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How we make moral decisions
In some situations, asking "what if everyone did that?" is a common strategy for judging whether an action is right or wrong.
It probably won't have a big impact on the financial well-being of your local transportation system. But now ask yourself, "What if everyone did that?" The outcome is much different — the system would likely go bankrupt and no one would be able to ride the train anymore.
Moral philosophers have long believed this type of reasoning, known as universalization, is the best way to make moral decisions. But do ordinary people spontaneously use this kind of moral judgment in their everyday lives?
In a study of several hundred people, MIT and Harvard University researchers have confirmed that people do use this strategy in particular situations called "threshold problems." These are social dilemmas in which harm can occur if everyone, or a large number of people, performs a certain action. The authors devised a mathematical model that quantitatively predicts the judgments they are likely to make. They also showed, for the first time, that children as young as 4 years old can use this type of reasoning to judge right and wrong.
"This mechanism seems to be a way that we spontaneously can figure out what are the kinds of actions that I can do that are sustainable in my community," says Sydney Levine, a postdoc at MIT and Harvard and the lead author of the study.
Other authors of the study are Max Kleiman-Weiner, a postdoc at MIT and Harvard; Laura Schulz, an MIT professor of cognitive science; Joshua Tenenbaum, a professor of computational cognitive science at MIT and a member of MIT's Center for Brains, Minds, and Machines and Computer Science and Artificial Intelligence Laboratory (CSAIL); and Fiery Cushman, an assistant professor of psychology at Harvard. The paper is appearing this week in the Proceedings of the National Academy of Sciences.
The concept of universalization has been included in philosophical theories since at least the 1700s. Universalization is one of several strategies that philosophers believe people use to make moral judgments, along with outcome-based reasoning and rule-based reasoning. However, there have been few psychological studies of universalization, and many questions remain regarding how often this strategy is used, and under what circumstances.
To explore those questions, the MIT/Harvard team asked participants in their study to evaluate the morality of actions taken in situations where harm could occur if too many people perform the action. In one hypothetical scenario, John, a fisherman, is trying to decide whether to start using a new, more efficient fishing hook that will allow him to catch more fish. However, if every fisherman in his village decided to use the new hook, there would soon be no fish left in the lake.
The researchers found that many subjects did use universalization to evaluate John's actions, and that their judgments depended on a variety of factors, including the number of people who were interested in using the new hook and the number of people using it that would trigger a harmful outcome.
To tease out the impact of those factors, the researchers created several versions of the scenario. In one, no one else in the village was interested in using the new hook, and in that scenario, most participants deemed it acceptable for John to use it. However, if others in the village were interested but chose not to use it, then John's decision to use it was judged to be morally wrong.
The researchers also found that they could use their data to create a mathematical model that explains how people take different factors into account, such as the number of people who want to do the action and the number of people doing it that would cause harm. The model accurately predicts how people's judgments change when these factors change.
In their last set of studies, the researchers created scenarios that they used to test judgments made by children between the ages of 4 and 11. One story featured a child who wanted to take a rock from a path in a park for his rock collection. Children were asked to judge if that was OK, under two different circumstances: In one, only one child wanted a rock, and in the other, many other children also wanted to take rocks for their collections.
The researchers found that most of the children deemed it wrong to take a rock if everyone wanted to, but permissible if there was only one child who wanted to do it. However, the children were not able to specifically explain why they had made those judgments.
"What's interesting about this is we discovered that if you set up this carefully controlled contrast, the kids seem to be using this computation, even though they can't articulate it," Levine says. "They can't introspect on their cognition and know what they're doing and why, but they seem to be deploying the mechanism anyway."
In future studies, the researchers hope to explore how and when the ability to use this type of reasoning develops in children.
In the real world, there are many instances where universalization could be a good strategy for making decisions, but it's not necessary because rules are already in place governing those situations.
"There are a lot of collective action problems in our world that can be solved with universalization, but they're already solved with governmental regulation," Levine says. "We don't rely on people to have to do that kind of reasoning, we just make it illegal to ride the bus without paying."
However, universalization can still be useful in situations that arise suddenly, before any government regulations or guidelines have been put in place. For example, at the beginning of the Covid-19 pandemic, before many local governments began requiring masks in public places, people contemplating wearing masks might have asked themselves what would happen if everyone decided not to wear one.
The researchers now hope to explore the reasons why people sometimes don't seem to use universalization in cases where it could be applicable, such as combating climate change. One possible explanation is that people don't have enough information about the potential harm that can result from certain actions, Levine says.
The research was funded by the John Templeton Foundation, the Templeton World Charity Foundation, and the Center for Brains, Minds, and Machines.
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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|
Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.
- Archaeologists find a cave painting of a wild pig that is at least 45,500 years old.
- The painting is the earliest known work of representational art.
- The discovery was made in a remote valley on the Indonesian island of Sulawesi.
Oldest Cave Art Found in Sulawesi<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="a9734e306f0914bfdcbe79a1e317a7f0"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/b-wAYtBxn7E?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.