate, but they sure look plausible.
How the Roman emperors got faced<a href="https://payload.cargocollective.com/1/6/201108/14127595/2K-ENGLISH-24x36-Educational_v8_WATERMARKED_2000.jpg" ><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ2NDk2MS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyOTUzMzIxMX0.OwHMrgKu4pzu0eCsmOUjybdkTcSlJpL_uWDCF2djRfc/img.jpg?width=980" id="775ca" class="rm-shortcode" data-rm-shortcode-id="436000b6976931b8320313478c624c82" data-rm-shortcode-name="rebelmouse-image" alt="lineup of emperor faces" data-width="1440" data-height="963" /></a>
Credit: Daniel Voshart<p>Voshart's imaginings began with an AI/neural-net program called <a href="https://www.artbreeder.com" target="_blank">Artbreeder</a>. The freemium online app intelligently generates new images from existing ones and can combine multiple images into…well, who knows. It's addictive — people have so far used it to generate nearly 72.7 million images, says the site — and it's easy to see how Voshart fell down the rabbit hole.</p><p>The Roman emperor project began with Voshart feeding Artbreeder images of 800 busts. Obviously, not all busts have weathered the centuries equally. Voshart told <a href="https://www.livescience.com/ai-roman-emperor-portraits.html" target="_blank" rel="noopener noreferrer">Live Science</a>, "There is a rule of thumb in computer programming called 'garbage in garbage out,' and it applies to Artbreeder. A well-lit, well-sculpted bust with little damage and standard face features is going to be quite easy to get a result." Fortunately, there were multiple busts for some of the emperors, and different angles of busts captured in different photographs.</p><p>For the renderings Artbreeder produced, each face required some 15-16 hours of additional input from Voshart, who was left to deduce/guess such details as hair and skin coloring, though in many cases, an individual's features suggested likely pigmentations. Voshart was also aided by written descriptions of some of the rulers.</p><p>There's no way to know for sure how frequently Voshart's guesses hit their marks. It is obviously the case, though, that his interpretations look incredibly plausible when you compare one of his emperors to the sculpture(s) from which it was derived.</p><p>For an in-depth description of Voshart's process, check out his posts on <a href="https://medium.com/@voshart/photoreal-roman-emperor-project-236be7f06c8f" target="_blank">Medium</a> or on his <a href="https://voshart.com/ROMAN-EMPEROR-PROJECT" target="_blank" rel="noopener noreferrer">website</a>.</p><p>It's fascinating to feel like you're face-to-face with these ancient and sometimes notorious figures. Here are two examples, along with some of what we think we know about the men behind the faces.</p>
Caligula<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ2NDk4Mi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY3MzQ1NTE5NX0.LiTmhPQlygl9Fa9lxay8PFPCSqShv4ELxbBRFkOW_qM/img.jpg?width=980" id="7bae0" class="rm-shortcode" data-rm-shortcode-id="ce795c554490fe0a36a8714b86f55b16" data-rm-shortcode-name="rebelmouse-image" data-width="992" data-height="558" />
One of numerous sculptures of Caligula, left
Nero<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ2NTAwMC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY1NTQ2ODU0NX0.AgYuQZzRQCanqehSI5UeakpxU8fwLagMc_POH7xB3-M/img.jpg?width=980" id="a8825" class="rm-shortcode" data-rm-shortcode-id="9e0593d79c591c97af4bd70f3423885e" data-rm-shortcode-name="rebelmouse-image" data-width="992" data-height="558" />
One of numerous sculptures of Nero, left
Scientists use new methods to discover what's inside drug containers used by ancient Mayan people.
- Archaeologists used new methods to identify contents of Mayan drug containers.
- They were able to discover a non-tobacco plant that was mixed in by the smoking Mayans.
- The approach promises to open up new frontiers in the knowledge of substances ancient people consumed.
PARME staff archaeologists excavating a burial site at the Tamanache site, Mérida, Yucatan.
Dr. Eric Lander is a pioneer in genomics. What role will he play in the new administration?
