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Why non-conformists always end up looking alike
Go ahead, try and be different.
- Anti-conformists have an odd way of ending up looking like each other.
- A Brandeis mathematician looks at how this synchronicity occurs.
- Understanding the mechanism behind non-conformist conformity has applications in other areas, like the stock market.
We're here for such a short time, and we'd like to think we matter. "I'm not just one more person — I'm different." That's true, and also… not. We're very much like one another, though the particular details of our lives are, of course, pretty unique. Still, particularly in the Western world, we like to be seen as separate from — and better than? — the herd. Many of us go out of our way to look different than "them," too, declaring our uniqueness in our appearance.
It's not just an issue of visual fashion, by the way. As Touboul tells Technology Review, "Beyond the choice of the best suit to wear this winter, this study may have important implications in understanding synchronization of nerve cells, investment strategies in finance, or emergent dynamics in social science."
The purpose of the study
Image source: LDWYTN / Shutterstock
While anti-conformists may, at first, succeed in devising their own personal brand of sartorial rebelliousness, it's followed by an inevitable, if unintentional, synchronization around a single appearance. Touboul's study looks at how such people seem to inevitably become synchronized. He suspects that a major influence on the way it happens may be the speed of propagation of styles through a culture.
Not everyone learns about or adopts new styles in the same way. Some follow fashion closely, some go by word of mouth, and some emulate the appearance of well-known individuals they admire. In the latter case, the tipping-point may occur after a mutually-revered celebrity adopts a new fashion.
In Touboul's simple model, one is either a member of the mainstream or a hipster (no snarky connotation intended), and he explores different hipster-to-mainstream ratios. He also factors in different amounts of time it might take a hipster to detect a new style and respond to it.
Simple as his model is, Touboul has found that experimenting with those two factors produces a surprisingly complex set of behaviors, though synchronization always occurs. Even when he revises his model to allow for more than two types of people, synchronization still occurs.
Often, of course, roles may swap when there are so many hipsters that they become the mainstream. "For example," says Touboul, "if a majority of individuals shave their beard, then most hipsters will want to grow a beard, and if this trend propagates to a majority of the population, it will lead to new, synchronized, switch to shaving." An odd sudden swap occurs when the number of mainstreamers and hipsters is roughly equal — everybody winds up switching together between different trends.
Answering why this last phenomenon occurs is an area cited by Touboul for further study.
It also bears saying that Touboul's modeling isn't really just about hipsters — it's about the manner in which any group of people suddenly decide to act in lockstep contrary to the mainstream. One example he mentions would be the stock market, in which a number of investors may suddenly conclude there's money to be made in acting contrary to the majority attitude.
A deeper understanding of the dynamics underlying such events, which new research hopefully will provide, would obviously be helpful.
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
"You dream about these kinds of moments when you're a kid," said lead paleontologist David Schmidt.
- The triceratops skull was first discovered in 2019, but was excavated over the summer of 2020.
- It was discovered in the South Dakota Badlands, an area where the Triceratops roamed some 66 million years ago.
- Studying dinosaurs helps scientists better understand the evolution of all life on Earth.
Credit: David Schmidt / Westminster College<p style="margin-left: 20px;">"We had to be really careful," Schmidt told St. Louis Public Radio. "We couldn't disturb anything at all, because at that point, it was under law enforcement investigation. They were telling us, 'Don't even make footprints,' and I was thinking, 'How are we supposed to do that?'"</p><p>Another difficulty was the mammoth size of the skull: about 7 feet long and more than 3,000 pounds. (For context, the largest triceratops skull ever unearthed was about <a href="https://www.tandfonline.com/doi/abs/10.1080/02724634.2010.483632" target="_blank">8.2 feet long</a>.) The skull of Schmidt's dinosaur was likely a <em>Triceratops prorsus, </em>one of two species of triceratops that roamed what's now North America about 66 million years ago.</p>
Credit: David Schmidt / Westminster College<p>The triceratops was an herbivore, but it was also a favorite meal of the T<em>yrannosaurus rex</em>. That probably explains why the Dakotas contain many scattered triceratops bone fragments, and, less commonly, complete bones and skulls. In summer 2019, for example, a separate team on a dig in North Dakota made <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">headlines</a> after unearthing a complete triceratops skull that measured five feet in length.</p><p>Michael Kjelland, a biology professor who participated in that excavation, said digging up the dinosaur was like completing a "multi-piece, 3-D jigsaw puzzle" that required "engineering that rivaled SpaceX," he jokingly told the <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">New York Times</a>.</p>
Morrison Formation in Colorado
James St. John via Flickr
|Credit: Nobu Tamura/Wikimedia Commons|
The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.
Can computers do calculations in multiple universes? Scientists are working on it. Step into the world of quantum computing.
- While today's computers—referred to as classical computers—continue to become more and more powerful, there is a ceiling to their advancement due to the physical limits of the materials used to make them. Quantum computing allows physicists and researchers to exponentially increase computation power, harnessing potential parallel realities to do so.
- Quantum computer chips are astoundingly small, about the size of a fingernail. Scientists have to not only build the computer itself but also the ultra-protected environment in which they operate. Total isolation is required to eliminate vibrations and other external influences on synchronized atoms; if the atoms become 'decoherent' the quantum computer cannot function.
- "You need to create a very quiet, clean, cold environment for these chips to work in," says quantum computing expert Vern Brownell. The coldest temperature possible in physics is -273.15 degrees C. The rooms required for quantum computing are -273.14 degrees C, which is 150 times colder than outer space. It is complex and mind-boggling work, but the potential for computation that harnesses the power of parallel universes is worth the chase.