- Dr. Lander is a mathematician and geneticist who's best known for his leading role in the Human Genome Project.
- Biden nominated Dr. Lander to head the Office of Science and Technology Policy and also serve as a cabinet-level science adviser, marking the first time the position has been part of the presidential cabinet.
- In an open letter, Biden said it's essential for the U.S. to "refresh and reinvigorate our national science and technology strategy to set us on a strong course for the next 75 years."
Who is Dr. Eric Lander?<p>Born in Brooklyn, New York, Dr. Lander started his academic career as a mathematician, often arriving at high school an hour early to do math. He won multiple awards in mathematics in his teens, including the Mathematical Olympiad in 1974.<br></p><p>Finding mathematics "too monastic" to pursue as a career, he began teaching managerial economics at Harvard Business School. Then, at the <a href="https://www.worldsciencefestival.com/videos/eric-lander-the-genesis-of-genius/" target="_blank">encouragement of his brother</a>, a neurobiologist, Dr. Lander became interested in studying neurobiology and microbiology. This pushed him to his main lifelong pursuit: unraveling the mysteries of the human genome.</p><p>Dr. Lander spent more than a decade as a leader within the Human Genome Project, which provided the world a complete map of all human genes in 2003. In 2004, he founded the Broad Institute, a biomedical and genomic nonprofit research center that partners with M.I.T. and Harvard University.</p>
Credit: Pixabay<p>Broad's <a href="https://www.broadinstitute.org/news-multimedia/basic-q-about-broad-institute" target="_blank">mission</a> is to "fulfill the promise of genomics by creating comprehensive tools for biology and medicine, making them broadly available to the world and applying them to the understanding of human biology and the diagnosis, treatment, and cure of human diseases." The institute aims to diminish diseases by better understanding cellular mechanisms, rather than simply treating symptoms.</p><p>Despite some <a href="https://www.statnews.com/2016/01/25/why-eric-lander-morphed/" target="_blank">minor controversies and patent disputes</a>, Dr. Lander remains a monumental figure in American science, and also previously served as co-chairman of former President Barack Obama's science advisory council.</p>
What will Dr. Lander do in the Biden administration?<p>If confirmed by the Senate, it's not exactly clear what Dr. Lander will do in his role as cabinet science adviser and head of the OSTP. But his primary focus likely won't be COVID-19, considering Biden has already established a task force dedicated to shaping policy and recommendations related to the pandemic.<br></p><p>But Biden revealed some of his expectation in an <a href="https://buildbackbetter.gov/wp-content/uploads/2021/01/OSTP-Appointment.pdf" target="_blank">open letter</a> that posed five questions for the Office of Science and Technology Policy to explore:</p><ol><li>What can we learn from the pandemic about what is possible—or what ought to be possible— to address the widest range of needs related to our public health?</li><li>How can breakthroughs in science and technology create powerful new solutions to address climate change—propelling market-driven change, jump-starting economic growth, improving health, and growing jobs, especially in communities that have been left behind?</li><li>How can the United States ensure that it is the world leader in the technologies and industries of the future that will be critical to our economic prosperity and national security, especially in competition with China?</li><li>How can we guarantee that the fruits of science and technology are fully shared across America and among all Americans?</li><li>How can we ensure the long-term health of science and technology in our nation?</li></ol><p>The president-elect wrote that it's essential to "refresh and reinvigorate our national science and technology strategy to set us on a strong course for the next 75 years," concluding:</p><p style="margin-left: 20px;">"I believe that the answers to these questions will be instrumental in helping our nation embark on a new path in the years ahead—a path of dignity and respect, of prosperity and security, of progress and common purpose. They are big questions, to be sure, but not as big as America's capacity to address them. I look forward to receiving your recommendations—and to working with you, your team, and the broader scientific community to turn them into solutions that ease everyday burdens for the American people, spark new jobs and opportunities, and restore American leadership on the world stage."</p>
To understand ourselves and our place in the universe, "we should have humility but also self-respect," Frank Wilczek writes in a new book.